Internal Branding of an AECL Department: Exploring Student Insights Regarding the Undergraduate Majors of Their Department

Katherine Bezner, Oklahoma State University,

Audrey E. H. King, Oklahoma State University,

Lauren Lewis Cline, Oklahoma State University,

Bree Elliott, Oklahoma State University,

Kane Kinion, The Ohio State University,

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First-year college students are entering higher education with less agricultural knowledge overall, leading to misperceptions about potential career paths associated with their chosen degree programs. Researchers in Oklahoma State University’s Agricultural Education, Communications and Leadership (AECL) Department’s pilot undergraduate research course desired to explore students’ perceptions of the department’s majors and internal brand. A quantitative, exploratory survey was completed by 207 students. Students felt most knowledgeable about their chosen major, were satisfied with their major choice, and anticipated obtaining a job upon graduation based on their major. When social pressures and career readiness perceptions of the majors were analyzed according to major program, most students showed a preference for their own major among the indicators. However, results varied on the students’ perceptions of other majors within the department. Agricultural communications was perceived the most positively by all AECL majors. Agricultural leadership was considered to be the most inclusive. Agricultural education was perceived to be the most important major program to the agricultural industry. It is recommended the AECL Department implement a multidisciplinary freshman orientation program, conduct a communications and branding assessment for recruitment and retention, and explore qualitatively the meaning of student perceptions of majors.


Changing demographics among colleges of agriculture nationwide call for diverse degree options available to students (Foreman et al., 2018). First-year college students are also entering higher education with less agricultural knowledge overall (Colbath & Morrish, 2010), leading to misperceptions about potential career paths associated with their chosen degree programs. The attributes of academic programs are communicated by faculty and staff (Erdoğmuş & Ergun, 2016). Studies show factors such as potential financial earnings and social standing influence students’ perceptions of majors rather than the content and focus of the discipline itself (Fosnacht & Calderone, 2017). Simultaneously, the average job tenure across the U.S. is less than five years (Bureau of Labor Statistics, 2020). Perhaps the misconceptions regarding agriculture and its related majors, combined with the influential factors on major choice, have created a disconnect between major choice satisfaction and job tenure lengths for college of agriculture graduates (Scofield, 1994).

Agricultural education departments serve an essential role in preparing a skilled and proficient workforce (McKim et al., 2017). Moreover, it is important agricultural education departments, or programs encompass a variety of agricultural social sciences (Barrick, 1989). To achieve these goals, the Agricultural Education, Communications and Leadership (AECL) Department at Oklahoma State University (OSU) must understand the perceptions of its undergraduate students toward the departmental majors. This understanding could offer insights about the internal brand and culture of the department as a whole.

Researchers in the AECL Department’s pilot undergraduate research course desired to explore students’ perceptions of the department’s majors and internal brand. These students felt a disconnect between the three programs. The interdisciplinary research team developed this study to examine organizational silos within the department. The internal brand of the AECL Department was explored by surveying other undergraduate students.

Literature Review

As the interest in student retention has increased, research regarding student major satisfaction and undergraduate students’ perceptions has also increased. Student major satisfaction, which is influenced by internal branding, is a crucial component to a student’s academic success (Milsom & Coughlin, 2017). Moreover, major satisfaction is one of the largest factors impacting undergraduate student retention (Graunke &Woosley, 2005). Numerous studies have found students are influenced to choose majors with high job availability and high financial return (often referred to as career readiness indicators) after graduation (Del Rossi & Hersch, 2008; Baker et al., 2013). Moreover, recruitment materials for academic majors that demonstrate high job stability are most appealing to prospective students (Baker et al., 2011).

Other fields have studied the challenges that face departments when several majors are housed in one department. For example, bioinformatics majors face considerable challenges when integrating their major program into life sciences departments. Institutional support issues have been reported as a plausible cause for this department divide (Bianchi et al., 2019).

Institutional support issues are described as a lack of internal faculty support for a given major program (Bianchi et al., 2019). If a disconnect arises within a group, an organizational silo can form, impairing a department’s overall functionality (Evans, 2012). Evans (2012) described the reality of organizational silos by stating:

Silos segregate one type of grain from another and the segregated parts within an organization. In a business suffering from silo syndrome, each department or function interacts primarily within that silo rather than with other groups across the organization. (p. 176)

External branding efforts can bring awareness to available major programs; however, a student’s autonomy and connection to an academic department can be more meaningful (Joseph et al., 2012). Research indicates a student’s major choice can be impacted by the academic environment’s friendliness and atmosphere (Stair et al., 2016). Additionally, faculty can either perpetuate or address misconceptions related to college majors and can even influence a student’s major choice (Alam et al., 2019; Hertel & Dings, 2014). Research concerning student perceptions is commonly reported; however, there is a lack of research focused on undergraduate students’ perceptions of other major programs within an academic department. Staff and faculty opinions may contribute to students’ perceptions of not only their major but other programs as well (Hertel & Dings, 2014). Within academic departments, these interpersonal relationships between students and faculty can be challenging to cultivate. This can be influenced by departmental siloing, or the act of not crossing disciplines when conducting research, classwork, or other forms of learning (Guth, 2017). Therefore, by exploring an academic department’s brand value, one could better identify student perceptions and instill a proactive approach to prevent organizational siloing and promote learning (Friedman & Kass-Shraibman, 2017).

Theoretical Framework

Branding is not only a theory, but a practice that attempts to distinguish a product, corporation, or organization from others (Franzen & Moriarty, 2009). A brand is not a logo or tagline. A brand is created through a system of exchanges between brand managers and consumers, known as the receiver of branding messages. In this study, the brand manager is operationalized as the department as a whole, and students would be considered to be the brand consumers. It is impossible to understand a brand as independent from the environment in which it exists (Franzen & Moriarty, 2009). Moreover, a brand exists for an organization regardless of the intentional creation of the organization. Though abstract, a strong brand is invaluable in today’s marketplace (Swaminathan et al., 2020).

A critical aspect of branding efforts is creating a strong internal brand (Punjaisri & Wilson, 2011; Sartain & Schumann, 2006). An internal brand is displayed through the way the internal stakeholders display the brand to external audiences. Internal brands are particularly important for service-based organizations that depend on interactions between people to thrive (Schmidt & Baugmgarth, 2018). Internal branding can be promoted through relationships and peer-to-peer interactions. Studies have shown that teaching staff and attitude towards the university are major factors in student success and satisfaction within their major (Erdoğmuş & Ergun, 2016).

The framework for this study integrated internal branding and student perceptions. It was adapted from Punjaisri and Wilson’s 2011 work that described internal branding communication, including brand identification, brand commitment, and brand loyalty. The level at which these three components operate is known as an organization’s brand performance (Punjaisri & Wilson, 2011). When consumers and stakeholders identify with the brand, they consider themselves part of the brand itself (Punjaisri & Wilson, 2011). Brand commitment is the psychological connection between a brand or service and the stakeholder (Punjaisri & Wilson, 2011). Brand loyalty is the continued service and investment in a certain product or organization (Punjaisri & Wilson, 2011).

Recently, higher education branding has become a more researched phenomena (Chapleo, 2011; Dholakia, 2017). Universities, like corporate entities, work to distinguish themselves from the competition. However, universities are more complex than corporations, and therefore, so is their Branding (Mazzarol & Soutar, 2002). A strong university brand helps students navigate the decision of picking the university that is right for them by displaying the differences between schools and displaying the unique attributes of a university (Chen & Chen, 2014). Researchers agree that students’ educational experience is of the utmost importance in an overall university brand (Ng & Forbes, 2009; Pinar et al., 2014). Many factors, including departmental structure, faculty, staff, and peers, can be affected by a student’s educational experience and university brand.

The general atmosphere of the university also affects brand loyalty (Erdoğmuş & Ergun, 2016). A study has shown students value a sense of community not only within their department but also in their university as a whole (Erdoğmuş & Ergun, 2016). The same study also showed that fellow students’ opinions did not impact their educational choices. However, it is important for current and incoming students to have a positive relationship with program alumni, as their opinions and experiences are influential when students make educational decisions (Erdoğmuş & Ergun, 2016).

When studying students’ perceptions of brand equity (i.e., the value of a brand), researchers found the most important factors are the perceived quality of faculty, university reputation, brand loyalty, academic offerings, prestige, career readiness, and emotional environment. These factors are intertwined and ultimately build university brand awareness (Alam et al., 2019; Pinar et al., 2014). Similar to the emotional environment finding of Pinar et al. (2014), Eldegwy et al. (2018) found students who were satisfied with the social aspects of the university were more likely to recognize, recommend, and pay for the university brand.

Perceived quality of education, the institution’s social image, and job market success were important factors for university selection among students in the U.S. (Mourad et al., 2020). Studies have shown that faculty bring their brand to their classrooms. However, internal branding is related to the orientation of faculty behavior, which ultimately results in the student’s experience (Sujchaphon et al., 2015). Thus, for universities to deliver on their brand promise, it is important for faculty to properly communicate that brand (Sujchaphon et al., 2015).

University branding is a priority for undergraduate recruitment. The end goal for university first-year student success should be a long-term student retention rate (Cox & Naylor, 2018). A student’s feeling of self-efficacy influences retention. A sense of belonging can be heightened with involvement in an orientation program (Huddleston, 2000). Orientations are commonly used to communicate the culture and brand of an organization. Therefore, multidisciplinary classrooms with small and large group activities can serve as an innovative model to encourage peer-to-peer discussions and diversity of education (Stebleton et al., 2010). A collaborative, multidisciplinary approach to recruitment can serve as a vehicle for brand identification and brand loyalty.

Departmental Background

The OSU agricultural education program (EDUC) was established in the early 1920s (Oklahoma State University, personal communication, 2014). The department expanded to add an agricultural communications program (COMM) was added shortly after. As interest in the department continued to grow, the agricultural leadership program (LEAD) was approved in 2005 (Oklahoma State University, personal communication, 2005).

OSU has an undergraduate student retention rate of 83.2% (Oklahoma State University, 2014). The department has incorporated multiple strategies to retain students: one-on-one academic advising with faculty, developing major career paths, and academic support. These types of strategies increase the strength of the department’s internal brand. A strong internal brand can help to retain and attract students (Devasagayam et al. 2010). The strength of an internal brand is demonstrated through student or employee behaviors and their relation to organizational values (Simi & Sudhahar, 2019). A strong internal brand can result in increased student involvement in department clubs, professional organizations, and extracurricular activities. Organizations that are collaborative and demonstrate a unified internal brand are more appealing (Alshathry et al., 2017).

For the purpose of this study, the university brand is described as how external stakeholders view the organization, which includes students, parents, and mentors. Internal branding consists of the internal stakeholders, faculty, and staff, who have an inside viewpoint of the brand. We are going to frame this study to explore internal departmental branding, which we have operationalized to mean undergraduate students in the AECL Department at OSU and their perceptions of the department brand.

Research Purpose and Objectives

The purpose of this study was to describe the undergraduate AECL Department students’ perceptions of the AECL Department undergraduate majors at OSU. Three research questions guided this study:

  1. What perceptions do AECL Department students have about their majors?
  2. What perceptions do AECL Department students have about other majors in the department?


This study was conducted as a quantitative, exploratory survey using a 10-item researcher-developed questionnaire. The study was conducted at OSU among a convenience sample of undergraduate students in the AECL Department. Of the 389 enrolled undergraduate students in the department at the time, 208 completed the questionnaire, but one incomplete questionnaire was removed from the study. The final response rate was 53.2% (N = 207). AECL undergraduate students identified their major as agricultural education (EDUC; n = 66, 31.9%), agricultural communications (COMM; n = 98, 47.4%) and agricultural leadership (LEAD; n = 35, 16.9%). Eight students identified as one of several double-major options in the department; it is noted that these students were included in analysis of demographic data to describe participants to determine a representative sample, but not included in further analysis based on the research questions and low response rates per double-major option.

Demographic data were collected from the study’s participants (N = 207). Participants were classified academically as freshmen (n = 7, 3.4%), sophomores (n = 43, 20.8%), juniors (n = 74, 35.7%), and seniors (n = 82, 39.6%). Participants included 161 (77.8%) self-identified females and 46 (22.2%) self-identified males. Of this population, about 62% (n = 129) entered OSU as first-semester freshman, about 37% (n= 77) were transfer students, and less than one percent (n = 1) entered as an exchange student. More than three-quarters (n = 163, 78.7%) reported their hometown as a rural, agriculturally based community. Thirty-eight participants (18.4%) reported their hometown as urban/suburban. The distribution of participants across the undergraduate majors in the AECL Department was deemed to be representative of the overall population.

The questionnaire consisted of 10 items based on the literature review related to student major choice, career readiness indicators, and internal branding. The first item analyzed for this study explored the influence of social pressures (e.g., other people’s opinions, family pressure, prestige, and career readiness indicators) to AECL Department students’ choice of major. The remaining nine questions gathered demographic data.

To establish reliability, the questionnaire was piloted among a 15-person AECL Department graduate research methods course. The instrument demonstrated acceptable test- retest reliability with Phi correlation coefficients ranging between .51 and .90. Minor edits were made to items with poor reliability and an advisory group of three AECL Department faculty representing the three undergraduate majors to ensure the face and content validity of the final questionnaire. The finalized questionnaire was distributed during a one-week period of the fall semester among 20 departmental courses. Data were analyzed using SPSS© Version 23.

Descriptive statistics, including frequencies, percentages, means, and standard deviations were calculated to answer the research questions.


Research Question 1: What perceptions do AECL Department students have about their majors?

Analysis of the items related to the perceptions undergraduate AECL Department students had about their major program demonstrated students were most knowledgeable about their chosen major. As shown in Table 1, most EDUC and COMM students believed they understood their major, while fewer LEAD students felt the same. More than 70% of students in all three majors appeared to be satisfied with their major.

Table 1

Research Question 2: What perceptions do AECL Department students have about other majors in the department?

To address the second research question, students were given a set of career readiness indicators and asked to choose the major they believed provided the best opportunity for each indicator. The six indicators and student responses aggregated by major and total responses are provided in Figure 1.

Figure 1

When looking at the perceptions of EDUC students (n = 66) across the six indicators of career readiness, EDUC students preferred their major among three out of six indicators. EDUC students considered the EDUC major to be the most inclusive (n = 31, 47%) and provided the most job opportunities (n = 42, 63.6%). Of the remaining three indicators, EDUC students believed the COMM major had more high-quality careers (n = 35, 53%), the highest potential income earning opportunity (n = 38, 57.6%), and was the most progressive and forward thinking (n = 23, 34.8%).

COMM students (n = 98) preferred their major among five of the six indicators. COMM students believed the COMM major to be the most inclusive (n = 56, 57.1%), provided more job opportunities (n = 66, 67.3%;), a higher-quality career (n = 87, 88.8%;), and had the highest potential income-earning opportunity (n = 70, 71.4%). COMM students believed the EDUC major was the most important to the agricultural industry (n = 65, 66.3%).

LEAD students (n = 35) preferred their major among three of the six indicators. LEAD students believed the LEAD major provided more job opportunities (n = 18, 51.4%), was the most inclusive (n = 28, 80%), and was the most progressive and forward-thinking (n= 24, 68.6%). Of the remaining three indicators, LEAD students perceived the COMM major as providing more high-quality careers (n =18, 51.4%) and the major with the highest potential income-earning opportunity (n = 15, 42.9%). LEAD students believed the EDUC major was the most important to the agricultural industry (n = 23, 65.7%).

When looking at the overall data, a consensus was shown by students from the three majors among three of the career readiness indicators. When data were aggregated, students believed the EDUC major was the most important to the agricultural industry (n = 147, 73.8%). The LEAD major was believed by more than one-third of the students to be the most inclusive program (n = 72, 36.2%). The COMM major was believed to provide more job opportunities (n = 98, 49.2%), more high-quality careers (n = 140, 70.4%), the highest potential income earning opportunity (n = 123, 61.8%), and to be the most progressive and forward thinking (n = 94, 47.2%).

Conclusions and Implications

Participants in the study were more likely to report their background as rural and agriculturally based than urban, which is representative of college of agriculture demographics at other institutions (Foreman, et al., 2018). More than three-quarters of students’ hometowns were a rural, ag-based community. Therefore, it could be concluded the prevalence of rural students might impact the internal branding of the AECL Department, as there may be a significant difference in major choice and perceptions between students with rural/ag-based backgrounds and urban/suburban students. Participants in this study were also primarily female, similar to the student population’s overall demographics within the College of Agriculture and Life Sciences at Iowa State University (Foreman, et al., 2018). The ratio of enrollment for male and female participants in our study was also consistent with the changing demographics of other colleges of agriculture in the United States (Foreman, et al., 2018). Perhaps the department’s recruitment efforts are over-investing in rural, ag based communities. The AECL Department’s internal brand influences undergraduate students and their perceptions of majors within the department. To increase collaboration, a multidisciplinary orientation course can enhance peer-to-peer discussions and diversity of education (Stebleton et al., 2010).

Research Question 1: What perceptions do AECL Department students have about their majors?

Findings indicated most AECL Department students felt they were knowledgeable, satisfied, and able to obtain a job after graduation with their major program. These positive preferences correlate to strong major satisfaction and internal branding buy-in. It seems natural for students to be most knowledgeable about their own majors. Strong major satisfaction is related to higher GPA levels, and it should be noted that students interested in multiple major programs do not necessarily improve their academic standing (Milsom & Coughlin, 2017). A student’s sense of belonging can be most influenced by interaction with faculty and staff members (Alam et al., 2019). Similarly, faculty members can contribute to student perceptions of other majors in an academic department (Hertel & Dings, 2014). Our college requires faculty academic advising, and most departmental faculty have a majority teaching appointment.

Participants in our study have the most exposure to their major program’s peers and faculty, which may have also impacted their perceptions of the department’s internal brand.

Research Question 2: What perceptions do AECL Department students have about other majors in the department?

Overall, COMM was perceived the most positively by all AECL Department majors. COMM was consistently identified as a major with career opportunities and benefits for graduates. EDUC was portrayed as the most important major to the agricultural industry. LEAD was considered to be the most inclusive. It is worth noting the COMM major was the most represented in our sample for this study. When perceptions of the majors were analyzed by their major program, most students showed a preference for their own major among the indicators. However, results varied on the students’ perceptions of other majors within the department. As a result, COMM student responses may have skewed the distribution of aggregated data for most items. This insight draws attention to the fact that LEAD was considered to be the most inclusive major despite students in each major perceiving their own major to be the most inclusive. Perhaps there were more defectors, or less consensus, within the majors for that particular item. Our findings could translate to a possible organizational silo within the AECL Department based on students’ tendency to prefer their own major among the items. Students enrolled in majors without multidisciplinary crossover may be in a less functional learning environment operating as an organizational silo (Friedman & Kass-Shraibman, 2017).


The findings demonstrate theoretical implications for student major selection. Based on the congruency between the findings in this study and the literature review, these are the following recommendations for the AECL Department: (a) implementation of a multidisciplinary freshman orientation program; (b) a recruitment assessment for retention; and (c) a qualitative study examining the meaning of student perceptions of majors.

A multidisciplinary freshman orientation course should be implemented to increase collaboration within the AECL Department and reduce a perceived organizational silo. This course would be a collaboration between faculty and graduate students in the department. At OSU, other programs have implemented orientation courses including animal science, agricultural economics, and a basic orientation course for all freshmen enrolled in the college of agriculture. Other disciplines have successfully implemented multidisciplinary courses, which increased the confidence level of major choice among their undergraduate students (Copp et al., 2012).

A multifaceted communications approach focused on the department’s internal brand is needed for the success of both student recruitment and student retention rates. The internal branding of a department is most effective when all programs work cohesively (Alshathry, 2017). The buy-in for internal branding among potential and current students positively correlates to personal meetings (Devasagayam, 2010). High-achieving students are not influenced by campus visits alone; therefore, we suggest that faculty continue to hold personal meetings face-to-face with prospective students and academic advising meetings with current students. Additionally, departmental branding and communications should be evaluated for cohesiveness and inclusive representation of each major program across recruitment materials and publications.

To improve organizational siloing within the department, the lack of understanding between the three major programs needs to be addressed. Perhaps a disconnect in the internal branding of the department has created misconceptions between students. A more collaborative departmental environment could increase student buy-in and academic success (Schreiner, 2009). As student buy-in rises, the department will have less student turnover and major changes.

Perhaps a qualitative study could explain the meanings of AECL Department student perceptions toward other majors in the department. An additional exploratory study would be beneficial to interview students and gauge from their responses how they perceive the internal Branding of the AECL Department. These conclusions could be used to further strengthen the internal brand of the AECL Department and improve both faculty and student brand loyalty.

Future research should also be conducted on the number of AECL Department students who work in the agricultural industry after graduation. This study was focused more on the quantity of student responses, whereas future studies could look to see if different perceptions of majors exist based on a variety of student demographic variables.

Our research had several limitations, one being that career readiness and programmatic statements were based on our (the research team’s) perceptions of the department majors. The internal brand of a department may not be viewed the same by each of its constituents. Another limitation of this study was the inability to generalize and apply the results to other departments and institutions. It would be beneficial to replicate this study within the social sciences departments of other colleges of agriculture. The instrument needs to be validated among multiple settings with the target population for continued research use.

Although our research focused on students within the AECL Department at OSU, this study may serve as a guide to gain a better understanding of the agricultural education discipline as a whole. More research needs to be conducted on student major choice and satisfaction in the social science field of agriculture. Future studies should include students who change their major and leave the department. It is evident AECL Department students communicate and perceive majors, and levels of career readiness within those majors, differently; this can be a limitation for students if they have misconceptions about the career readiness and opportunities of a major.


Alam, M. I., Faruq, M. O., Alam, M. Z., & Gani, M. O. (2019). Branding initiatives in higher educational institutions: Current issues and research agenda. Marketing and Management of Innovations, 1, 34-45.

Alshathry, S., Clarke, M., & Goodman, S. (2017). The role of employer brand equity in employee attraction and retention: A unified framework. International Journal of Organizational Analysis, 25(3), 413-431.

Baker, L., Settle, Q., Chiarelli, C., & Irani, T. (2013). Recruiting strategically: Increasing enrollment in academic programs of agriculture. Journal of Agricultural Education, 54(3), 54-66.

Baker, L. M., & Abrams, K. (2011). Communicating strategically with generation me: Aligning students’ career needs with communication about academic programs and available careers. NACTA Journals, 55(2), 32-39.

Barrick, K. (1989). Agriculture education: Building upon our roots. Journal of Agricultural Education, 30(4), 24-29.

Bianchi, C., Williams, J. J., Drew, J. C., Galindo-Gonzalez, S., Robic, S., Dinsdale, E., Morgan, W. R., Triplett, E. W., Burnette, J. M., Donovan, S. S., Fowlks, E. R., Goodman, A. L., Grandgenett, N. F., Goller, C. C., Hauser, C., Jungck, J. R., Newman, J. D., Pearson, W. R., Ryder, E. F., Sierk, M., Smith, T. M., Tosado-Acevedo, R., Tapprich, W., Tobin, T. C., Toro-Martínez, A., Welch, L. R., Wilson, M. A., Ebenbach, D., McWilliams, M., Rosenwald, A. G., & Pauley, M. A. (2019). Barriers to integration of bioinformatics into undergraduate life sciences education: A national study of U.S. life sciences faculty uncover significant barriers to integrating bioinformatics into undergraduate instruction. Plos One, 14(11), 1-9.

Bureau of Labor Statistics. (2020). Employee Tenure in 2020. U.S. Department of Labor.

Chapleo, C. (2011). Exploring rationales for branding a university: Should we be seeking to measure branding in the U.K. universities? Journal of Brand Management, 18(6), 411-422.

Chen, C.-F., & Chen, C.-T. (2014). The effect of higher education brand images on satisfaction and lifetime value from students’ viewpoints. Anthropologist, 17(1), 137-145.

Colbath, S. A., & Morrish, D. G. (2010). What do college freshman know about agriculture: An evaluation of agricultural literacy. NACTA Journal, 54(3), 14-17.

Copp, N. H., Black, K., & Gould, S. (2012). Accelerated integrated science sequence: An interdisciplinary introductory course for science majors. Journal of Undergraduate Neuroscience Education, 11(1), A76-A81.

Cox, S., & Naylor, R. (2018). Intra-university partnerships improve student success in a first- year success and retention outreach initiative. Student Success, 9(3), 51-64.

Del Rossi, A. F., & Hersch, J. (2008). Double your major, double your return? Economics of Education Review, 27(4), 375-386.

Devasagayam, R. P., Buff, C. L., Aurand, T. W., & Judson, K. M. (2010). Building brand community membership within organizations: A viable internal branding alternative? Journal of Product & Brand Management, 19(3), 210-217.

Dholakia, R. R. (2017). Internal stakeholders’ claims on branding a state university. Services Marketing Quarterly, 38(4), 226-238.

Eldegwy, A., Elsharnouby, T. H., & Kortam, W. (2018). How sociable is your university brand? An empirical investigation of university social augmenters’ brand equity. International Journal of Educational Management, 32(5), 912-930.

Erdoğmuş, İ., & Ergun, S. (2016). Understanding university brand loyalty: The mediating role of attitudes towards the department and university. Procedia – Social and Behavioral Sciences, 229, 141-150.

Evans, N. (2012). Destroying collaboration and knowledge sharing in the workplace: A reverse brainstorming approach. Knowledge Management Research & Practice, 10(2), 175-187.

Foreman, B., Retallick, M., & Smalley, S. (2018). Changing demographics in college of agriculture and life sciences student. NACTA Journal, 62(2), 161-167.

Fosnacht, K., & Calderone, S. M. (2017). Undergraduate financial stress, financial self-efficacy, and major choice: A multi-institutional study. Journal of Financial Therapy, 8(1), 107-123.

Franzen, G., & Moriarty, S.E. (2009). The Science and Art of Branding (1st ed.). Routledge.

Friedman, H. H., & Kass-Shraibman, F. (2017). What it takes to be a superior college president: Transform your institution into a learning organization. The Learning Organization, 24(5), 286-297.

Graunke, S., & Woosley, S. (2005). An exploration of the factors that affect the academic success of college sophomores. College Student Journal, 39(2), 367-376.

Guth, D. J. (2017). Building a new structure. Community College Journal, 87(5), 28-33.

Hertel, T. J., & Dings, A. (2014). The undergraduate Spanish major curriculum: Realities and faculty perceptions. Foreign Language Annals, 47(3), 546-568.

Huddleston, T. J. (2000). Enrollment management. New Directions for Higher Education, 111(7), 65-73.

Joseph, M., Mullen, E. W., & Spake, D. (2012). University branding: Understanding students’ choice of an educational institution. Journal of Brand Management, 20(1), 1-12.

Mazzarol, T., & Soutar, G. N. (2002). “Push‐pull” factors influencing international student destination choice. International Journal of Educational Management, 16(2), 82-90.

McKim, A. J., Greenhaw, L., Jagger, C., Redwine, T., & McCubbins, O. P. (2017). Emerging opportunities for interdisciplinary application of experiential learning among colleges and teachers of agriculture. NACTA Journal, 61(4), 310-316.

Milsom, A., & Coughlin, J. (2017). Examining person-environment fit and academic major satisfaction. Journal of College Counseling, 20(3), 250-262.

Mourad, M., Meshreki, H., & Sarofim, S. (2020). Brand equity in higher education: Comparative analysis. Studies in Higher Education, 45(1), 209-231.

Ng, I. C. L., & Forbes, J. (2009). Education as service: The understanding of university experience through the service logic. Journal of Marketing for Higher Education, 19(1), 38-64.

Pinar, M., Trapp, P., Girard, T., & Boyt, T. E. (2014). University brand equity: An empirical investigation of its dimensions. International Journal of Educational Management, 28(6), 616-634.

Punjaisri, K., & Wilson, A. (2011). Internal branding process: Key mechanisms, outcomes and moderating factors. European Journal of Marketing, 45(9/10), 1521-1537.

Sartain, L., & Schumann, M. (2006). Brand from the inside. Jossey-Bass.

Schmidt, H. J., & Baumgarth, C. (2018). Strengthening internal brand equity with brand ambassador programs: development and testing of a success factor model. Journal of Brand Management, 25(3), 250-265.

Schreiner, L. A. (2009). Linking student satisfaction and retention. Noel-Levitz.

Scofield, G. G. (1994). An Iowa study: Factors affecting agriculture students career choice.

NACTA Journal, 38(4), 28-30.

Simi, J., & Sudhahar, C. (2019). Dimensions of internal Branding: A conceptual study. Journal of Management, 6(1), 177-185.

Stair, K., Danjean, S., Blackburn, J. J., & Bunch, J. C. (2016). A major decision: Identifying factors that influence agriculture students’ choice of academic major. Journal of Human Sciences and Extension, 4(2), 111-125.’_Choice_of_Academic_Major

Stebleton, M. J., Huesman, R. L. J., & Kuzhabekova, A. (2010). Do I belong here? Exploring immigrant college student responses on the SERU survey sense of belonging/satisfaction factor. Center for Studies in Higher Education Research & Occasional Paper Series, 13(10), 1-12.

Sujchaphong, N., Nguyen, B., & Melewar, T. (2015). Internal branding in universities and the lessons learnt from the past: The significance of employee brand support and transformational leadership. Journal of Marketing for Higher Education, 25(2), 204-237.

Swaminathan, V., Sorescu, A., Steenkamp, J.-B. E. M., O’Guinn, T. C. G., & Schmitt, B. (2020). Branding in a hyperconnected world: Refocusing theories and rethinking boundaries. Journal of Marketing, 84(2), 24-46.

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Stacy K. Vincent, University of Kentucky,

Andrew Hauser, University of Kentucky

Lucas D. Maxwell, Illinois State University

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Introducing, building support for, and implementing an innovation requires many components of diffusion to be in place. The purpose of this qualitative case study was to investigate the most effective methods for developing new agricultural education programs. Using Roger’s (2003) Diffusion of Innovation model, four themes emerged from information collected in this study: a) identifying stakeholder needs, b) communication, c) education, and d) securing funding. Recommendations include creating informational packets for community members and administrators, hosting webinars to determine best practices, develop relationships with potential stakeholders, and research on additional information desired by secondary stakeholders and the circumstances under which diffusion in agricultural education fails. 


When secondary students graduate high school, their next successful transition is career, military, or post-secondary education. For many secondary students, productive dispositions and behaviors, coupled with non-cognitive skills, are most predictive of future earnings (Castellano, et al., 2017). Therefore, emphasis has been placed on college and career readiness through career and technical education programs. A number of educational organizations, including the National Association of State Directors of Career and Technical Education (NASDCTE), the Association for Career and Technical Education (ACTE), and the Partnership for 21st Century Skills, agree that all students in the United States should be college and career ready (Erdogan & Stuessy, 2016). However, the U.S. Department of Education (2010) reported that approximately 40% of all college freshmen are enrolled in remedial courses. To address this, schools have placed a greater emphasis on providing education which better prepares students for the transition from high school to higher education, the workplace, and/or the military. To ensure all students, regardless of geographic location, were indeed prepared to be college and career ready after completing their education in a public school, Common Core State Standards were developed in 2009 (Center for American Progress, 2014). In addition, common assessments were designed to measure the college readiness of secondary students (McIntosh, 2010). Although measuring college readiness is advancement in education, simply measuring readiness will not develop curriculum which allows students to pass. Instead, it is vital to also develop programs designed to produce career readiness in addition to college readiness, and to do so in a program that is available to all secondary students.

Along with conventional academic subjects, schools are turning to career and technical education courses to facilitate the development of college and career readiness. Not only does career and technical education provide knowledge and skill sets for students, but it also introduces potential career fields, as evidenced by a study in New York that revealed over half of middle school participants identified areas of interest that were representative of the agriculture, food, fiber, and natural resources industry (Conroy, 2000).

Secondary agriculture courses provide opportunities for a variety of learners to excel and succeed in an educational setting. This can be seen in a study completed by Roberts, Hall, and Briers (2009) in which new agricultural programs in Texas schools encouraged Latino students to become more active in their education by joining and participating in an intracurricular program. Furthermore, parents of the students and alumni from the area became more active in the students’ education. Access to a teacher who provides intracurricular opportunities, such as an agricultural educator, help at-risk students improve their GPAs and narrow the achievement gap (Herrick, 2010). 

Career and Technical Education (CTE) not only helps engage minorities and at-risk students, but it also helps to increase the high school transition rate. According to Wells, Gifford, Bai, and Corra (2015), only 75% of public high school students earn a diploma within four years of entering the ninth grade. Fortunately, research infers students who enroll in career and technical education demonstrate higher levels of engagement, leading to a reduction in the probability of the students dropping out of school and improved levels of academic performance (Evan,et al., 2013). In the 2013 study, the researchers identified the geographic location of all career academies in public schools in Florida in order to determine the type of student most likely to enroll in the career and technical education classes, as well as school information such as dropout and student engagement rates, and then compared those numbers to geographic areas which did not offer career academies. They found that enrollment in career and technical education gives students an advantage in academic and career success. Furthermore, in a study by Neild, Boccanfuso, and Byrnes (2015) determined that increased engagement in and out of the CTE classroom was correlated to a reduction in disciplinary referrals.

All students can benefit from completing a course in career and technical education. In a study completed by Hagen (2010), every student in one high school completed at least one career and technical course per year. As a result of being enrolled in the course, the study revealed student engagement, achievement, student transitions, attendance rates, dropout frequency, graduation potential all improved. In addition, students developed a higher level of self-confidence and competence. Once students experience the development of their self-confidence, they experience a higher level of motivation (Eggen & Kauchak, 2010). Those who completed agricultural courses demonstrated a higher need for achievement than those students who did not complete an agricultural course. Furthermore, participants who completed agricultural courses and joined the local FFA chapter demonstrated an even higher need for achievement (Herren & Turner, 1997).

With the benefits from agricultural education courses that ensure the development of college and career readiness, an expectation for program expansion should be a priority; however, 15 states have seen the number of agricultural programs decrease since the 2011-2016 academic year. The remaining states had an average increase of 12.6 programs per state, with 67% of that growth coming from ten states (National FFA Organization, 2017). What are the high growth states doing to increase the number of programs, and to provide secondary students with the opportunity to succeed?  Why aren’t states employing methods to produce more career and technical, and specifically agricultural, programs? This study focuses on obtaining answers to those questions so that all secondary students can experience the benefits of agricultural education.

Conceptual/Theoretical Framework

The guiding theory of this study is Rogers’ Diffusion of Innovations (Rogers, 2003). An innovation is defined as a new idea, product, or method as perceived by an individual or group of people (Rogers, 1983). According to Rogers (2003), an innovation is adopted after a need is determined. The Diffusion of Innovations Theory describes the necessary means for an innovation to be adopted by a group of people. Rogers (2003) described diffusion as a process where innovation is communicated through various methods over time among the members of a social system.           

Diffusion can only occur when certain elements are present. Specifically, there are four essential elements for diffusion to occur. The source, or the entity presenting the innovation, provides two of those elements: the innovation itself and the communication of the innovation. The communication presented by the source must be through distinct channels, which connect the source to the social system in which the innovation is being presented. These channels can include mass media (television, social media, etc.) and/or interpersonal interactions (face to face meetings). According to Rogers (2003), the most effective channel for innovation is an interpersonal channel in which someone of similar status (age, education, etc.) completes a subjective evaluation of the innovation after adopting it themselves. People of similar status are referred to as “near peers”. The third element, time, occurs throughout the process as the fourth element, a social system, or adaptor, considers the innovation before it decides to diffuse the innovation (Rogers, 2003). Figure 1 provides a visual interpretation of the Diffusion of Innovation.

Figure 1

The four elements (innovation, communication through certain channels, time, and social systems) play roles in different stages of diffusion (Rogers, 1983). The knowledge stage is completed when the source communicates to the social system about the existence of the innovation and provides a basic understanding of said innovation. An example of how these stages can be seen in an educational setting can be found in a study by Hagen (1999) in which Valley City State University adopted a learning environment in which an innovation curriculum computer program was utilized. In this environment, all faculty and students were given laptops with constant access to the internet. The music department of the university entered the knowledge stage when they learned about what the technology could offer their department. As faculty learned about the possibilities, they entered the persuasion stage during in which they evaluated their initial impressions of the innovation. In this case, positive impressions were developed. The decision stage was completed when faculty began to create curriculum for the music department. The curriculum was implemented, and students focused on five innovative areas via the new environment: communication and aesthetic responsiveness, problem solving, effective citizenship and global perspective, collaboration and wellness, and technology (Hagen, 1999). 

Barriers can prevent the adoption of an innovation, and different barriers arise in different stages. Nelson and Thompson (2005) studied the diffusion of new technology into distance learning programs. When asked what the biggest barriers were to the adoption of the new technology, about 40% of teachers agreed the lack of adequate compensation for time and effort of the faculty was a major barrier. About 35% of respondents agreed the lack of faculty rewards/incentives was a major barrier and one-third of teachers agreed program development costs were also a major barrier to innovation.

Purpose and Objectives

The purpose of this qualitative multiple case study was to determine the most effective methods for program expansion of new agriculture education programs. The research questions were:

  1. What methods of diffusion are utilized in the conception and implementation of new agricultural education programs, as described by leaders in state associations?
  2. What barriers must be addressed to establish new programs?

Methods and Procedures

This study is a qualitative case study in nature, as designed by Denzin and Lincoln (2008). Case studies investigate a phenomenon, population, or general condition, which is common between different cases (Creswell, 2009). Case studies facilitate the conveyance of a participant’s experience to the researcher.  Case studies are chosen because it is believed that understanding them will lead to a better understanding about a larger collection of cases (Yin, 2008). In this study, the researchers investigated a series of phenomena, common in multiple states. The researchers sought to obtain detailed information about the methods and communication channels used when launching new agricultural education programs throughout the United States.

Participant Selection

Ten states served as the sampling frame for this study due to their growth of secondary agriculture programs. Each participant selected was chosen or nominated because of his or her affiliation with the state’s growth. Achieving the best understanding of phenomena depends on choosing the best cases to study (Denzin & Lincoln, 2008). In this research study, the participants were selected based upon a series of standards set by the researchers. With the help of the National FFA Organization, ten participants met a predetermined series of qualifications. Qualifications for participant selection were as follows: represent a state which obtained a 10%+ chapter growth over a seven-year period at the time of growth and was employed by the state FFA association or by the state’s Department of Education. The selected states had a growth range from 21 to 43 programs.

Researchers selected participants because 1) their state was one of the top 10 states to experience  a growth of 10%+ in the last seven years in secondary agriculture programs; and 2) they were the considered instrumental in the state programmatic expansion by their peers. Three states considered a team of individuals were responsible for the state’s growth. The researchers selected to interview the group of individuals within the selected states in a focus group interview; thus, each state’s selected participant(s) partake in an interview.


The researchers focused on obtaining information about the growth of agricultural education programs over the past two decades. The National FFA Organization was contacted and information for the past seven years was provided. The National Association for Agricultural Education did not have an accurate count of agricultural education programs; therefore, the frame was limited to growth over the past seven years. The number of FFA programs were compared for all state associations from the 2010-2011 and 2017-2018 academic years. Because there must be an agricultural education program in place in a school before an FFA chapter could be started, the researchers decided the number of FFA chapters in a state was an accurate method for determining the highest growth.

Semi-structured interviews were completed, with each participant being interviewed for approximately one hour. The open-ended questions used in the interview were structured and centered on the individual’s views and experiences planning and implementing new agricultural programs, as it aligned with Rogers’ (1983) model. Following Yin’s (2018) framework of questioning, the researchers gradually modified their questions to transition from Level 1 questioning to Level 4 questioning. Pre-established prompts were used, but the order in which the questions were asked depended upon the participant’s responses. Furthermore, clarifying and elaborating probes were utilized as needed in order to provide further explanations and seek more detail in participant responses. The interviews took place over the phone and were audio-recorded.  Throughout the interview, the moderating researcher took field notes of reactions from the participant and researcher. After each interview was completed, the researcher expanded on the field notes in a reflective journal. Each interview was transcribed for analysis. 

Data Analysis

This data from this study consisted of field notes, taped interviews, transcribed interviews, and journals of personal reflections over the interview. Once all data were collected, researchers read and reread the data to become more familiar with them. Then, the researchers independently and systematically coded the data by identifying phrases and topics which consistently were mentioned in the data. Next, researchers used axial coding to create themes from the codes through the iterative method of recursive analysis. By utilizing recursive analysis, researchers constantly compared each piece of data to the prior pieces of data in order to ensure the themes were representative of the data (Yin, 2008). The researchers coded the thematic responses following Rogers’ Diffusion of Innovations to develop assertions. To add multiple perceptions to the analysis of the data for the enhancement of validity and credibility to the results, triangulation was utilized throughout the procedures. 


After all interviews were conducted, audio recorded, transcribed verbatim, and field notes written and expanded upon in a journal, the researchers independently coded data and then compared results. Peer debriefing was utilized from an outside source throughout data collection and the coding process as an impartial colleague was asked to review methodology. Furthermore, the researchers independently coded the data and then compared results. This created inter-rater reliability, which enhances thematic credibility (Denzin & Lincoln, 2008; Saldena, 2009). To increase the trustworthiness of this study, member checking was utilized. Each participant was given a copy of the findings and asked to confirm the results, creating data confirmability (Denzin & Lincoln, 2008). Follow-up phone calls were conducted to each participant to verify the content of their interview. Finally, the findings and conclusions were sent to the participants for validation. Overall, the researchers established credibility of the data using reference materials, peer debriefing, and member checking. 

Triangulation and Bracketing

Triangulation is the simultaneous display of multiple realities (Denzin & Lincoln, 2008). Data source triangulation, which is the use of multiple sources of data, were utilized in this study. There were three major types of data: interviews, field notes, and a reflective journal kept by the researchers. Investigator triangulation was also used. Because the researchers have not experienced the same events as the participants, there are multiple realities, which could lead to bias from the researchers. In order to prevent bias in this study, the background of the researchers should be addressed. The two researchers are involved in agricultural education. One researcher is involved in teacher education and taught at the secondary level for seven years prior to beginning his career at the collegiate level. The other is a current agriculture teacher who has four years of experience and is currently working on a PhD in Curriculum and Instruction. Investigator triangulation was utilized in all interviews and interactions with participants to minimize the influence of biases. This was achieved through independent coding and the maintenance of a reflective journal to describe the interactions between the researcher and the participants and to note any biases. 


For all findings, participants have been given alias names. Research question one sought to determine what methods of diffusion are utilized in the conception and implementation of new agricultural education programs. For this objective, two themes emerged.

Theme 1: Identify potential stakeholders and their needs

Throughout the interviews, participants emphasized the importance of identifying stakeholder needs. Several different stakeholders were identified, and three subgroups emerged.  The first group is community members ranging from parents, students, alumni, to legislators. The second group is school officials, ranging from administrators, school board members, superintendents, and the Department of Education. The third group included important people in the agriculture industry, such as farmers, Farm Bureau, the Department of Agriculture, sponsors, and other agribusinesses. These different groups of stakeholders have different needs for an agricultural education program. For example, a farmer would want an agricultural education program that would focus on production agriculture. However, an urban community would want an agricultural program to focus on urban agriculture and sustainability. A school administrator would want a program that would appeal to students, parents, and testing scores. Participants in the study emphasized the importance of first building a relationship with and then helping stakeholders to identify their needs and then providing them with the necessary tools and resources to advocate and build support for an agricultural education program in their community. Table 1 includes supporting statements from participants.

Theme 2: Communication is a method of entry

An important part of building relationships with stakeholders is communication. In each interview with participants, communication was a factor in creating and implementing new agricultural education programs. Two sub-themes emerged under this umbrella: methods of communication and resources in communication. Methods of communication varied, but a theme emerged with the most popular ones. Participants overwhelmingly responded that face-to-face meetings were the most effective methods of communication. However, participants recognized the need for other methods of communication, such as emails and telephone calls. For communication with students, the participants reported social media, including Facebook and Twitter, were the most effective method. Participants also listed several documents and online resources they utilize in order to implement new programs. These resources include student surveys, educational literature about agriculture and agricultural education, course standards, websites with information about agriculture courses and opportunities in FFA, and letters. State officers are also used as resources. Additional resources mentioned include grants for new programs, time placed into working with new programs, conference calls and webinars for teachers in new programs, leadership training for officers of new programs, state staff designated to help a region of teachers, and an employee who visits new programs and provides assistance as requested.   Table 2 includes supporting statements for this theme. 

Research question two sought to identify what barriers had to be overcome in order to implement new agricultural education programs.  For this objective, two themes emerged.

Theme 1: Overcoming educational barriers

When asked about barriers to creating and implementing new agricultural education programs, participants responded one barrier was the education of communities. Two sub-themes also emerged.  The first sub-theme is the education of the community (including all stakeholders) about the importance of agriculture. This was especially noted in urban and suburban areas as many people in those areas are unaware of the importance of agriculture in their community. However, in order to begin new programs, the community must see value in agriculture in order to develop a need for an agricultural education program. Participants spoke about how their first task in beginning new programs was to establish validity and importance for agriculture. The second sub-theme is the education of school officials, including administrators, school board members, and superintendents, about the difference between agricultural education and FFA.  Several participants spoke about schools that wanted to start an FFA chapter, but were unaware they had to have an agricultural education program first. Participants spoke of the importance in making school officials understand the importance of agricultural education, and not just the FFA component. Table 3 gives quotes from participants about community education.

Theme 2: Addressing financial support issues

 Overwhelmingly, the biggest barrier participants reported having to overcome is the lack of funding, or trying to find funding for new agricultural education programs to start. In most states, agriculture teachers are paid for extended days or they are paid on ten to twelve month contracts because they work with students throughout the year, regardless of whether or not school is in session. Because of this, many states have passed legislation that allows agriculture teachers to be paid for this time. In addition to paying a higher salary to a teacher, agricultural programs require additional facilities. For example, some schools have Agriscience labs or shops. Other schools have farms for their students. No matter what facilities are chosen for a program, it is an investment for schools, and many schools have struggled to find funding for new programs. The economic downturn, beginning in 2008, has also made it hard for schools to find funding for new programs. Table 4 provides participant responses on the funding barriers.

Conclusions, Implications, & Recommendations

The researchers recognize the limitations to this study and acknowledge that the findings were based upon the stories of a few individuals who were instrumental to the significant growth of program expansion in their state. However, this study did not include members of the community where the programs began, members of the organizations that further assisted with the growth, nor governmental or school officials that assisted with program expansion. Furthermore, the study is viewed from the lens of the authors who took a pragmatic worldview; hence the use of Roger’s model as a guide. Nevertheless, the findings from this study were implemented which have resulted in program development and expansion of programs within the Commonwealth of Kentucky. Of the programs developed, all have maintained partnerships with the community agency and are still present in secondary schools.

Although no community is the same and agricultural programs were not be created and implemented homogenously, there are similarities in the diffusion process. From this study, it was concluded that one of the first steps in creating new programs is to identify the needs of all stakeholders, including the community members, school officials, and industry representatives. Another important part of the process is to use the most effective communication channels possible. Although all types of communication are used (phone, email, letter, etc.), the most effective method of communication is face-to-face meetings. Resources must be used efficiently as well, in order to increase the understanding of agricultural education for the school officials and community. Resources that are utilized include websites, documents, student surveys, etc. 

The need for stakeholders serves as Channels to Rogers’ (1983) Diffusion of Innovation Theory. These stakeholders are the voice box for promoting the start of the agricultural education program from within a community. Various organizations serve as fundamental platforms and provide opportunities for community members to encourage the promotion of a secondary program within a school. To begin the process of innovation, agricultural education state staff members who are looking for the correct channel should consider meeting with various advocate groups that can assist in the process. Examples of organizations to be contacted, but not limited to, are the local Farm Bureau, the National Association of Agricultural Educators, and the local Chamber of Commerce. Each serve as interpersonal channel that could open opportunities for an agricultural education program to exist. Based upon Rogers’ model, the interpersonal channel the participants made, had a great impact on the adoption of new program expansion.

State staff members are recommended to build relationships with community leaders, administrators, and leaders in the agricultural industry prior to program proposals. Once established, the relationships serve as additional adopters for promoting and advocating the implementation of new agricultural education program. In addition, states with an interest in program expansion should seek to increase support at the state level. In this study, participants expanded upon utilizing trained support, such as state officers or teachers in the state, to further help in explaining the benefits and developing supportive networks. From the findings, it is recommended that states with the desire of program expansion seek opportunities to gain help by providing professional development in the area of advocacy. Such professional development could be provided to teachers within the state, state officers, alumni, and members of Foundation boards.  Building support by developing relationships with adopters is vital in getting a community to adopt an innovation (Rogers, 1983).

Communication in this process occurs through various ways, including phone calls, email, letters, etc., but the participants overwhelmingly agreed that face-to-face meetings are the most effective way to implement new programs. However, the main key is that an initial contact be implemented. For example, in a study utilizing Diffusion of Innovation Theory, small businesses who showed the most growth made various efforts to contact potential clients (Nooteboom, 1994). Resources that are crucial to share include student surveys, information about agriculture and agricultural education, agricultural education and FFA websites, and examples of other successful agricultural education programs in an area like the school which is considering a program. Currently, the National FFA Organization provides opportunities for administrators of non-agricultural education programs to attend the National FFA Convention to understand the value of the profession. Each state is encouraged to consider opening the door of their State FFA Convention to administrators of schools. Through these methods, communication serves as a source to the Diffusion of Innovation Theory (Rogers, 1983).

If support for agricultural education could be raised in school systems that do not currently have programs, they would be much more likely to implement one. Therefore, it is recommended that advocates for agricultural education programs should target school administrators to encourage implementation. To best implement the recommendation, face-to-face meetings are suggested, based upon the findings.  In addition, different resources, such as community, financial, industrial, and collegial, are beneficial if established prior to meeting. The establishment of such resources solves for barriers that may limit the social system (Rogers, 1983).

To fulfill Rogers’ Diffusion of Innovations Theory more efficiently in the context of agricultural education program development, it is recommended that stakeholders develop educational materials for individuals seeking to propose new agricultural education programs. Similarly, Roberts, et al. (2009) determined that new curriculum was needed for program expansion in Hispanic serving program where Roger’s model was implemented. These educational materials could consist of, but are not limited to, creative program support information, beneficial results from agricultural education, and correspondence from administration of successfully started programs.

It was concluded that the development of a new program is costly and such cost may sometimes be detrimental to its development. Therefore, supporters of agricultural education, such as National Association for Agricultural Education (NAAE) and its state affiliates, should lobby to state legislatures in the development of funding resources for establishing new programs. Such funding sources, such as the STAR program, would assist in addressing barriers and further securing a commitment between administration and agricultural education stakeholders.

To help understand the emerging themes, the researchers developed the Agricultural Education Program Development Concept Model (Figure 2). It was concluded from the results in this study that the model best represented what the participants conceptualized for their success in program expansion. Each of the four emerging themes are all linked together in the process of program expansion. Although time is necessary in the development of a new program, stakeholders must be involved, funding sources need to be addressed, a clear and open flow of communication has to exist, and the school or profession must identify and extinguish educational barriers.

Figure 2
Agricultural Education Program Development Concept Model

It is important to note that this model is merely a visual representation of how the participants in this study conceptualize program expansion. It is recommended that the model serve as a guide toward the development of questionnaires in exploring and validating the findings in this study.

The National FFA Organization, in partnership with NAAE, are encouraged to host a series of webinars in which state leaders, such as the participants in this study, collaborate and share their ideas for new program expansion. The purpose of the webinar would be to create a list of best practices for distribute to all associations, which could then be utilized in the creation and implementation of agricultural education programs across the nation.

To provide opportunities for all students in all schools to become college and career ready, programs which provide that type of preparation must be available, including agricultural education. By following Rogers’ (1983) Diffusion of Innovation Theory and ensuring all necessary stakeholders are involved, clear and effective communication is presented, and options to overcome barriers are developed, it is possible to help schools adopt this innovation and improve the education provided to their students.


Brown, N. R., & Kelsey, K. D. (2013). Sidewalks and city streets: A model for vibrant agricultural education in urban American communities. Journal of Agricultural Education, 54(2), 57-69.

Castellano, M., Sundell, K. E., & Richardson, G. B. (2017). Achievement outcomes among high school graduates in college and career readiness programs of study. Peabody Journal of Education, 92(2), 254-274.

Center for American Progress. (2014). Common core state standards assessments: Challenges and opportunities.

Conroy, C. A. (2000). Reinventing career education and recruitment in agricultural education for the 21st century. Journal of Agricultural Education, 41(4), 73-84.

Creswell, J. W. (3 Ed.). (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications.

Denzin, N. K., & Lincoln, Y. S. (2008). Strategies of Qualitative Inquiry. SAGE Publications.

Eggen, P., & Kauchak, D. (2010) Educational psychology: windows on classrooms. Pearson Education, Inc.

Erdogan, N., & Stuessy, C. (2016). Examining the role of inclusive STEM schools in the college and career readiness of students in the United States: A multi-group analysis on the outcome of student achievement. Educational Sciences: Theory and Practice, 15(6), 1517-1529.

Evan, A. J., Burden, F. F., Gheen, M. H., & Smerdon, B. A. (2013). Explaining variability in high school students’ access to and enrollment in career academies and career theme clusters in Florida: Multi-level analyses of student and school factors. Career and Technical Education Research, 38(3), 211-243.

Hagen, S. L. (1999). Technology diffusion and innovations in music education in a notebook computer environment. Research Proceedings of the 1999 Society for Information Technology & Teacher Education International Conference. San Antonio, TX. 

Hagen, S. N. (2010). The effects of mandated Career and Technical Education (CTE) on the college and career preparation of high school students. (Doctoral dissertation). Purdue University, West Lafayette, IN.

Herren, R. V., & Turner, J. (1997). Motivational needs of students enrolled in agricultural education programs in Georgia.  Journal of Agricultural Education, 38(4), 30-41.

Herrick, C.J.  (2010) The impact of an advisor-advisee mentoring program on the achievement, school engagement, and behavior outcomes of rural eighth grade students. (Doctoral Dissertation) University of Nebraska, Omaha, NE.

McIntosh, S. (2010). State high school tests: Changes in state policies and the impact of the college and career readiness movement.  Washington, DC: Center on Education Policy.

Nield, R. C., Bocannfuso, C., & Byrnes, V. (2015). Academic impacts of career and technical schools. Career and Technical Education Research, 40(1), 28-47.

Nelson, S. J., & Thompson, G. W. (2005). Barriers perceived by administrators and faculty regarding the use of distance education technologies in preservice programs for  econdary agricultural education teachers. Journal of Agricultural Education, 46(4), 36-48.

Nooteboom, B. (1994). Innovations and diffusion in small firms: Theory and evidence. Small Business Economics, 6(5), 327-347.

Roberts, T. G., Hall, J. L., & Briers, G. E. (2009). Engaging Hispanic students in agricultural education and the FFA: A 3-year case study. Journal of Agricultural Education, 50(3), 69-80.

Rogers, E. M. (1983). Diffusion of Innovations. Macmillan.

Rogers, E. M. (5 Ed.). (2003). Diffusion of Innovations. Macmillan.   

U.S. Department of Education. (2010). A blueprint for reform: The reauthorization of the elementary and secondary education act. Education Publications Center.

Wells, R., Gifford, E., Bai, Y., & Corra, A. (2015). A network perspective on dropout prevention in two cities. Educational Administration Quarterly, 51(1), 27-57.

Yin, R. K. (2018). Case study research and applications: Design and methods. SAGE.

Georgia Extension Agents’ Perceptions of Rural Stress

Jessica Holt, University of Georgia,

Madison Crosby, University of Georgia,

Kevan Lamm, University of Georgia,

Abigail Borron, University of Georgia,

Alexa Lamm, University of Georgia,

PDF Available


Rural populations, including farmers, are often disconnected from more populated areas. This disconnect includes access to healthcare and resources to address stress and mental wellbeing. Stress has been shown to lead to increased suicide rates and substance abuse. Because Extension agents are often a trusted source of information within these rural communities, identifying rural communities’ levels of stress is an essential step in creating programmatic efforts to address mental health disparities with these individuals. Utilizing a 10-item survey, Georgia agents’ perceptions of stress/mental health in their communities were measured. The survey results of 281 agents (90% response rate) were analyzed for means and frequencies to better understand what specific factors associated with stress/mental health may be the most pressing for rural communities. The findings indicate the level of stress varies by location within the state. This finding indicates programmatic efforts and future research may need to target specific location needs given the unique needs of the audience, rather than a one-size-fits-all approach.


With agriculture being the largest industry in Georgia, contributing $73.3 billion annually to the state’s economy (Kane, 2019), the farming population of the state is relied upon heavily to sustain a significant contribution to the state’s economic viability. With such a large portion of the state’s economy dependent upon the success of its rural communities, it only stands to reason the health and wellbeing of those involved in agriculture would be a forefront of consideration. However, little research or programmatic efforts have been solely focused on understanding the impacts mental health and stress play on rural communities, specifically farming communities.

According to the U.S. Census, rural is defined as “all territory, persons, and housing units not defined as urban” (Ratcliffe et al., 2016, p. 2). In 2010, this defined area accounted for approximately 97% of the U.S. land area and 19.3% of the population (Ratcliffe et al., 2016). According to the most recent documents, in Georgia during 2012, 38.6% of the state’s land was used for agricultural purposes and in 2018, 17.1% of its population occupied rural lands (Rural Health Information Hub, 2019). As the demands and challenges of production agriculture continue to grow and unfold, rural communities become ever increasingly essential.

Over the last decade, a prevalent issue in rural U.S. communities has become mental health and stress (Centers for Disease Control, 2019a). Primary indicators of this issue are documented in the increase of pharmaceutical addiction and suicide rates (Centers for Disease Control, 2019b; Centers for Disease Control, 2019d). With approximately 46 million individuals living in these rural areas, this level of social disparity is observable, but often overlooked Centers for Disease Control, 2019c). In 2018, 13.9 suicide deaths per 100,000 people within the population of Georgia were reported (America’s Health Rankings, 2019). Additionally, the Centers for Disease Control and Prevention has reported an increased risk of suicide in rural areas than urban populations (Clay, 2014). Experts have attributed this increased risk due to higher use of drugs and alcohol, with fewer health care providers and hospital access, as well as access to firearms (Clay, 2014). This increased risk poses many risks to the overall health and vitality of rural communities. In the following section, considerations to rural stress/mental health within rural communities is expounded upon to further elucidate the issues mentioned above.

Conceptual Framework

The boundaries of rurality can often be visually depicted with open fields along a highway; however, much like mental health the definition of rural is much more complex and divisive. While the U.S. Census Bureau defines rural as not urban, and defines urban as those areas with 50,000 or more residents (Ratcliffe et al., 2016) other sources apply culture, lifestyle, and clusters of residents (Ratcliffe et al., 2016; Vanderbroom & Madigan, 2007). The current study was not developed to add validity to a definition of rural, rather to recognize that the needs and barriers of those residents in less densely populated areas, specifically farming areas, are different than those in urbanized areas.

Rural Culture

With pharmaceutical addiction and suicide rates on the rise in rural communities, the need to identify the associated catalyst is essential. One contributing stressor is the overall work environment. By working and living in isolated areas and having little time away from work, farmers have minimal separation between work and personal life (Gregoire, 2002). In addition, the farming community can include living in small, tight-knit rural communities, which can lack privacy. These factors can create additional stress and exhaustion, which can lead to accidents (Naik, 2017). Compounding that stress, agriculture and farming have unique and variable factors that can add stress and uncertainty. For example, Kolstrup et al (2013) identified that dairy farmers have stressors such as diseases related to livestock, taxes on their production, and negative attitudes of the public toward their practices. These stresses can take a toll on mental health and wellbeing, leading to self-prescribed methods of coping (Clay, 2014; Gregoire, 2002; Naik, 2017).

When seeking assistance for issues related to mental health, male farmers experience barriers like distance to appointments, lack of financial access for help, and stigma associated with mental health and treatment or help-seeking behaviors (Roy, Tremblay, & Robertson, 2014). Research has demonstrated that sometimes in the farming community, the idea of the masculine farmer as tough and self-reliant can conflict with individuals seeking help for mental health issues, which can be construed as a sign of weakness (Naik, 2017). Education, and especially educating young people, in rural areas can help destigmatize mental health and the act of seeking mental health help (Gregoire, 2002). Research has recommended for both governmental and non-governmental organizations to assist with mental health education, awareness, and stigma associated with mental health (Gregoire, 2002).

In New Zealand and Australia, research has recommended implementing preventative programming, online initiatives, events, workshops, and publications to encourage a discussion about mental health in the agricultural industry (Naik, 2017). Mental health programming can assist in opening the conversation and breaking the taboo about mental health in the communities (Naik, 2017), as well as enhance the ability to recognize the signs of mental health issues (El-Amin et al., 2019).

Additionally, farmers are more likely to seek services from people and organizations they trust for health-related information (Kilpatrick et al., 2012). As such, mental health programs need to continue to be outside of traditional medical facilities to help with health in rural and farming communities (Roy et al., 2014). The inclusion of community member volunteers can help with small group counseling sessions and encourage participants to feel more open to expressing their concerns due to their familiarity and living in the same rural community (Thompson & McCubbin, 1987). Research has found that mental health promotion might be better disseminated through other non-governmental networks since most farmers visit their physicians only to deal with obvious physical issues (Gregoire, 2012).

A farmer’s family and other social support systems can help farmers’ deal with stressors they face (Anderson et al., 2012; Fraser et al., 2005). Other close relationships, such as friendships, can help farmers and serve as a coping device to deal with stress (Roy et al., 2014). For example, role models have been found to play an important role in helping address male farmers’ distress and promoting help-seeking behavior for mental health (Roy et al., 2014). Additionally, finding trusted relationships where farmers seek out confidants in their community beyond their immediate farming peers has shown to be important (Roy et al., 2014). Overall, mental health services need to be made more accessible to farmers in a comfortable, trusted environment with trusted people (Polain et al., 2011).

Rural Healthcare

In addition to the individual and contextual challenges associated with rural environments, there are also systemic challenges associated with the rural healthcare system. For example, rural healthcare workers have stressors related to being overbooked with clients, working in multiple locations, a lack of support in staffing due to a high-turnover rate, and frustrations with technology (Hasbrouck & Waddimba, 2017). Research has suggested that healthcare organizations should invest in programming and mentoring to help healthcare workers maintain their health to care for patients, cope with stress, and deal with the stigma of someone receiving help (Hasbrouck & Waddimba, 2017). Previous research demonstrated that mental health services need to be made more accessible, especially to farmers who work closely on their land or who lack mobility (Polain et al., 2011). Additionally, farmers feel physicians need to understand their culture better to treat them more effectively and recognize the risks involved with the integrated relationship between their careers and lifestyle choices (Anderson et al., 2012). Nevertheless, there has been an observable trend to increase mental health literacy and programming in the United States in both rural and urban settings; however, there remains a gap in curriculum catered specifically to rural areas and the nuanced challenges associated within them (El-Amin et al., 2019).

Role of Extension

During the economic crisis in the 1980s, local Extension agents faced many community members dealing with substantial financial loss, changes in their social structure and network, and uncertainty in the future (Molgaard, 1997). People turned to Extension for support because they were viewed as a valued source of information, and people in rural areas trusted their agents during these challenging times (Molgaard, 1997). Programs were set up through the Cooperative Extension Service to develop plans to help farm families come up with strategies to cope and identify stressors through the use of counselors, both professional or peer-trained, to help farm families deal with emotional and physical stress (Thompson & McCubbin, 1987).

In the past, Extension agents have shown to be vital to connecting and engaging the farming community with the health care professional community (Guin et al., 2012). Extension provides access to education that can promote health education while decreasing the health disparities in rural communities (Fitch et al., 2013). Extension agents can serve as change agents, who communicate desired change to others in a community and, with the assistance of volunteers, can help with health education (Rogers, 2003; Wang, 1974). Training designed to improve the capacity of knowledge for Extension agents to identify and deal with mental health issues can result in agents feeling more comfortable dealing with community members, including farmers, who indicate signs of mental health issues (Hossain et al., 2010). In some communities, county Extension agents are trained similarly to community health workers and are ingrained in the local culture (Fitch et al., 2013). Individuals working in healthcare in the communities, such as doctors, might then be able to connect with county Extension agents to create a community-based partnership and help give more robust healthcare access to rural areas (Fitch et al., 2013).

Need for More Literature

 A gap exists in research related to mental health prevention and treatment programming through university Extension in the United States. A large portion of the existing literature base is associated with Australia and New Zealand research associated farmer mental health programming reacting to climate change stressors (Brew, Inder, Allen, Thomas, & Kelly, 2016) and major climatic disasters such as drought (Fuller et al., 2007; Hanigan, Schirmer, & Niyonsenga, 2018; Hossain et al, 2010). Additionally, studies examined India’s agrarian crisis from a national economic crisis in the agricultural industry; however, this research did not specifically focus on issues associated with mental health (Merriott, 2016). There is a need for additional studies on mental health in farmers and farming communities (Gregoire, 2002), as well as evaluating how farmers’ resilience positively affects their mental health in comparison to other population segments (Fraser et al., 2005; Berry et al., 2011). Research within agricultural communities related to mental health would provide a foundation for future programming, both within the context of Extension as well as outside of the extension domain (Gregoire, 2002).

Purpose and Objectives

The purpose of this research was to quantitatively understand the needs and perceptions of Georgia Extension agents about the current state of rural stress and mental health within their rural communities. By capturing the current state of rural stress within their communities, research and programming can be implemented to provide resources and initiatives to serve the communities through Extension.

Two objectives were guiding this research: (1) Identify Georgia Extension Agents’ perceptions of rural stress within their counties to evaluate agents’ comfort and need for programming related to rural stress and mental health for their communities; (2) establish baseline data for Georgia Extension to understand the current state of rural stress and mental health within individual districts.


Employing a descriptive research design to better explore Georgia Extension agents’ perceptions of farmer/rural stress, the current study utilized quantitative measures. During a mandatory, annual training, for all Georgia agents in fall 2018, the researchers collected both quantitative and qualitative data to explore the concept of rural stress/mental health in Georgia. For this study, only one scale within the survey is addressed. All agents were required to attend an all-day workshop, where the concept of rural stress/farmer mental was discussed during a 30-minute portion of the day. The agents were asked to participate in an activity and listen to a 5-minute presentation about the state’s current statistics related to rural mental health, and farming suicide rates over the last years. The survey was administered before the workshop session presented information and data to the participants. Georgia has four districts, and the agents were asked to attend the one workshop day in their district. The same workshop and speakers presented the information at all four of the workshop days. All of the workshops were held in the same week, as the presenters and researchers traveled to each of the districts.


Questions within the survey addressed agents’ perceived comfort and perceptions of current levels of stress experienced by their community members. The current study was part of a larger project. The current study was focused on a specific scale to determine factors related to rural stress/farmer mental health. The instrument was developed from an instrument used to assess farmer mental health/stress in other regions of the country from a mental health and family well-being expert. The instrument used in the current study was reviewed by a panel of experts to ensure use of relevant terms for Georgia, layout of information, and applicability of topics to Georgia communities. Within the survey, an 8-item, Likert-type scale was used to quantify agent perceptions. The items within the scale were summed and averaged to create an indexed value. The scale was also shown to be a reliable (α = .90) (Field, 2009), with “1” indicating a “low” perception and “5” indicating a “high” perception.


Out of the 312 agents in Georgia, 281 responses were collected from agent participation at the workshops for a response rate of 90%. In the Northwest district, there were 79 participants, 63 participants in the Northeast district, 70 participants in the Southwest district, and 69 participants in the Southeast district. Some participants selected not to answer all the questions; however, since this was an exploratory survey for the state, all responses were kept and analyzed.


Of the respondents, 133 (46.7%) classified themselves as Agricultural and Natural Resource agents, 97 (34.2%) classified themselves as 4-H agents, 40 classified themselves as Family and Consumer Sciences agents (14.1%), 8 (2.9%) classified themselves as other, and 6 (2.1%) chose not to respond.

Within the survey, agents were asked to assess the current level of stress/difficulty for farm and farm-related operations in their county. Overall, on a 5-point scale with “1” indicating “low” and “5” indicating “high”, agents perceived the current level of stress in their community to be slightly above “moderate” (M = 3.54, n = 271) (Table 1). Examining this question by district, the Northwest district had a mean of 3.32 (n = 75) or “moderate” and the Northeast district had a mean of 3.58 (n = 59). The Southwest district indicated the level of stress to be 3.74 (n = 68) and the Southeast district indicated a level of stress at 3.54 (n = 69).

Table 1
Level of Perceived Stress by District and Georgia

To further understand specific factors that may be associated with the current level of stress or difficulty for farms in their districts, agents were asked eight questions about specific elements in farming that may contribute to stress level (Table 2).

Table 2
Extension Agents' Perceptions of Factors Related to Rural/Farm Stress
concern for weather-related issues83.02710.17026.18832.87528.0
concern to make ends meet62.2259.36825.49736.27226.9
concern for cash-flow in the operation93.5186.99034.78633.25621.6
concern to get needed financing to continue93.4259.69536.48331.84918.8
concern for market and trade issues93.4238.89837.59034.54115.7
concern for crop/livestock prices114.2166.19436.010038.34015.3
concern for mental health/suicide risk249.16223.511142.05018.9176.4


The survey provided a snapshot of Georgia Extension agents’ perceptions of their communities’ current need with regard to rural stress/farmer mental health. The data indicated most districts to be in similar positions with rural stress. Of an important note, the Southwest and Southeast districts did report more distressing levels of concern about rural stress. In utilizing these findings, it is important to note the timeline of the survey data collection. This survey data was collected within two weeks after Hurricane Michael caused great destruction to the agricultural industry in South Georgia. Therefore, these findings should be cautiously viewed with that external factor in mind. Additionally, specific factors that impact rural stress (weather issues, market prices, etc.) were similar throughout the state, with the Northwest district indicating a lower level of concern, while the Southeast and Southwest reported slightly higher levels of concern. While this data provides a baseline for Georgia Extension agents’ perceptions, the findings from this research cannot be applied to any other populations or locations; however, it can give a starting point for providing valuable resources to the communities.

Based upon the objectives for this research, the following recommendations were developed to assist in future work and research around rural stress/farmer mental health for Georgia. From an applied perspective, future discussions and trainings should be developed to help Georgia Extension agents identify resources for rural stress/mental health and how to effectively share that information with their community members. This coincides with previous research to recognize the unique needs and qualities of each community, specifically as they relate to existing cultures, which be accounted for when engaging them in mental health/stress efforts (Gregoire, 2002; Naik, 2017). Additionally, engaging other populations (farmers, community opinion leaders, faith-based leaders, etc.) to evaluate their perceptions of rural stress/mental health in the community may contribute to fully understanding the scope of stress/mental health in the community.

Future research should examine more in-depth Extension agents’ preparedness and comfort level in addressing rural stress/mental health in their communities. By collecting more in-depth demographic information, more specific conclusions about unique qualities, characteristics, and experiences that better position Extension agents to address rural stress/mental health in their communities could be developed. This could lead to creating more specialized training and resources targeted toward specific community and agent needs and mentorship programs. Additionally, future research should work with community members to identify the most appropriate form of communicating information about rural stress/mental health with specific audiences. The current research indicates a one-size-fits-all approach may not be beneficial for all areas, even in the same state.

The reality of suicide rates, even if only focused on the U.S. in this research context, warrants the need to consider this issue in other countries, specifically as it relates to U.S.-based researchers and specialists immersing themselves into local issues and cultural dynamics.


Centers for Disease Control and Prevention. (2019, January 28a).

Centers for Disease Control and Prevention. (2019, January 28b). Drug overdose in rural America.

Centers for Disease Control and Prevention. (2019, January 28c). Leading causes of death in rural America.

Centers for Disease Control and Prevention. (2019, January 28d). Suicide in rural America.

America’s Health Rankings. (2019). Georgia Annual Report. Retrieved from

Anderson, B. T., Johnson, G. J., Wheat, J. R., Wofford, A. S., Wiggins, O. S., & Downey, L. H. (2012). Farmers’ concerns: A qualitative assessment to plan rural medical education. The Journal of Rural Health, 28(2), 115-121.

Berry, H. L., Hogan, A., Owen, J., Rickwood, D., & Frager, L. (2011). Climate change and farmers’ mental health: Risks and responses. Asia-Pacific Journal of Public Health, 23(2), 1195-1325.

Brew, B., Inder, K., Allen, J., Thomas, M., & Kelly, B. (2016).  The health and wellbeing of Australian farmers: a longitudinal cohort study. Public Health, 16(988).

Clay, R. A. (2014). Reducing rural suicide. American Psychological Association, 45(4), 36. Retrieved from

El-Amin, T., Anderson, B. L., Leider, J. P., Sartorius, J., & Knudson, A. (2018). Enhancing mental health literacy in rural America: Growth of mental health first aid program in rural communities in the United States from 2008-2016.  Journal of Rural Mental Health, 42(1), 20-31. 

Field, A. (2009). Discovering statistics using SPSS. SAGE Publications Inc.

Fitch, D., Donato, L., & Strawder, P. (2013). Extending the University into the community to address healthcare disparities. West Virginia Medical Journal, 109(4), 72-75. Retrieved from

Franz, N. K., & Townson, L. (2008). The nature of complex organizations: The case of Cooperative Extension. New Directions for Evaluation, 2008(120), 5–14.

Fraser, C. E., Judd, S. F., Humphreys, J. S., Frager, L. J., & Henderson, A. (2005). Farming and mental health problems and mental illness.  International Journal of Social Psychiatry, 51(4), 340-349.

Guin, S. M., Wheat, J. R., Allinder, R. S., Fanucchi, G. J., Wiggins, O. S. & Johnson, G. J. (2012) Participatory research and service-learning among farmers, health professional students, and experts: An agromedicine approach to farm safety and health.  Journal of Agromedicine, 17(1), 22-29.

Hanigan, I. C., Schirmer, J., & Niyonsenga, T. (2018). Drought and distress in Southeastern Australia. EcoHealth, (15)3, 642-655.

Hasbrouck, M. A., & Waddimba, A. C. (2017).  The work-related stressors and coping strategies of group-employed rural health care practitioners: A qualitative study. American Journal of Industrial Medicine, 60, 867-878.

Hossain, D., Gorman, D., Eley, R., & Coutts, J. (2010). Advisory and extension agents in supporting farmers in rural Queensland. Rural and Remote Health, 10(1593). Retrieved from

Kane, S. P. (2019). 2019 Ag snapshots. Retrieved from

Kilpatrick, S. Willis, K., Johns, S., & Peek, K. (2012).  Supporting farmer and fisher health and wellbeing in ‘difficult times’: Communities of place and industry associations.  Rural Society, 22(1), 31-44.

Kolstrup, C. L., Kallioniemi, M., Lundqvist, P., Kymäläinen, H. R., Stallones, L., & Brumby, S. (2013).  International perspectives on psychosocial working conditions, mental health, and stress of dairy farm operators. Journal of Agromedicine, 18(3), 244-255.

Lasley, P. (1986).  Farm crisis response: Extension and research activities in the North Central region. Retrieved from

Molgaard, V. K. (1997).  The extension service as key mechanism for research and services delivery for prevention of mental health disorders in rural areas.  American Journal of Community Psychology, 25(4), 515-544.  Retrieved from

Naik, A. (2017).  In search of farmer well-being.  International Journal of Agricultural Management, 6(1), 1-3.

Polain, J. D., Berry, H. L., & Hoskin, J. O. (2011). Rapid change, climate adversity and the next ‘big dry’: Older farmers’ mental health. The Australian Journal of Rural Health, 19, 239-243.

Ratcliffe, M., Burd, C., Holder, K., & Fields, A. (2016). Defining Rural at the U.S. Census Bureau. United States Census Bureau. Retrieved from

Roy, P., Tremblay, G., & Robertson, S. (2014). Help-seeking among male farmers: Connecting masculinities and mental health. Socioligia Ruralis, 54(4), 460-476. https://doi:10.1111/sonu.12045

Rural Health Information Hub. (2019). Retrieved from

Thompson, E. L., & McCubbin, H. I. (1987).  Farm families in crisis: An overview of resources. Resource Review Essay, 36(4), 461-467.

University of Georgia Extension. (n.d.) Our Programs. Retrieved January 28, 2019, from

Vanderbroom, C. P., & Madigan, E. A. (2007). Federal definitions of rurality and the impact on nursing research. Research in Nursing and Health, 30. 175-184.

Wang, V. L. (1974).  Using cooperative extension programs for health education.  American Journal of Public Health, 64(2), 107-111.

Bats and Beyond: Communicating Wildlife and Climate Change Empathy to Youth through an Electronic Field Trip

Peyton N. Beattie, University of Florida,

Kevin W. Kent, University of Florida,

Teresa E. Suits, University of Florida,

Jamie L. Loizzo, University of Florida,

J. C. Bunch, University of Florida,

PDF Available


Today’s youth must take decisive action to maintain and improve the world by being environmentally literate through understanding, interpreting, and applying information and media about the environment and human interactions. Ample room exists for science communicators and educators to work together to develop real-world programs for connecting youth audiences with scientists and science research to impact environmental perceptions, attitudes, and learning. Universities, Cooperative Extension, museums, and schools can use electronic field trips (EFTs) as a vehicle to connect youth audiences to scientists globally. Sixty-four classrooms in the United States and Trinidad and Tobago participated in an EFT at the University of Florida Bat Houses with three mammalogists from the Florida Museum of Natural History. The study examined students’ attitudes toward wildlife and climate change through pre/post-surveys, before and after participating in the EFT.


Environmental communication leverages media with writing and imagery as a vehicle to educate, inform, and sometimes even persuade audiences about the environment and human relationships with the natural world. Today’s youth must take decisive action to maintain and improve the world by being environmentally literate through understanding, interpreting, and applying information and media about the environment and human interactions (Roth, 1992). Environmental literacy has an established history and is often defined as a combination of scientific knowledge, problem-solving, and critical thinking for fostering and growing positive environmental behaviors and attitudes (Cole, 2007; Roth, 1992). Increasing opportunities exist for environmental communicators and educators to develop innovative programs for addressing environmental engagement and literacy in youth audiences. The American Association for the Advancement of Science’s (AAAS; 2009) Project 2061 includes benchmarks such as The Living Environment and The Nature of Science with specific recommendations for introducing PK-12 youth to environmental concepts, as well as how scientists work to investigate problems and find solutions. The Next Generation Science Standards (NGSS; 2013) focus on cross-cutting concepts such as ecosystems, genetics, and global climate change. The National Research Council (NRC; 2000) suggested connecting PK-12 classrooms with the broader community to expand youth engagement and learning with a variety of concepts. Therefore, ample room exists for science communicators and educators to work together to develop real-world programs for connecting youth with scientists and science research to impact environmental perceptions, attitudes, and learning.

Literature Review

The following literature review including sub-sections about EFTs, wildlife empathy, and climate change attitudes informed the study design.

Electronic Field Trips (EFTs)

Communication and education programs can introduce youth to environmental science concepts, careers, and locations during a typical school day via online interactive technologies (Cassady et al., 2008; Garner, 2004; Tuthill & Klemm, 2002). Ideally, children should spend direct, in-person time outdoors for many physical and mental benefits, and learning about the world around them (Pate et al., 2011). While it is still highly recommended children directly engage with the environment for deep learning, in-person field trips are often not possible for schools. The cost and logistics to plan and implement traditional field trips have reduced the amount of time teachers can take students off school grounds to unique locations for studying the environment (Tuthill & Klemm, 2002). Instead, educators can leverage EFTs hosted from natural outdoor locations. EFTs can be streamed into classrooms to communicate and raise awareness about environmental issues, related STEM research, and careers (Loizzo et al., 2019).

EFTs (also sometimes referred to as virtual field trips) involve computer mediated-communication between a host location and a student site (Loizzo et al., 2019; Loizzo & Beattie, In Press). The programs can be offered in real-time (i.e., synchronous) via live, interactive web-streamed video or self-paced (i.e., asynchronous) via pre-recorded videos, websites, or virtual reality. Science communicators, educators, and scientists have developed and implemented various topics, formats, and technologies for EFTs. For instance, the Purdue zipTrips program included an EFT about animals, diseases, and genes for middle school students. The 45-minute zipTrips had an in-studio and off-site audience, live interactions with scientists, pre-recorded segments, and integrated activities. The EFTs positively impacted middle schoolers’ perceptions of scientists and their research (Adedokum et al., 2011a; Adedokun et al., 2011b). The EFT examined in this study, called Bats and Beyond, followed a live, interactive web-streamed video format from a university field site with scientists to youth in PK-12 classrooms throughout the United States and Trinidad and Tobago. The EFT included scientists presenting their wildlife and climate change research in real-time, as well as pre-recorded images and footage of bats and international research sites.

Wildlife Empathy

Youth empathy toward environmental issues and ecosystems via an introduction to animal models can be achieved through EFTs. Research has shown visitors to informal learning spaces such as zoos and aquariums often hold pre-existing, high empathy for wildlife (Young et al., 2018). Thus, there is a need to reach youth who may not necessarily have access to or typically visit settings to learn about animals. Young et al. (2018) defined empathy as, “…a stimulated emotional state that relies on the ability to perceive, understand and care about the experiences or perspectives of another person or animal” (p. 329). It is imperative that environmental communication and education programs depicting human-animal interactions keep empathy in mind, when developing key messages and images.

Kellert (1984) found the most frequent attitude type children hold toward animals is ‘humanistic’ – meaning they have strong affection toward animals, mostly pets. Prior research showed introducing youth to animals can positively impact their awareness, attitudes, and empathy toward bigger picture environmental issues such as endangered species and climate change (Morgan & Gramann, 1989). Morgan and Gramann (1989) found that when a wildlife expert demonstrated engagement with snakes, such as holding a snake for others to see and touch, people often became vicariously less fearful of the animals. Wagler and Wagler (2014) found educational activities which included live endangered spiders reduced children’s (i.e., ages 10-11) fear and disgust of spiders. Hence, animal models have the potential to influence attitudes toward animals and increase human concern for how changes in the environment are impacting animals.

Climate Change Attitudes

According to the Intergovernmental Panel on Climate Change (IPCC, 2014), the current warming trend beginning in the mid-20th Century can be attributed to human activity with a probability greater than 95%. Via the Cloud and Land Elevation Satellite (ICESat) and the NASA/German Aerospace Center Gravity Recovery and Climate Experiment (GRACE), NASA (2018) recorded a .12-inch increase in sea levels since 2012, with recorded ice losses of 241.4 billion tons per year in Antarctica. 

Children learn about climate change inside and outside of the classroom. Researchers have found that school-based interventions can increase climate change knowledge for children in Bangladesh (Kabir et al., 2015). The National Education Association (Flannery, 2017) advised teachers to use accurate data, local stories, cross-curricular connections, and inspiration for climate change education. Outside of the classroom, informal science learning centers (ISLCs) like zoos, museums, or libraries serve as a “safe” neutral space for visitors to learn about environmental issues and develop attitudes (Clayton et al., 2009). Humans have developed attitudes, perceptions, and beliefs about climate change. Affective (i.e., emotional), beliefs (i.e., values), cognitive (i.e., knowledge), and behavioral intentions are used to explain environmental attitudes (Christensen & Knezek, 2015). Research has shown young people form attitudes on climate change through their education and can be influenced by teacher figures. Middle school children in North Carolina reflected on their teacher’s beliefs that climate change is happening, but they formed their own opinions about climate change, which differed from their teacher’s (Stevenson et al., 2016). One study found science teachers’ teaching styles differed on climate change, based on their political ideologies (Plutzer & Lee, 2018).

Conceptual Frameworks

Social Cognitive Theory (SCT; Bandura, 2009) and the Theory of Planned Behavior (Ajzen, 1991) were conceptually used to guide this study.

Social Cognitive Theory

SCT guided this study to inform how the vicarious learning environment (Bandura, 2009) of an EFT impacted students’ attitudes toward wildlife and climate change. Additionally, the researchers examined how the participating teachers perceived the students’ attitudes toward wildlife and climate change within the vicarious learning program. The environment in which a person lives and the behaviors they see modeled is how their learning is constructed, rather than construction through individual autonomy (Bandura, 1999, 2009). Therefore, a person can witness an event and use “cognitive, vicarious, self-regulatory, and self-reflective” processes to determine how that event will shape their knowledge and behavior (Bandura, 2009, p. 95). Within this study’s EFT program context, researchers used a pre-/post-survey to investigate how the students used cognitive, vicarious, self-regulatory, and self-reflective processes to adjust their attitudes regarding wildlife and climate change.

The EFT developers intentionally designed and viewed Bats and Beyond as a vicarious learning experience. The EFT featured scientist role models, photos, and video content that could impact participants’ understanding of the specific content within the program, including bats, mammalogy and specimen curation, and climate change through a segment dedicated to each of these content areas. In turn, through vicariously viewing the role models and visual content, the EFT aimed to influence youths’ wildlife empathy and climate change attitudes.

Theory of Planned Behavior

The Theory of Planned Behavior (TPB; Ajzen, 1991) also conceptually served as a lens for researchers to understand students’ behavioral beliefs regarding wildlife empathy and climate change through their attitude changes before and after the EFT. TPB aimed to predict a person’s behavior based on their intentions and perceived behavioral control (Ajzen, 1991). Intent to perform an action is determined by a person’s internal will to want to complete such behavior. Still, that intent can also be limited by factors outside the person’s control, including money, time, or skills (Ajzen, 1991). Considering a person’s intrinsic motivation to perform an action coupled with a person’s actual ability to perform the behavior leads to actual control of the behavior (Ajzen, 1991). Ajzen (1991) noted, “perceived behavior control, together with behavioral intention, can be used directly to predict behavioral achievement” (p. 184).

The EFT developers and scientist role models intentionally included visual and audio content discussing human impacts on wildlife/bats and the environment. Specifically, developers and scientist role models aimed to use the example of how sea level rise and climate change has impacted bats’ habitats and migration patterns to impact youths’ understanding of climate change and intentions to participate in conservation behaviors for improving the environment.

Purpose and Research Questions

The purpose of the current study was to investigate how an environmentally-focused electronic field trip (EFT) program produced by agricultural communication, leadership, and education graduate students impacted participating youth’s wildlife empathy and attitudes toward climate change. Further, the investigation also sought to describe students’ attitudes toward climate change and wildlife empathy. More specially, the following research questions guided this research study:

  • What impact did participation in the EFT have on students’ attitudes toward wildlife?
  • What impact did participation in the EFT have on students’ attitudes toward climate change?
  • What were the teachers’ perceptions of the impact EFTs had on students’ attitudes toward wildlife and climate change? 

The study aligns with multiple topics outlined in the American Association of Agricultural Education (AAAE) Research Agenda. The EFT content and research focus on youth’s climate change and wildlife attitudes addressed complex problems occurring in the world, research priority area seven of the AAAE Research Agenda (Andenoro et al., 2016). Additionally, the study implemented new technologies, practices, and products to lead change in youth’s attitudes about wildlife and climate change, which is a focus of research priority area two of the agenda (Lindner et al., 2016).


EFT Context

Graduate students at the University of Florida developed, implemented, and assessed the Bats and Beyond EFT program, as part of the fourth author’s Information and Communication Technologies course. The eight graduate students enrolled in the course designed and delivered the EFT with the assistance of three scientists. The EFT was live and web-streamed at the bat houses on the University of Florida campus on November 15, 2018. Two sessions were offered at 2:00 p.m. and 4:00 p.m. EST. The EFT was titled Bats and Beyond to reflect the focus of the program’s content. Three segments were included in the live webcast: (a) an overview of bats, bat houses, and mammalogy careers; (b) an inside look at the university museum’s bat collections; and (c) the mammalogists’ bat genetics research conducted in The Bahamas. As such, three mammalogists from Florida Museum of Natural History volunteered to assist with the EFT. The graduate student course participants included seven master’s and doctoral students specializing in agricultural education, communication, leadership, and Extension and one agricultural and biological engineering doctoral student. 

The Institutional Review Board for Human Subjects Research at the University of Florida approved this study. Teachers were recruited to participate in the EFT through various outlets, including (a) direct email invitation to museum teacher contact lists and agriculture teachers in Florida, (b) word of mouth via students’ personal education contacts, (c) Streaming Science social media, and (d) direct email through Extension offices in Florida. Teachers registered for the EFT via the registration form and indicated their interest in participating in this research. Approved opt-out consent forms were sent home to parents to inform them of their child’s participation in the EFT and anonymous research. Parents who did not wish for the child to participate had the option to sign and return the forms to school. However, no parents opted for their child to not participate.

Study Design

Researchers conducted this study using a survey design approach. Researchers analyzed a sample (i.e., two schools – School A and School B) of the population (i.e., 64 schools participating in the EFT) to understand both tangibles (i.e., demographic data) and intangibles (i.e., student and teacher attitudes; Ary, Jacobs, Sorrensen, & Walker, 2014; Creswell & Creswell, 2018). The student and teacher surveys for this study were developed using Qualtrics, an online survey platform, and disseminated to the teachers who signed up to participate in the EFT via the Qualtrics link provided through email. Teachers distributed the pre- and post-surveys to the students. The population identified for this study was all schools (n = 64) who participated in the Bats and Beyond EFT. The sample for this specific study were the two schools who were classified as the intended audience for the EFT (i.e., middle and high schools) and were the two schools who completed all three surveys disseminated (i.e, the pre-survey, post-survey, and teacher survey). Thus, purposive sampling techniques were used to determine the sample for this study (Ary et al., 2014).

Participant Demographics

There were approximately 330 students from 64 elementary, middle, and high schools who participated in the live EFT webcasts. The two schools examined for this specific study were School A, and School B. School A is located on the eastern coast of central Florida. There were 738 students enrolled in School A in the 2018-2019 school year in grades 9-12 (Public School Review, n.d.). Seventy-five percent of students enrolled in School A were white, 10% of students were Hispanic, 7% of students classified themselves as two are more races, 6% of students were Asian, and 1% of students were black (, n.d.). The school-wide gender breakdown for School A was 50% female and 50% male (, n.d.). School B is located in north-central Florida. There were 948 students enrolled in School B for the 2018-2019 school year in grades 7-12 (Public School Review, n.d.). Seventy-four percent of students enrolled in School B were white, 13% of students were Hispanic, 10% were black, and 3% classified themselves as two or more races (, n.d.). Students’ gender breakdown at School B was 48% female and 52% male (, n.d.). For this study, researchers analyzed School A & B, grades 8-12. Specific demographic information regarding the student participant sample are outlined in Table 1. There was one teacher from School A and two teachers from School B for three teachers included in this study.

Table 1
Demographics of Student Participants


Student Survey

Students participating in the EFT were asked to participate in pre- and post-surveys. The student survey measured students’ level of agreement or disagreement with 19 statements on a 7-point, Likert-type scale. Of the 19 total statements, 16 were analyzed to address the objectives of this study. Ten of the statements regarded wildlife empathy, and six statements regarded climate change. The remaining three statements were outside the scope of this study and were not analyzed. Some of the climate change statements used for this study were adapted from a climate change attitude instrument developed by Christensen and Knezek (2015). Individual item means and standard deviations are reported, thus reliability estimates were not computed because constructs were not developed. Validity is important to quantitative research to ensure the study is accurately measuring what was intended (Ary et al., 2014). Face and content validity of the instruments was determined by graduate students who were heavily involved in the production of the EFT. Face validity was determined by ensuring the questions on the instrument were relevant to the purpose and objectives of the EFT. 

Teacher Survey

Teachers of the participating EFT classrooms were invited to participate in a post-survey. Forty-four statements were included in the teacher survey for the teachers to rate their level of agreement or disagreement. Seven of the 44 statements were used in the analysis of this study because of their direct relation to the students’ attitudes toward wildlife and climate change. This study measured teachers’ level of agreement or disagreement using seven items on a 7-point, Likert-type scale. 

Data Analysis

All objectives of the study were analyzed using descriptive statistics. Individual mean scores and standard deviations were reported for each of the statements included in this study. The original values for the Likert scales were recoded to reflect a 1 to equal strongly disagree and a 7 to equal strongly agree, as the inverse scale was used in the survey. The values were re-coded to increase understanding of the results presented below. The following real limits of the scale were used to interpret the recoded mean scores and standard deviations: 1.00 – 1.49 = strongly disagree, 1.50 – 2.49 = disagree, 2.50 – 3.49 = somewhat disagree, 3.50 – 4.49 = neither agree nor disagree, 4.50 – 5.49 = somewhat agree, 5.50 – 6.49 = agree, 6.50 – 7.00 = strongly agree


A limitation of the pre-survey and post-survey design used in this study is the duration between the pre and post-survey not being adequate time to demonstrate larger mean differences regarding students’ attitude change. The nature of this exploratory study and the small sample size does not allow for the results and conclusions of this study to be generalized to everyone who participated in the EFT but rather only explains what occurred in students’ attitudes and perceptions of the teachers from School A and School B.


RQ1. What impact did participation in the EFT have on student’s attitudes towards wildlife?

Students’ highest mean score regarding their attitude toward wildlife for both the pre-survey (M = 5.05, SD = 1.39) and post-survey (M = 5.32, SD = 1.17) was it is important that scientists study bats (see Table 2). The more negative attitude, indicated by the lowest mean score, students reported for both the pre-survey (M = 3.27, SD = 1.54) and post-survey (M = 3.27, SD = 1.63) was related to bats are pests.

Table 2
Student Attitudes toward Wildlife Before and After the EFT
Note. Real limits of the scale: 1.00 – 1.49 = strongly disagree, 1.50 – 2.49 = disagree, 2.50 – 3.49 = somewhat disagree, 3.50 – 4.49 = neither agree nor disagree, 4.50 – 5.49 = somewhat agree, 5.50 – 6.49 = agree, 6.50 – 7.00 = strongly agree

RQ2. What impact did participation in the EFT have on student’s attitudes toward climate change?

The students reported a more positive attitude regarding the item bats are important to our environment for both the pre-survey (M = 5.39, SD = 1.59) and the post-survey (M = 5.58, SD = 1.17; see Table 3). A lower mean score regarding I believe human activity does not cause climate change was reported by the students for both the pre-survey (M = 3.30, SD = 1.81) and post-survey (M = 3.29, SD = 1.77).

Table 3
Student Attitudes toward Climate Change Before and After the EFT
Real limits of the scale: 1.00 – 1.49 = strongly disagree, 1.50 – 2.49 = disagree, 2.50 – 3.49 = somewhat disagree, 3.50 – 4.49 = neither agree nor disagree, 4.50 – 5.49 = somewhat agree, 5.50 – 6.49 = agree, 6.50 – 7.00 = strongly agree

RQ3. What did the teachers perceive the impact of participating in the EFT had on students’ attitudes? 

The teachers’ perceptions regarding the students’ attitudes was the most positive related to I encourage wildlife empathy in my class/program (M = 6.67, SD = .58; see Table 4). The lowest mean score reported by the teachers was regarding my students are concerned about bats (M = 4.33, SD = .58).

Table 4
Teacher Perceptions of Students' Attitudes toward Wildlife and Climate Change (n=3)
Real limits of the scale: 1.00 – 1.49 = strongly disagree, 1.50 – 2.49 = disagree, 2.50 – 3.49 = somewhat disagree, 3.50 – 4.49 = neither agree nor disagree, 4.50 – 5.49 = somewhat agree, 5.50 – 6.49 = agree, 6.50 – 7.00 = strongly agree

Conclusions and Discussion

Wildlife Attitudes

The students maintained a consistent attitude about wildlife before and after the EFT and indicated they somewhat agreed, neither agreed nor disagreed, or somewhat disagreed with nine of the ten statements regarding wildlife empathy. The students’ attitudes towards bats are pests were consistently the most negative and indicated they somewhat disagreed with the statement before and after the EFT. 

The participating students’ attitudes regarding the statement bats are cute moved in a negative direction according to the real limits of the scale between pre-survey and post-survey. The research team discussed this finding and attributed this negative direction in attitude to the pictures of bats and pinned bats exhibited throughout the EFT program. The EFT program exposed the students to what “real” bats look like, whereas students may have only had idealized images of bats in their minds before the EFT program. Some of the bat images the students viewed throughout the EFT are presented in Figure 1.

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Figure 1. A picture of a live, real bat taken by one of the participating mammalogists and a picture of a pinned bat collection taken by a graduate student shown to students throughout the EFT program.

Teachers somewhat agreed their students knew about bats before the EFT. However, the teachers neither agreed nor disagreed that their students were concerned about bats. The teachers strongly agreed wildlife empathy is encouraged in their class/program, and they discuss climate change concepts in their class/program. The students and teachers who participated in the Bats and Beyond EFT neither agreed nor disagreed the students were concerned about bats. Kellert (1984) discussed programs could not solely entertain with animals to have an impact. The findings from this study and Kellert’s (1984) statement indicates a need for children to have prolonged interactions with animals and the environment for deeper learning. Young et al. (2018) confirmed that empathy is a complicated construct, which takes time and multiple interactions to grow.

Climate Change Attitudes

The students’ attitudes toward climate change remained consistent before and after the EFT. The students’ attitudes toward the presented climate change statements ranged from somewhat disagree, neither agree nor disagree, or somewhat agree. The students had the most negative attitude regarding I believe human activity does not cause climate change and indicated they somewhat disagree before and after the EFT.  Teachers somewhat agreed their students are concerned about climate change, and the teachers indicated they discuss climate change in their class/program.

The results of the study indicated students who participated in the Bats and Beyond EFT from School A and School B agreed that bats are essential to the environment after the EFT. Similarly, the teachers who participated in the EFT from School A and School B perceived the EFT increased their students’ understanding of the role bats play in the ecosystem. Understanding the importance of an animal (i.e., bats) to the environment supports Thompson and Gullone’s (2003) research, which reported a positive and statistically significant relationship between humans and how they treat animals and, ultimately, the environment. Additionally, this finding aligns with Wagler and Wagler’s (2014) research that specified animal models positively influence attitudes towards animals and increase concern for how changes in the environment impact animals. The implementation of EFTs has repeatably shown positive impacts for youth audiences. EFTs are important to the agricultural communication field because of its unique position to engage with youth audiences in a dialogue around complex agricultural and natural resources issues rather than just developing content to be passively received by youth audiences. However, Streaming Science’s EFTs are in their infancy, and the team has established a number of recommendations for practice and research to ensure EFTs are continuously increasing impacts with youth audiences around agricultural and natural resources topics. 



Future iterations of the EFT experience could better utilize the question and answer portions of the program to not only respond to questions from youth, but to also ask youth to respond to evaluation-type questions in real-time, such as (a) What do you think is the most important finding of this research?, (b) How do you feel about bats?, and  (c) What are some ideas you have for protecting the environment? In-depth analysis of supplemental materials teachers use in the classroom before and/or after the EFT could be conducted to understand the impact of supplemental materials on student learning outcomes. Researchers could also observe a classroom participating in the EFT and observe the same classroom not participating in the EFT to understand how teachers interact, discuss, and teach climate change and wildlife empathy, as compared to the scientists. There are opportunities to determine if EFTs impact other audiences besides youth (i.e., adults) and impactful with different types of content (i.e., developing complex solutions to complex problems rather than just engaging in dialogue around complex problems). Lastly, can an EFT be developed and hosted that has larger impacts on its audience than previously determined. More substantial impacts include concepts other than attitude, knowledge, and opinions and are geared more toward behavior change and developing complex solutions.


There is a potential for EFTs to raise awareness and support factual learning (Adedokun et al., 2011a; Adedokun et al., 2011b; Cassady et al., 2008; Stoddard, 2009). However, to impact student attitudes toward wildlife, science communicators and educators should provide supplemental materials or wrap-around experiences to prolong engagement with wildlife and reinforce learning and attitudes. This supplemental material and wrap-around experiences provide an opportunity for Streaming Science to expand their EFT programs by developing such materials or experiences for teachers to use with students. The additional materials should accompany the lessons to ensure that students are interacting with the material for wildlife empathy to be achieved (Young et al., 2018). Additionally, Streaming Science could develop an online community of practice for participating teachers to share content or lessons they have created and taught to add to teachers’ collections of resources. A community of practice for teachers participating in EFTs could open up a dialogue about STEM issues, communication and education strategies, build relationships, and answer questions regarding EFTs for implementation support and best practices.


Andenoro, A. C., Baker, M., Stedman, N. L. P., & Weeks, P. P. (2016). Research priority 7: Addressing complex problems. In T. G. Roberts, A. Harder, & M. T. Brashears (Eds.), American Association for Agricultural Education national research agenda: 2016-2020. Department of Agricultural Education and Communication.

Adedokun, O. A., Parker, L. C., Loizzo, J., Burgess, W. D., & Robinson, J. P. (2011a). Factors influencing participant perceptions of program impact: Lessons from a virtual fieldtrip for middle-school students. Journal of Extension, 49(6).

Adedokun, O., Parker, L. C., Loizzo, J., Burgess, W., & Robinson, J. P. (2011b). A field trip without buses: Connecting your students to scientists through a virtual visit. Science Scope, 34(9), 52-57.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

American Association for the Advancement of Science [AAAS]. (2009). Project 2061: Benchmarks for science literacy.

Ary, D., Jacobs, L. C., Sorensen, C., & Walker, D. A. (2014). Introduction to research in education (9th ed.). Wadsworth Cengage Learning.

Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology, 2, 21–41.

Bandura, A. (2009). Social cognitive theory of mass communication. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed.).

Cassady, J.C., Kozlowski, A. & Kornmann, M. (2008). Electronic field trips as interactive learning events: Promoting student learning at a distance. Journal of Interactive Learning Research, 19(3), 439-454.

Clayton, S., Fraser, J., & Saunders, C. D. (2009). Zoo experiences: Conversations, connections, and concern for animals. Zoo Biology, 28, 377-397.

Christensen, R. & Knezek, G. (2015). The climate change attitude survey: Measuring middle school student beliefs and intentions to enact positive environmental change. International Journal of Environmental & Science Education, 10(5), 773-788. 0.12973/ijese.2015.276a

Cole, A.G. (2007). Expanding the field: Revisiting environmental education principles through multidisciplinary frameworks. Journal of Environmental Education, 38(2), 35-44. 0.3200/JOEE.38.1.35-46

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.

Flannery, M. E. (2017). 5 ways to teach about climate change in your classroom. News and Features from the National Education Association.

Garner, L. C. (2004). Field trips and their effect on student achievement in and attitudes toward science: A comparison of a physical versus virtual field trip to the Indian River Lagoon [Doctoral dissertation, Florida Institute of Technology]. Proquest. (n.d.). Find a great school.

Intergovernmental Panel on Climate Change (IPCC). (2014). Climate change 2014 synthesis report summary for policymakers. Retrieved from

Kabir, M. I., Rahman, M. B., Smith, W., Lusha, M. A., & Milton, A. H. (2015). Child centered approach to climate change and health adaptation through schools in Bangladesh: A cluster randomized intervention trial. PLOS One, 10(8),

Kellert, S. R. (1984). Attitudes toward animals: Age-related development among children. In M.W. Fox & L.D. Mickley (Eds.) Advances in animal welfare science. The Humane Society of the United States.

Lindner, J. R., Rodriguez, M. T., Strong, R., Jones, D., & Layfield, D. (2016). Research priority 2: New technologies, practices, and products adoption decisions. In T. G. Roberts, A. Harder, & M. T. Brashears (Eds.), American Association for Agricultural Education national research agenda: 2016-2020. Department of Agricultural Education and Communication.

Loizzo, J. L. & Beattie, P. N. (In Press). Self-study of a project-based graduate science communication course focused on electronic field trip development. North American College Teachers Association Journal.

Loizzo, J. L., Harner, M. J., Weitzenkamp, D. J., & Kent, K. (2019). Electronic field trips for science engagement: The Streaming Science Model. Journal of Applied Communications (103)4.

Morgan, J., & Gramann, J. (1989). Predicting effectiveness of wildlife education programs: A study of students’ attitudes and knowledge toward snakes. Wildlife Society Bulletin,17(4), 501-509.

National Research Council (NRC)(2000). How people learn: Brain, mind, experience, and school: Expanded edition. The National Academies Press.

NGSS Lead States. (2013). Next generation science standards: For states, By states. The National Academies Press.

Pate, R. R., Mitchell, J. A., Byun, W., & Dowda, M. (2011). Sedentary behaviour in youth. British Journal of Sports Medicine, 45(11), 906-913.

Plutzer, E., & Lee Hannah, A. (2018). Teaching climate change in middle schools and high schools: Investigating STEM education’s deficit model. Climatic Change, 149(3-4), 305-317.

Public School Review. (n.d.). Welcome to public school review. Retrieved from

Roth, C. (1992).  Environmental literacy: Its roots, evolution and directions in the 1990s. ERIC Clearinghouse for Science, Mathematics, and Environmental Education.

Stevenson, K. T., Peterson M. N., Bradshaw A. (2016). How climate change beliefs among U.S. teachers do and do not translate to students. PLOS One, 11(9).

Stoddard, J. (2009). Toward a virtual field trip model for the social studies. Contemporary Issues in Technology and Teacher Education, 9(4), 412-438.

Thompson, K. L. ., & Gullone, E. (2003). Promotion of empathy and prosocial behaviour in children through humane education. Australian Psychologist, 38(3), 175–182.

Tuthill, G., & Klemm, E. B. (2002). Virtual field trips: Alternatives to actual field trips. International Journal of Instructional Media, 29(4), 453-468.

Wagler, A. & Wagler, R. (2014). Arthropods and the current great mass extinction: Effective themes to decrease arthropod fear and disgust and increase positive environmental beliefs in children? International Journal of Environmental and Science Education, 9, 197-214. 10.12973/ijese.2014.211a

Young, A., Khalil, K. A., & Wharton, J. (2018). Empathy for animals: A review of the existing literature. Curator, 61(2), 327–343.

An Examination of Organizational Citizenship Behavior Characteristics Amongst Undergraduate Students

Kevan W. Lamm, University of Georgia,

Alyssa Powell, University of Georgia,

Alexa J. Lamm, University of Georgia,

Eric D. Rubenstein,

PDF Available


In addition to creating engaging learning environments agricultural educators must also focus on preparing the next generation to enter the workforce. The purpose of this study was to examine a potential entry point for both responsibilities. Specifically, this study focused on the organizational citizenship behavior (OCB) characteristics of undergraduate students enrolled in an agricultural education leadership or communication course. Within the literature it is well established that OCBs are related to positive organizational outcomes such as: higher levels of performance and reduced turnover intention. However, there is little research establishing the general OCB characteristics of undergraduate students enrolled in agricultural education leadership or communication courses. Of the five OCB factors examined, students exhibited the highest levels of courtesy followed by altruism; students reported the lowest levels of conscientiousness. The results indicate that agricultural educators in leadership or communication courses may find courtesy as an effective entry point for engaging learners. Additionally, the use of OCBs as a content area may help to provide workforce development opportunities. Preparing individuals with both the technical knowledge required for a career and the skills required for success should be a priority of agricultural educators. The current study provides recommendations and proposes OCBs as a potential candidate for success skill education.


For better or worse, companies are corporate machines which exist to make a profit (Hagel et al., 2009). Individuals are the input into these machines, and often, this workforce is sustained by the influx of university-educated post-graduates (Grubb & Lazerson, 2005). In today’s global economy, employees are often required to work in groups comprised of colleagues from different professional, ethnic, socio-economic, and personal backgrounds. Thus, the ability to be a good team player and work effectively with a variety of peers is a highly coveted skill (Clark, 2012). Managers are looking for high-capacity employees who can successfully collaborate with diverse personalities and maintain a network of connections (The Economist, 2018). The National Institute of Food and Agriculture (2015) reported a shortage of 22,500 college-educated employees for open positions within the agricultural industry. Therefore, the responsibility of adequately preparing all graduates entering the agricultural field becomes more critical such that new employees continue to be successful in the changing work environment. 

Education and human resource development typically occur in three locations within the agricultural sector: (1) formalized education (public schools and higher education), (2) workforce education through public and private organizations, and (3) nonformal education programs (Rivera & Alex, 2008). For academia specifically, how can higher education professionals ensure their students are equipped with skills necessary to be marketable in the current job market? One method is developing emotionally intelligent individuals who cultivate an atmosphere of psychological safety. A social confidence characterized by interpersonal trust and mutual respect between team members (Schneider, 2017; Duhigg, 2016), psychological safety is built by behaviors necessary to establish interpersonal bonds, e.g. conversational turn-taking and emotional conversations (Duhigg, 2016). Podsakoff et al. (1990) found the effects of transformational leadership behaviors were mediated by the follower’s trust, implying that even leader behaviors are filtered through follower perceptions. Individuals who feel valued and secure when participating in team discussions are more likely to embrace their full potential and provide the team with innovative ideas and creative solutions (Henderson, 2017). Encouraging students to appreciate the strengths of their peers creates pathways for open communication and may result in the development of individuals who become highly-effective members of high-performing corporate teams (Steenbarger, 2018).

Much research has been conducted regarding the desired traits of students entering careers. To be successful in new agricultural careers, students must obtain skill development in leadership, teamwork, critical thinking, creative problem solving, and adaptability (e.g. Casner-Lotto & Barrington, 2006; Landrum et al., 2010; Paranto & Kelkar, 2000; Rateau et al., 2015). Over time these habits will holistically improve performance and may lead to increased productivity and member satisfaction in the workplace (Steenbarger, 2018). Organ (1988) defined these behaviors as organizational citizenship behaviors (OCBs), i.e. discretionary behaviors “not part of the employee’s formal role requirements, [but which] promote the effective functioning of the organization,” (p. 4). Example behaviors include cooperating with coworkers, taking preventative actions against workplace issues, offering suggestions to improve the organization, and making intentional investments in one’s professional development skillset (Brief & Motowidlo, 1986). Therefore, it may be appropriate for agricultural educators to consider the role of these OCBs as both an effective classroom engagement technique and a toolset to prepare learners for the workforce.

A comprehensive review of existing citizenship behavior literature found strong evidence indicating OCBs are related to performance (Podsakoff et al., 2000). This belief is well-founded since OCBs may help to increase employee and managerial productivity, expand resource availability, improve coordination within and across work groups, improve an organization’s retention rate, and boost an organization’s overall adaptability (Podsakoff et al., 2000). In addition, OCBs have been found to relate to quantity and quality of work performance (Podsakoff et al., 1997; Podsakoff & MacKenzie, 1994; Walz & Niehoff, 1996). However, not all characteristics of OCBs impact performance in the same way; some dimensions display stronger evidence for these relationships than others (Podsakoff et al., 2000).

The current study is intended to contribute to meaningful and engaged learning within agricultural education contexts as well as adequately preparing students to engage in the scientific and professional workforce. By examining undergraduate student levels of the five OCB dimensions the study intends to address specific questions such as “what methods, models, and programs are effective in preparing people to work in a global agriculture and natural resource workforce?” (Roberts et al., 2016, p. 31) and “how can delivery of educational programs in agriculture continually evolve to meet the needs and interests of students?” (p. 39). Agricultural educators are at the nexus between engaged learning and workforce preparation. Awareness of learner tendencies may help to inform teaching strategies and learner outcomes (McKeachie & Svinicki, 2013).

Conceptual Framework

Although there are numerous conceptualizations of OCB (e.g. Smith et al., 1983; Moorman & Blakely, 1995; Podsakoff, et al., 2000), the conceptual framework for this research is based on the model proposed by Podsakoff et al. (1990), who identified five dimensions of OCB: altruism, conscientiousness, sportsmanship, courtesy, and civic virtue.

OCB Dimensions


Although altruism is a facet of agreeableness in the five-factor personality model (Costa & McCrae, 1992), altruism – as a dimension of OCB – is defined as discretionary behaviors which help a specific individual with an organizationally relevant task or problem, (Podsakoff, et al., 1990). This behavior should not be perceived as mere charity nor precipitated by personality-based dispositions that predispose individuals towards helping others (Khalil, 2004). In this context, altruism is a voluntary willingness to assist a colleague with a work-related task (Podsakoff & MacKenzie, 1994).


It is important to note that while conscientiousness is one of the factors in the widely-accepted five-factor personality model (Costa & McCrae, 1992), this is not the context meant when referring to conscientiousness relative to OCB. As a dimension of OCB, conscientiousness characterizes the practice of voluntarily completing task-related behaviors at a level “well beyond the minimum role requirements,” (Podsakoff et al., 1990, p.115). This dimension is difficult to distinguish from in-role behavior because the distinction lies primarily in the degree to which the task is performed, not necessarily the nature of the task itself (Organ, 1988).


Sportsmanship consists of an individual’s willingness to assist others or tolerate less than ideal circumstances without complaint or perceived offense (Podsakoff et al., 1997; Podsakoff et al., 1990; Podsakoff & MacKenzie, 1994). This behavior involves the ability to maintain a positive demeanor, a state of general agreeableness, and a willingness to sacrifice personal interests for the good of the group (Podsakoff et al., 2000).


Courtesy is defined as a voluntary action that seeks to prevent the occurrence of work-related problems with other peers (Podsakoff et al., 1990; Podsakoff & MacKenzie, 1994). Examples of these behaviors include notifying superior of an absence or holding a meeting with team members to assign task-related responsibilities. Within the workplace, courtesy had a significant positive relationship with the quality of supervisory/subordinate relationships (Tanksy, 1993).

Civic Virtue

Civic virtue describes behaviors that exhibit concern for the organization and includes the individual’s responsibility to participate in the larger group (Podsakoff et al., 1990; Podsakoff & MacKenzie, 1994; Podsakoff et al., 1997). For example, these actions may involve offering constructive criticism to elevate work-group effectiveness, which may result in increased resource availability or enhanced efficacy (Podsakoff, et al., 1997).

OCBs in Education

Within the existing literature base, there is a noticeable dearth of studies examining OCB levels among undergraduate students in higher education. Many of the studies concerning OCBs in education examine antecedents of instructor-level OCBs (e.g. Somech & Ron, 2007; Kagaari & Munene, 2007) or the influence of instructor-level OCBs on various outcomes, (e.g. Khalid et al., 2010; Allison et al., 2001; Rose, 2012; Jimmieson et al., 2010).

As it relates to antecedents of instructor-level OCBs, previous research has found enabling school structures and academic optimism both had a positive relationship with instructor OCB levels (Messick, 2012). Kagaari and Munene (2007) found that lecturer overall OCB level was statistically significantly related to individual’s ability to plan, organize, and supervise others. Additionally, perceived superior support and collectivism both had positive relationships with altruism, conscientiousness, sportsmanship, civic virtue, and overall instructor-OCB levels, while negative affectivity had a negative relationship with these same instructor OCB dimensions (Somech & Ron, 2007).

Examining the effects of professor OCB levels on their undergraduate students’ success, Khalid et al. (2010) found both instructor-level altruism and courtesy positively predicted student academic performance. Jimmieson et al. (2010) found that teacher levels of civic virtue were positively related to job efficacy which in turn had a positive effect on student quality of school life.  Additionally, Rose (2012) found that faculty reporting higher OCB-I levels (i.e. altruism or interpersonal helping behaviors) reported higher student contact hours and those reporting higher levels of OCB-O (i.e. helping behaviors directed toward overall organization) reported a greater number of presentations and increased service on institutional committees.

At the student level, Allison et al. (2001) examined student academic performance based on: student productivity, as measured by student’s semester course load and associated semester GPA, and overall GPA. Results indicated that student sportsmanship and conscientiousness were positively related with student GPA. Additionally, sportsmanship, conscientiousness, civic virtue as well as overall OCB levels were positively related to student productivity (Allison et al., 2001). At the more general level, LeBlanc (2014) examined possible antecedents of OCBs in college students. Results indicated gender was positively related to a willingness to engage in or actual engagement in OCBs, and female students were more likely to report higher OCB levels. Additionally, religious affiliation had a positive relationship with student OCB levels, as students who identified as devout members of religious faiths reported higher levels of OCBs. Undergraduate major also had a statistically significant relationship with engagement in, or willingness to engage in OCBs, with students who majored in social sciences (i.e. helping professions) reporting higher levels of OCBs than students majoring in business or STEM related fields (LeBlanc, 2014). However, LeBlanc (2014) clarifies that this association may not be due to the specific major of students but rather the characteristics inherent in students who choose those particular majors. The present study extends upon the results of LeBlanc (2014) with a particular focus on level of OCB amongst undergraduate students enrolled in agricultural education leadership or communication courses.

Purpose and Research Objectives

The purpose of this study is to examine the five dimensions of OCB amongst undergraduate students enrolled in agricultural education leadership or communication courses. As such, this study is driven by the following research objectives:

  1. Describe individual levels of altruism amongst undergraduate students enrolled in agricultural education leadership or communication courses.
  2. Describe individual levels of conscientiousness amongst undergraduate students enrolled in agricultural education leadership or communication courses.
  3. Describe individual levels of sportsmanship amongst undergraduate students enrolled in agricultural education leadership or communication courses.
  4. Describe individual levels of courtesy amongst undergraduate students enrolled in agricultural education leadership or communication courses.
  5. Describe individual levels of civic virtue amongst undergraduate students enrolled in agricultural education leadership or communication courses.
  6. Examine whether OCB levels were statistically significantly different between classes.


A descriptive study was employed to meet these research objectives. Undergraduate students enrolled in agricultural education leadership or communication courses was the population of interest for the study. A purposive sample of five courses were selected across two universities, and included both agricultural leadership and agricultural communication courses. Specifically, four courses were from an agricultural leadership course taught over four semesters at the University of Florida from 2013 (n = 32), 2014 (n = 44), spring 2015 (n = 40), and fall 2015 (n = 39). The other class was from an agricultural communications course taught over one semester at University of Georgia in 2018 (n = 81). The agricultural leadership course was taught by the same instructor all four times and was focused on leadership in groups and teams. The course included lecture, service learning, and team projects. Enrollment was open to students across the university and included both agricultural majors, as well as individuals from other colleges across the university. The agricultural communication course was taught by a different instructor and included lecture and project-based learning. Again, enrollment was open to students across the university and included both agricultural majors, as well as individuals from other colleges at the university. It is important to note that the data analyzed within this study capitalizes on data previously collected within the Lamm et al. (2017) sample. The current study broadens the results of the previous one in several notable ways. While the previous study centered on personality analysis, this study concentrates on examining organizational citizenship behavior trends across undergraduate students enrolled in agricultural education leadership and communication courses. These disclosures are made based on existing recommendations for clarity (Kirkman & Chen, 2011).

Data were collected using a paper-based questionnaire, which was distributed, completed, and recollected for analysis during a single class period. A total of 236 responses were obtained for an effective response rate of 100%; however, incomplete responses contributed to lower response rates reported at the individual scale level.

Demographics were self-reported by each respondent. Of the respondents, 68.2%

(n = 161) were female, and 30.1% (n = 71) were male. Regarding university classification, 13.1% (n = 31) of respondents were freshmen, 14.4% (n = 34) were sophomores, 30.1% (n = 71) were juniors, and 41.9% (n = 99) were seniors. As for racial demographics, 84.3% (n = 199) of respondents indicated they were white, 8.1% (n = 19) indicated they were Black or African American, 7.2% (n = 17) indicated they were Asian or Pacific Islander, 0.4% (n = 1) indicated they were American Indian or Alaska Native. Individuals were able to select as many race categories as they felt applied, therefore total counts may not match overall response rate. Respondents indicated an age range between 18 and 48 (M = 21.34, SD = 3.03). Respondent major information was not collected as part of the research study; however, respondents represented agricultural undergraduate majors including agricultural education, animal sciences, and horticulture, as well as other majors within the university including, business, journalism, engineering, family and consumer sciences, and so forth.

Individual-level OCB scores for each dimension were collected using the 24-point scale developed by Podsakoff et al. (1990).  Responses were rated on a five-point, Likert-type scale, with possible response ranging from 1 = strongly disagree to 5 = strongly agree. Five scale items measured conscientiousness, including “I obey company rules and regulations even when no one is watching.” Sportsmanship was measured with five scale items, for example, “I consume a lot of time complaining about trivial matters.” Four scale items measured civic virtue and included “I attend meetings that are not mandatory but are considered important.” Courtesy was measured by five scale items, for example, “I am mindful of how my behavior affects other people’s jobs.” Altruism was measured by five scale items, including “I willingly help others who have work-related problems.”

 As a measure of internal consistency and reliability, Cronbach’s α coefficient was calculated for each of the five dimension indices, as well as the overall OCB index. In particular, the altruism index was found to have a Cronbach’s α coefficient of 0.84, the conscientiousness index was found to have an α coefficient of 0.65, the sportsmanship index was found to have an α coefficient of 0.76, the courtesy index was found to have an α coefficient of 0.77, and the civic virtue index was found to have an α coefficient of 0.70. The overall instrument had an α coefficient of 0.85.

The data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Descriptive statistics were calculated to quantify levels of OCBs amongst undergraduate students enrolled in agricultural education leadership or communication courses. A one-way ANOVA was used to determine whether OCB observations were statistically significantly different between classes, or whether OCB values were not statistically significantly different. A significance level of .05 was determined a priori.


Descriptive statistics, including mean scores, were calculated for the five individual OCB dimensions as well as an overall OCB index score. Results are presented in Table 1.

Table 1
OCB Scale Scores of Undergraduate Students Enrolled in Agricultural Education Leadership or Communication Courses
OCB Scale ScoresnMSDMinMax
Civic Virtue2303.930.512.505.00

Undergraduate students enrolled in agricultural education leadership or communication courses reported the highest mean score in the courtesy dimension (M = 4.23, SD = 0.46) and the lowest mean score for the conscientiousness dimension (M = 3.86, SD = 0.53). The results of the one-way ANOVA tests found no significant effect of class on any of the five OCB dimensions nor overall OCB for undergraduate students completing agricultural education leadership or communication related courses. The results from the ANOVA analyses are displayed in Table 2.

Table 2
Summary of One-Way ANOVA Tests
AltruismBetween Groups0.36240.0900.3040.875
Within Groups68.2532290.298  
ConscientiousnessBetween Groups1.99740.4991.8090.128
Within Groups63.4722300.276  
SportsmanshipBetween Groups3.59740.8992.2400.066
Within Groups90.6982260.401  
CourtesyBetween Groups0.85440.2130.9810.418
Within Groups50.0182300.217  
Civic VirtueBetween Groups0.64640.1620.6270.644
Within Groups57.9912250.258  
Overall OCBBetween Groups0.85140.2131.6660.159
Within Groups28.2102210.128  

Conclusions, Recommendations, and Implications

The first five research objectives concerned describing the individual levels of each OCB dimension in undergraduate students enrolled in agricultural education leadership or communication courses. We found that within this sample, all of the OCB dimensions were rated relatively high. From their perspective, students primarily agreed that they displayed these OCB dimensions within their class.

The last research objective concerned examining potential OCB differences between classes. The lack of a statistically significant difference observed between classes was somewhat unexpected. For example, LeBlanc (2014) found that undergraduate student OCB levels were likely to be influenced by factors including gender, religious affiliation/devoutness, and most relevantly university major. Within the present study no statistically differences were observed between classes. Therefore, the result of the current study may indicate undergraduate OCB levels of students enrolled in agricultural education leadership or communication courses may be more consistent across course type and university than originally expected. This conclusion is more consistent with previous research amongst university instructors where a range of instructor competencies were not found to be statistically significantly related to OCBs (Kagaari & Munene, 2007).

A primary contribution of the present study is to establish a foundational set of data upon which to expand future agricultural education related research. However, as a main limitation, the scope of the study is limited and thus the generalizability is also limited. To mitigate the limitation, purposive selection of five courses across both agricultural education leadership and communication, from multiple years, with two different instructors, and in two different universities was conducted. Therefore, although the results indicate that there were not any statistically significant differences observed between classes, it is not possible to conclude that the primary observations (e.g. courtesy is highest and conscientiousness is lowest) will remain consistent across all agricultural education leadership and communication courses. An associated recommendation would be for educators to consider administering an instrument like the one from the present study at the beginning of a course to get a general sense for the composition of learners and what teaching strategies might appeal to learners within a specific classroom environment. An additional recommendation would be to replicate the findings of the present study and determine whether there are trends that agricultural educators can use to inform their teaching practice. For example, replicating data collection in agricultural education courses across leadership, communication, teacher preparation, extension, and so forth may help to validate the present findings, or provide a more comprehensive perspective of learner tendencies, doing so would help educators continue to create learning environments that “continually evolve to meet the needs and interests of students” (Roberts et al., 2016, p. 39).

A recommendation for future research would be to examine OCBs as a set of predictor variables for student performance. Specifically, are students going to be more successful in the classroom and in their careers because they display higher OCB levels? Or do students who display higher OCB levels do so because of their success? This distinction has not yet been explored within the literature so it would be interesting to examine the influence of mediating variable, such as conditions which predispose OCBs, on external outcomes. Another recommendation for future research would be to examine whether instructor-level OCBs influence student-level OCBs at the undergraduate level. For instance, if a professor displays high levels of courtesy, or any other OCB dimension, would students be more inclined to reciprocate these behaviors? Support for this line of research doesn’t necessarily result from the conclusions of this study, but the concepts are related and are important to consider within the context of agricultural education more broadly. For example, Khalid et al. (2010) found that instructor-level OCBs positively influence student performance, while Allison et al. (2001) found that student-level OCBs positively influence student performance in terms of productivity and overall GPA. We wonder, is there an interaction between these two relationships? Is it because instructor displays of OCBs appeal to, and catalyze the reciprocal display of, student OCBs, which in turn contribute to increased performance? The literature appears to be inconclusive so it is important to continue to examine the nature of the phenomenon.

In addition to the above recommendations for research, recommendations are also posited for application. Specifically, a recommendation would be for agricultural leadership and communication educators to consider integrating OCB education curriculum into existing course content. The National Council for Agricultural Education (2015) outlined career ready practices amenable to OCB education as part of their Agriculture, Food, and Natural Resources foundational content standards. Specifically, CRP.09.03 states educators should “demonstrate behaviors that contribute to a positive morale and culture in the workplace and community (e.g., positively influencing others, effectively communicating, etc.” (National Council for Agricultural Education, 2015, p. 22). Sub-sections of this section instruct agricultural educators to identify and summarize respectful and purposeful behaviors, to examine personal levels of these behaviors, and to devise, implement, and evaluate strategies for continuation and improvement of these behaviors (National Council for Agricultural Education, 2015). These standards represent clear opportunities for integration of OCB education into the existing AFNR content. We recommend that teachers capitalize on these opportunities by introducing OCB education to students and discussing the characteristics and implications of OCBs in community and career settings.

Beyond the use of OCBs to connect with learners as an engagement strategy, the literature is clear that OCBs can have a positive effect once an individual enters the workforce (e.g., Alkahtani, 2015; Chen et al., 1998; Podsakoff et al., 1997). Taking the time to inform learners about the fundamental characteristics of OCBs and the relationship with many positive workforce outcomes may help individuals to be aware of how their actions and specifically their embodiment of OCB characteristics may help “to prepar[e] people to work in a global agriculture and natural resource workforce” (Roberts et al., 2016, p. 31). The use of OCBs as a content area within educational settings may help to provide workforce development opportunities. Preparing individuals with not only the technical knowledge required for a career, but also for the skills required for success in the workforce should be a priority of agricultural educators. The current study provides recommendations and proposes OCBs as a potential candidate for success skill education.


Alkahtani, A. (2015). Organizational citizenship behavior (OCB) and rewards. International Business Research, 8(4). 210-222.

Allison, B. J., Voss, R. S., & Dryer, S. (2001). Student classroom and career success: The role of organizational citizenship behavior. Journal of Education for Business, 76(5). 282-288.

Brief, A. P., & Motowidlo, S. J., (1986). Prosocial organizational behaviors. Academy of Management Review, 11(4). 710-725.

Casner-Lotto, J., & Barrington, L. (2006). Are they really ready to work? Employers’ perspectives on the basic knowledge and applied skills of new entrants to the 21st Century U.S. workforce. USA: The Conference Board, Inc., the Partnership for 21st Skills, Corporate Voices for Working Families, and the Society for Human Resource Management.

Chen, X. P., Hui, C., & Sego, D. J. (1998). The role of organizational citizenship behavior in turnover: Conceptualization and preliminary tests of key hypotheses. Journal of Applied Psychology, 83(6). 922-931.

Clark, D. (2012). Five ways to become a better team player. Forbes. Retrieved from

Costa, P. T., Jr., & McCrae, R. R. (1992). Revised NEO personality inventory and five factor inventory professional manual. Psychological Assessment Resources.

Duhigg, C. (2016). What Google learned from its quest to build the perfect team. New York Times. Retrieved from

Grubb, W. N., & Lazerson, M. (2005). Vocationalism in higher education: The triumph of the education gospel. The Journal of Higher Education, 76(1), 1-25.

Henderson, T. (2017). Leadership and teamwork: The secret sauce to business success. Forbes. Retrieved from

Jimmieson, N. L., Hannam, R. L., & Yeo, G. B. (2010). Teacher organizational citizenship behaviours and job efficacy: Implications for student quality of school life. British Journal of Psychology, 101(3), 453-479.×470572

Kagaari, J. R. K., & Munene, J. C. (2007). Journal of European Industrial Training, 31(9), 706-726.

Khalid, S. A., Jusoff, H. K., Othman, M., Ismail, M., & Rahman, N. A. (2010). Organizational citizenship behavior as a predictor of student academic achievement. International Journal of Economics and Finance, 2(1). 65-71.

Khalil, E. (2004). What is altruism? Journal of Economic Psychology, 25. 97-123.

Kirkman, B. L., & Chen, G. (2011). Maximizing your data or data slicing: Recommendations for managing multiple submissions from the same data set. Management and Organization Review, 7(3). 433-446.

Lamm, K. W., Sheikh, E., Carter, H. S., & Lamm, A. J. (2017). Predicting undergraduate leadership student goal orientation using personality traits. Journal of Leadership Education, 16(1), 18-33.

Landrum, R. E., Hettich, P. I., & Wilner, A. (2010). Alumni perceptions of workforce readiness. Teaching of Psychology, 37(2), 97-106.

LeBlanc, C. J. (2014). Characteristics shaping college student organizational citizenship behavior. American Journal of Business Education, 7(2), 99-108.

McKeachie, W., & Svinicki, M. (2013). McKeachie’s teaching tips. Cengage Learning.

Messick, P. P. (2012). Examining relationships among enabling school structures, academic optimism, and organizational citizenship behaviors [Doctoral dissertation, Auburn University]. Auburn University Electronic Theses and Dissertations.

Moorman, R. H., & Blakely, G. L. (1995). Individualism-collectivism as an individual difference predictor of organizational citizenship behavior. Journal of Organizational Behavior, 16. 127-142.

National Council for Agricultural Education. (2015). Agriculture, food, and natural resources (AFNR) career cluster content standards. Retrieved from

National Institute of Food and Agriculture (2015). One of the best fields for new college graduates? Agriculture. Retrieved from

Organ, D. W. (1988) Organizational citizenship behavior: The good soldier syndrome. Lexington, Books.

Paranto, S. R., & Kelkar, M. (2000). Employer satisfaction with job skills of business college graduates and its impact on hiring behavior. Journal of Marketing for Higher Education, 9(3), 73-89.

Podsakoff, P. M., Ahearne, M, & MacKenzie, S. B. (1997). Organizational citizenship behavior and the quantity and quality of work group performance. Journal of Applied Psychology, 82(2), 262-270.

Podsakoff, P. M., & MacKenzie, S. B. (1994). Organizational citizenship behaviors and sales unit effectiveness. Journal of Marketing Research, 31(3). 351-363.

Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers’ trust in leader, satisfaction, and organizational citizenship behaviors. Leadership Quarterly, 1(2), 107-142.

Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., & Bachrach, D. G. (2000). Organizational citizenship behaviors: A critical review of the theoretical and empirical literature and suggestions for future research. Journal of Management, 26(3). 513-563.

Rateau, R. J., Kaufman, E. K., & Cletzer, D. A. (2015). Innovative classroom strategies that prepare college graduates for workplace success. Journal of Agricultural Education, 56(3), 52-68.

Rivera, W. M., & Alex, G. E. (2008). Human resource development for modernizing the agricultural workforce. Human Resource Development Review, 7(4), 374-386.

Roberts, T. G., Harder, A., & Brashears, M. T. (Eds). (2016). American Association for Agricultural Education national research agenda: 2016-2020. Department of Agricultural Education and Communication.

Rose, K. J. (2012). Organizational citizenship behaviors in higher education: Examining the relationships between behaviors and performance outcomes for individuals and institutions (Publication No. 403) [Doctoral dissertation, University of Arkansas-Fayetteville]. ScholarWorks@UARK Theses and Dissertations.

Schneider, M. (2017, July 19). Google spent 2 years studying 180 teams. The most successful ones shared these 5 traits. Inc. Retrieved from

Smith, C. A., Organ, D. W., & Near, J. P. (1983). Organizational citizenship behavior: Its nature and antecedents. Journal of Applied Psychology, 68. 655-663.

Somech, A., & Ron, I. (2007). Promoting organizational citizenship behavior in schools: The impact of individual and organizational characteristics. Educational Administration Quarterly, 43(1), 38-66.

Steenbarger, B. (2018, June 29). The three strategies for making your team work. Forbes. Retrieved from

Tansky, J. W. (1993). Justice and organizational citizenship behavior: What is the relationship? Employee Rights and Responsibilities Journal, 6(3). 195-207.

The Economist. (2018, September 6). The pros and cons of collaboration. Retrieved from

Walz, S. M., & Niehoff, B. P. (1996). Organizational citizenship behaviors and their effect on organizational effectiveness in limited-menu restaurants. Academy of Management Best Papers Proceedings, 1996(1), 307-311.

Teaching Agriculture-specific Controversial Issues Through Guided Group Discussion

Chaney Mosley, Middle Tennessee State University,

Thomas Broyles, Tennessee State University,

James Scott, Middle Tennessee State University,

PDF Available


The effect of participating in and observing a guided group discussion on attitude toward agriculture-specific controversial issues was investigated. Fifty-five undergraduate students over two semesters completed a pretest to measure attitudes toward two controversial topics: sustainable agriculture and animal welfare. After the pretest, students were randomly assigned to one of four roles. Each role was assigned one of the two topics and given neutral questions to research in preparation for a group discussion that were related to the characteristics of controversy. Two roles, each with a different topic, met for the purpose of having neutral discussions on the topic. While one group discussed, the other group observed. A posttest was administered to measure the change in attitudes toward the controversial topics. Students also provided written responses to open-ended questions regarding their experience with the group discussion activity. No significant differences between pretest and posttest scores were observed. Based on the qualitative data, students preferred teacher centered methods of teaching controversial issues and appreciated that the guided group discussion approach allowed controversial topics to be considered and delivered objectively.

Keywords: controversial issues, group discussion, animal welfare, sustainable agriculture


Many controversial issues are closely related to agriculture, such as genetically modified organisms, food product labeling, or animal identification systems. Consequently, much of these issues become infused into agricultural education curricula, often presenting ideas that conflict with the values of students (Cotton, 2006b). Though controversial topics in agricultural science classrooms have become a larger issue in recent years given that political parties have become enamored with debating climate change and other agricultural related topics (Owens et al., 2017), the research on teaching agriculture-specific controversial issues is severely limited (Agbaje et al., 2001; Bennett-Wimbush et al., 2015; Fiske, 1991; Goodwin, 1993; Nordstrom et al., 2000; Poole et al., 2016; Terry & Lawver, 1995). Whether educators can maintain neutrality when teaching controversial issues is questionable, as the rhetorical nature of controversial issues suggests that teacher neutrality may be impractical and “the idea of maintaining a neutral position is portrayed as an illusion” (Cotton, 2006a, p. 77). The inability to maintain neutrality begs the question, why teach controversial issues?

Teaching about issues that are controversial, while requiring a lot of time and preparation, has been viewed as a useful tool for preparing students to become effective citizens (Soley, 1995). A healthy democracy is based on the nature of open discussion about issues of public concern. Therefore, young citizens should be trained in the discussion of social, political, and economic policies that are controversial (Harwood & Hahn, 1990). Additionally, introducing controversial issues serves as an appropriate way for students to learn about values and value conflicts. Another advantage of instruction on controversial issues is the encouragement of thinking. Assessment that measures students’ ability to regurgitate facts requires low levels of thinking; however, learning about controversial issues requires in-depth study, consideration of facts versus opinions, and critical examination of the issues. Learning how to approach, investigate, and form an opinion on controversial issues may present cognitive conflict, but can also serve as a bridge for assisting students in dealing with their own personal conflicts (Soley, 1995). Though the benefits of teaching controversial issues present a strong argument in support of the notion, teacher attitudes and perceptions should be considered.

While many educators believe that teaching controversial issues is important, this belief system is only in place so long as the teaching of these issues does not endanger their careers (Byford, Lennon, & Russel, 2009). Support from educators exists because teaching controversial topics exposes issues of personal and societal interest that students can often relate to, but some teachers are unsure of their ability to teach controversial content (Byford et al., 2009; Zimmerman & Roberston, 2017). Asimeng-Boahene (2007) asserted that “conducting beneficial discussions on controversial issues is an art that requires skills and practice” (p. 235). To increase teacher efficacy for presenting controversial issues, training is needed that focuses on the nature of controversial issues, principles for teaching controversial issues, and effective teaching strategies (Robertson, 2018), especially when topics are polarizing.

Zimmerman and Robertson (2017) explain that controversial issues fall into three categories: expert-expert disagreement, expert-public disagreement, and maximally controversial issues. Expert-expert disagreement is characterized by experts disagreeing on topics not of widespread public concern (such as interpretations of literary works or visual art), whereas expert-public disagreement is described as experts agreeing, but members of the general public contesting the stance of experts (such as climate change being caused by human behavior). Maximally controversial issues are those where experts disagree with each other and members of the general public disagree with each other, the topic is of public concern, and discussions generate an emotional response (such as abortion, voting rights, or same-sex marriage). In agricultural education, animal rights (Nordstrom et al., 2000) and sustainable agriculture (Agbaje et al., 2001) are examples of maximally controversial issues that can be so dividing, teachers must exercise caution when teaching them, but how?  

When introducing controversial issues, adopting a stance that is non-committal and neutral is critical (Asimeng-Boahene, 2004; Zimmerman & Robertson, 2017) because “everything the teacher does, as well as the manner in which he does it, incites the child to respond in some way or another, and each response tends to get the child’s attitude in some way or the other” (Dewey, 1933, p. 59). Teachers should not be afraid to share their opinions with a class; however, they need to be able to defend their opinions with logical explanations and should emphasize that their position is one of many and that it may be challenged. Still, agriculture, as a content area, is unique in that student attitudes may be strongly rooted and influenced by personal background. For example, Terry and Lawver (1995) discovered that male students had more positive perceptions about using medications on animals that females and that hometown background such as growing up on a farm or living in a town of less than 5,000, for example, explained large amounts of variance in student perceptions of issues related to agriculture. Further, Poole et al. (2016) discovered academic major influenced student concerns about agricultural issues, and Bennett-Wimbush et al. (2015) reported female students were better able to distinguish between animal rights and animal welfare than male students. Therefore, agricultural educators may be more inclined to employ a strategy that affords ambiguity of personal stance when teaching. One method of introducing controversial issues into the classroom, alleviating the teacher from committing to one side or the other, is group discussion (Ho et al., 2017).

Through group discussion, students can expand their clarity of controversial issues. In addition to serving as a bias free approach, group discussions on topics that are controversial in nature are stimulating and “can be an excellent way of expanding the knowledge students have about the changing world in which we live” (Asimeng-Boahene, 2004, p. 233). According to Hess (2009), discussion is a valued form of learning for students. After selecting an issue to be discussed, teachers must prepare students for the discussion, provide an adequate amount of information resources, ensure an intellectual balance, and encourage equal participation. Because there are typically not right or wrong answers with controversial issues, performance-based activities, such as group discussions, are often better suited for assessment than traditional paper-based tests. Performance based activities allow educators to assess a student’s ability to evaluate competing arguments, use evidence to defend a position, and draw well thought out conclusions (Asimeng-Boahene, 2004). Furthermore, participation in group discussion demonstrates performance at higher levels of learning (Anderson et al., 2001), but not all educators agree with discussion as the best technique. Proponents of teacher centered classrooms argue that teaching only the facts or concepts is easier and more straightforward than helping students examine attitudes, values, and beliefs associated with controversial issues; however, if students do not learn to address moral dilemmas and argue social issues when in school, when will they? Teachers have the responsibility of supplying a format for learning how to identify controversy and labor through it (Asimeng-Boahene, 2004; Zimmerman & Robertson, 2017).

Theoretical and Conceptual Framework

The framework for this study was built on Festinger’s (1957) cognitive dissonance theory and the cognitive reconstruction of knowledge model (CRKM) (Figure 1) presented by Dole and Sinatra (1998). According to Festinger (1957), people desire consistency among individual concepts including attitudes, behaviors, beliefs, values, and opinions. Cognitive dissonance theory purports dissonance occurs when information is presented that contradicts with one’s held concepts. The strength of dissonance is impacted by two things – the amount of discordant beliefs and the degree of importance attached to each belief. When contradiction is present, something must adjust to eliminate the dissonance. Festinger’s theory provides three methods by which dissonance can be removed. One possibility for eliminating dissonance is the reduction of importance of the inharmonious thought. A second option for removal involves attaching more harmonious beliefs that compensate for the dissonant beliefs. The third method for removing dissonance is to change the cacophonous beliefs so that they are no longer inconsistent (Festinger, 1957). When beliefs are altered to rid inconsistency, conceptual change occurs. Conceptual change refers to “revisions in personal mental representations; revisions that are often precipitated by purposeful educational experiences” (Murphy & Mason, 2006, p. 307). Because group discussions about controversial topics will facilitate cognitive dissonance, conceptual change may occur.

In comparison to cognitive dissonance theory, the CRKM considers cognitive psychological research, science education research, and social psychology (Dole & Sinatra, 1998). This model provides a description of the interactions between learner and message characteristics, which lead to various degrees of engagement with a new concept. The likelihood that conceptual change will occur depends on the depth of engagement – significant conceptual change is more likely when learners present high engagement on the engagement continuum.

Figure 1
Cognitive Reconstruction of Knowledge Model (Dole & Sinatra, 1998)

The visual model (Figure 2) developed by the researchers provides the conceptual framework for the present study. In the model, engagement levels of two groups, participant and observer, are depicted by shaded areas at each phase of a group discussion guided by research questions. Participants are those who are involved with researching a topic, discussing the topic from a neutral stance, and formulating a position on the topic after the discussion. Observers are those who watch and listen, but have no formal responsibilities before, during, or after the discussion. Being active in each phase, the researchers assume participant group engagement will start low during the research phase, reach a crescendo during the group discussion, and decrease again in the final phase when participants are formulating a final position. With a passive role, the researchers assume the observer group will not be engaged during the research or formulating position phases and experience limited engagement during the group discussion. Each engagement continuum is bookended by held attitudes about topics being discussed, as students will hold a perspective before and after the discussion.

Figure 2
Visual Model of the Conceptual Framework

Purpose and Research Questions

The purpose of this study was to investigate the utility of guided group discussion (Lewin, 1952; Werner et al., 2008; Werner & Stanley, 2011) as a method for providing instruction on controversial issues. When a teacher presents information on topics that are controversial in nature, there may be students who disagree with the content, resulting in cognitive dissonance. With continued instruction, conceptual change could occur; however, if the teacher is unable to instruct in a neutral manner, he or she may unintentionally cause conceptual change from a bias standpoint. The study was steered by the overarching question of whether guided group discussion was an effective approach to teaching controversial issues. Specific questions were:

  1. What is the effect of participating in a group discussion on attitudes toward controversial issues?
  2. What is the effect of observing a group discussion on attitudes toward controversial issues?
  3. How do students prefer to learn about controversial issues?
  4. How do students perceive the strategy of guided group discussion for learning about controversial issues?


The participants in this study were undergraduate students enrolled in a fall semester and spring semester agricultural oral communications course at a four-year university in the southeastern region of the United States. The course was a required course for all students pursuing an undergraduate degree in an agricultural field. Data were collected over two semesters resulting in a total of 55 students divided across five laboratory sections over the two semesters. Institutional Review Board procedures were followed by university guidelines. Consent was obtained from all participants. Table 1 provides a description of the participants.

Table 1
Description of Participants (N= 55).

Instruments were used in a pretest and posttest to assess the attitudes of students toward two specific controversial issues – sustainable agriculture and animal welfare. Sustainable agriculture is the production of plant and animal products for human consumption through methods that are ecologically sound and socially responsible as well as economically viable (Ikerd, 2008, p. 11). As this method of production contradicts modern industrial agriculture techniques, agricultural education teachers are unsure about the potential for sustainable agriculture to enhance the quality of life for famers and society, thus making this topic controversial in the agriculture industry (Agbaje et al., 2001). According to Broom (1991), animal welfare refers to the state of an animal in relation to its environment, with welfare being a characteristic of an animal, not something given to it; indicators of poor welfare may include reduced life expectancy, impaired growth, body damage, and adrenal activity, among others. Attitudes regarding appropriate treatment of animals differ greatly with polarized opinions related to hunting, production and consumption of animals for food, and using animals in biomedical and psychological research (Herzog & Mathews, 1997); therefore, this topic is also controversial in the agriculture industry.

The Sustainable Agriculture Attitude Test was an adapted version of a test, developed by Allahyari et al. (2008), comprised of twelve self-report items on a five-point Likert-type scale. A response of “1” to each item indicated strong disagreement and a response of “5” indicated strong agreement. Sample items included “The primary goal of farmers should be to maximize the productivity, efficiency, and profitability of their farms” and “The key to agriculture’s future success lies in learning to imitate natural ecosystems and farm in harmony with nature”. Total scores on this instrument can range from 12 to 60; higher scores suggested positive perceptions toward sustainable agriculture. The calculated Chronbach’s alpha reliability coefficient for the pretest and posttest was 0.69 and 0.60, respectively, demonstrating low, but acceptable levels for this type of exploratory research (Murphy & Davidshofer, 1988).

The Animal Attitude Scale, developed by Herzog et al. (1991), measured attitude towards animal welfare and was comprised of 20 self-report items on a five-point Likert-type scale. A response of “1” to each item indicated strong disagreement and a response of “5” indicated strong agreement. Sample items included “I think it is perfectly acceptable for cattle and hogs to be raised for human consumption” and “Much of the scientific research done with animals is unnecessary and cruel”. Total scores on this instrument can range from 20 to 100; higher scores suggested greater levels of concern for animals. The calculated Chronbach’s alpha reliability coefficient for the pretest was 0.90 and the Chronbach’s alpha reliability coefficient for the posttest was 0.90 as well.

One month after completing both pretests, students within laboratory sections were randomly assigned to two groups for the purpose of participating in a group discussion. Each role was then randomly assigned a controversial topic for the group discussion – sustainable agriculture or animal welfare. Students in each group received a set of neutral, topic specific guiding questions that focused on the characteristics of the controversy and were instructed to answer these questions, individually, in preparation for a group discussion. Two weeks after receiving the research questions, students participated in a 20-minute group discussion guided by the questions researched. Prior to the discussion, students were instructed to maintain a neutral position and present evidence gathered during individual research, while addressing both sides of the controversy. As the discussion took place, participating students took notes on various points that were made. At the conclusion of the discussion, each student formulated a position on the topic and articulated this position in a closing statement. While one group in each laboratory section participated in the discussion, the other group observed. Two weeks after the group discussion, the same instruments were used in a posttest. Additionally, students in the spring semester provided written responses to eight open-ended questions regarding their experience with the group discussion activity. According to Bogdan and Biklen (2003), participants will express opinions more freely with open-ended questions.

This mixed methods study was designed as an embedded sequential explanatory case study with a quantitativequalitative two-strand design of inquiry (Creswell et al., 2003). The first strand of inquiry used a quantitative approach to explore student attitudes toward agriculture-specific controversial issues. The second strand of inquiry qualitatively investigated how students experienced the group discussion.

To answer research questions one and two, attitude pretest and posttest scores of the two roles (participant or observer) by topic and semester were analyzed using a paired samples t-test. This is an appropriate analysis to compare the difference between the means in cases where the same participants respond on two separate incidents (Howell, 2007).

Research questions three and four, which were qualitative in nature, were answered using a constant comparative analysis approach to interpret responses to the open-ended questions. According to Glauser and Strauss (1967), this approach requires identifying similarities and differences in content through a systematic review of data. As the researchers coded the responses separately, inter-rater reliability was established, which increased the confidence in emergent patterns (Bernard & Ryan, 2010). Participant quotes were used to support research findings. Because critics may be reluctant to accept the findings from qualitative research, the researchers applied Guba’s (1981) framework for assessing the trustworthiness of qualitative inquiries. In the present study, the researcher ensured credibility by developing a familiarity with the culture being investigated, using a mixed methods approach for triangulation of data, and conducting member checks by sharing selected quotes associated with conclusions drawn with students who provided the quotes. Transferability was ensured as the researchers described the context of the study and described the phenomenon under investigation. Finally, confirmability was achieved by admitting researcher beliefs and assumptions in regard to the study and identifying limitations of the study.


The researchers were concerned with looking at data collected from each of the roles for the two topics. Each discussion group was comprised of undergraduate agricultural majors, but heterogeneous in gender, age, and ethnicity. For each topic, there were five groups who participated in a guided group discussion and five groups who observed (Tables 2 and 3).

Table 2
Participant Role Test Scores (N= 55)
Sustainable Agriculture39.074.27314738.294.143146
Animal Welfare64.5414.08378564.2713.733489
Note. Scores on the Sustainable Agriculture Attitude Test range from 12 to 60. Scores on the Animal Attitude Scale range from 20 to 100.

The mean score on the pretest for those who participated in a discussion about sustainable agriculture was 39.07 (SD= 4.27). For those who observed a discussion about sustainable agriculture, the mean score on the pretest was 41.64 (SD= 4.48). On the posttest, for those who participated in the discussion, the mean score was 38.29 (SD= 4.14), while the mean score for those who observed was 40.44 (SD= 4.03).

Table 3
Observer Role Test Scores (N= 55)
Sustainable Agriculture41.644.48324940.444.033447
Animal Welfare65.0010.45408465.649.684182
Note. Scores on the Sustainable Agriculture Attitude Test range from 12 to 60. Scores on the Animal Attitude Scale range from 20 to 100.

The mean score on the pretest for those who participated in a discussion about animal welfare was 64.54 (SD= 14.08). For those who observed a discussion about animal welfare, the mean score on the pretest was 65.00 (SD= 10.45). On the posttest, for those who participated in the discussion, the mean score was 64.27 (SD= 13.73), while the mean score for those who observed was 65.64 (SD= 9.68).

Research question one inquired about the effect of participating in a group discussion on attitudes toward controversial issues. The results of a paired samples t-test indicated that the effect of participating in a group discussion was not statistically significant.

Research question two asked about the effect of observing a group discussion on attitude toward controversial issues. The results of a paired samples t-test indicated that the effect of observing a group discussion was not statistically significant.

Research question three explored how students preferred to learn about controversial issues. The open-ended questions prompted students to reflect on prior experiences with controversial issues in a classroom setting and explain how they preferred teachers to present topics that are controversial in nature. Student responses indicated a variety of experience with methods that encouraged active learning such as debates, research papers, and general classroom discussions. Passive learning experiences were described as lecture or illustrated lecture (where a PowerPoint presentation was used). Students were not favorable of methods that only presented one side of an issue, evidenced by comments such as “I have had teachers only present their biased opinions and I didn’t like that at all. Teaching that way doesn’t give the student the opportunity to see both sides and make a decision on where the student stands.” and “Just listening to a lecturer can cause the audience to take on the lecturer’s opinion.” Interestingly, when asked about how the students enjoyed learning about controversial topics, the students indicated a preference toward passive learning experiences. Students commented, “I prefer teachers present controversial topics by providing an objective lecture supported with a PowerPoint. I think it is important to introduce the controversial topic and let the audience form their own opinion of the subject.” and “I would prefer a completely unbiased presentation of both sides, probably in a list of facts such as on a PowerPoint or in a lecture; but as long as neither view point is pushed on me.”  The preference for an objective, teacher centered approach is consistent with cognitive dissonance theory (Festinger, 1957), which asserts people crave information to be presented in a way that does not conflict with personal convictions. A non-persuasive lecture void of discussion allows students to diminish the importance of dissonant information.

Research question four addressed how students perceived the strategy of guided group discussion for learning about controversial issues. The general impression was that students enjoyed the learning environment created by the requirement of maintaining neutrality. Most enjoyable was the objectivity and evidence-based component of the discussion:

The thing I enjoyed most was being able to have a comfortable conversation with classmates without being at each other’s throats over some controversial issues. I didn’t grow up on a farm, nor do I have strong opinions on sustainable agriculture, but I could tell some people in the class did, so if we had more of a debate, I would expect there to have been much more conflict.

Another participant responded:

I liked the fact that it was objective and not just people spewing out their opinions. Everything had to be backed up with evidence, which should always be the case, but often times aren’t in debate or other opinionated discussion.

While students appreciated the nonthreatening environment, maintaining a neutral position proved to be a challenging aspect of the guided group discussion. Students reported that the inability to state their own opinions, and the domination of conversation by other students was frustrating:

It was difficult to stay neutral and it was hard to verbalize negative aspects of sustainable agriculture because there were not many negatives found during researching the topic. I found it frustrating to not be able to clearly state your side.

One participant commented, “It was difficult to be neutral on the topic of animal welfare. I also didn’t like how I had a hard time butting in to talk when three people in my group dominated the conversation.” In spite of these frustrations, students agreed that the teaching strategy was beneficial. Requiring students to research the topic before the discussion and providing questions to guide their research efforts helped engage students in the learning process. One participant wrote, “I still feel the same way about the topic, however I have gained a greater appreciation for sustainable agriculture. I feel quite strongly against sustainable agriculture, however after learning more, I did appreciate it more.” Another participant commented:

I certainly felt that the research was the most informative part of this assignment. I put a lot of time into the research so that I could fully understand both sides of each of the questions posed. The guiding questions were very good because they covered a wide range of animal welfare issues and required that we explore each of the aspects, including those that we may not have considered on our own.

A student concluded, “It really made me see both sides and look at the topic open minded.” From an observation standpoint, students commented that watching their classmates engage in the discussion was educational, exposing different viewpoints. The role of observer also made students aware of how telling facial expressions and body language can be in a group discussion. While they enjoyed observing, students often found this role difficult, expressing a desire to join the conversation. According to one student, “I really wanted to jump in to the discussion when we listened to the other group.” Interestingly, observing students noticed when those discussing the topic held a certain opinion based on their nonverbal communication – “It was very clear when some people disagreed with what was said because of their facial expression and body language.” Observing the participation also had the benefit of exposing students to unknown knowledge: “When observing the other group’s discussion, I was surprised at the facts and figures the group gave. I had no idea about the topic and the concrete evidence was very clarifying.” Students valued the factual evidence that was presented during the discussion. One observing participant reported, “I thought that observing another group was very beneficial to me because I was able to gather a lot of unbiased information on the topic and was able to create my own unbiased view on their topic.”

Conclusions, Implications, and Recommendations

Guided group discussion is characterized by an instructor identifying a controversial topic, creating questions to guide students through investigating all aspects of the topic before a discussion, and designing a structure for group discussion that requires participants to speak from the supporting and opposing side of a topic while asking questions of other participants. The guided group discussion technique may help teachers feel comfortable facilitating the learning about controversial topics, as this approach removes instructors from the possibility of impacting conceptual change due to not maintaining a neutral position. Participating in a guided group discussion about controversial issues has many implications in support of this instructional method as an approach to cognitive dissonance and conceptual change. First, participating in and observing a guided group discussion encourages students to consider both sides of an issue which might not occur in a lecture format. The action of researching both sides of an issue encourages student learning at higher levels of Bloom’s Taxonomy (Anderson et al., 2001), where examining data, organizing ideas, and preparing for a discussion require analyzing ideas and evaluating positions. The highest level of Bloom’s Taxonomy, creation, is reached when students compare the different points of discussion, evaluate the information, and then construct their own position when presenting a closing statement where an argument toward the controversy is presented and supported by garnered knowledge. Secondly, guided group discussion allows the teacher to maintain neutrality and avoid bias when providing instruction on controversial issues, which Cotton (2006a) explained is unfeasible. This frees the teacher from struggling to not employ a personal agenda and creates an autonomous learning environment for the students, which gives way to a third advantage of guided group discussion – teacher protection from responsibility of conceptual change. The CRKM (Dole & Sinatra, 1998) suggests that conceptual change is unlikely with low engagement; however, the possibility of conceptual change increases when engagement is high. As the guided group discussion technique accelerates high engagement for students participating in the discussion, conceptual change is possible. In the present study, if conceptual change had occurred, students would have been responsible for their individual conceptual change, not the instructor, concluding that guided group discussion as an instructional approach to controversial issues relieves the teacher from responsibility of conceptual change that may occur amongst students.

While the data did not show a significant difference between the effects of participating in or observing a group discussion on attitude toward controversial issues, further research with additional groups of varying sizes, populations, and topics is recommended. In addition, we recommend that modifications be made in future studies to provide for the collection of evidence that each student conducted background research prior to the discussion. Evidence could be in the form of written responses to the guiding questions, an outline that explains key findings during research, or a conceptual model designed by students that highlights information discovered, for example. Another recommendation is the provision of equitable talk time amongst participants. During the group discussions, some students dominated the conversation while others were more passive in their participation. Brookfield and Preskill (1999) posit that participation in discussion will help students develop a more critical understanding of and appreciation for diverse viewpoints; therefore, ensuring equal participation by each student in the discussion is critical for maximum benefit of the small group discussion. Additionally, equitable treatment, regarding amount of time spent discussing the various characteristics of controversy, is encouraged. Early in the discussions, students spent much of the allotted time focusing on a few specific areas of controversy, resulting in less time for discussing additional aspects. Not providing time for discussing the topics from multiple angles and viewpoints may limit students in their ability to form an opinion on the issue. Students who participated in the discussion recommended more preparation before the activity occurs. One student remarked, “I really don’t have any more recommendations, other then maybe explaining how it’s done a little more in depth then what we covered in class.” Another student indicated:

Since this was our first discussion I would have liked to do a dry run and gone over what things were going to be said and get a feel on what things impacted the memebers the most. I am not quick at thinking off the top of my head and I felt like that was a huge drawback for me with this exercise and I feel like I really didn’t do a good job on the task at all.

We acknowledge limitations of this study related to the population and sampling. Findings are limited to the case site under investigation and cannot, therefore, be generalized to a larger population. This limitation could not be overcome using the chosen method because data collection required adapting course syllabi and curricula, and therefore, other case sites willing to accommodate such required adaptations were not identified. Also, the student sample for the qualitative data collection only represented perspectives from students in the spring semester. It is possible that perspectives of students from the fall semester could differ; however, because the course is required for all students pursuing an undergraduate degree in an agricultural field at the university where the research took place, we determined the sample was representative of the total population under investigation.

In future studies, we recommend that discussions be recorded, transcribed, and analyzed for the frequency of statements in support of or opposition to a topic, as this might have an impact on posttest scores. This type of analysis would identify possible inequity in treatment to the topic being discussed if significantly more comments were in favor or spoke against the issue. Future research should include both quantitative and qualitative measurements of cognitive dissonance, student engagement, and student value of the learning experience. Finally, future investigations should occur at multiple case sites to allow for enhanced generalizability.


Agbaje, K., Martin, R., & Williams, D. (2001). Impact of sustainable agriculture on secondary school agricultural education teachers and programs in the north central region. Journal of Agricultural Education, 42(2): 38-45.

Allahyari, M. S., Chizari, M., & Homaee, M. (2008). Perceptions of Iranian agricultural extension professionals toward sustainable agriculture concepts. Journal of Agriculture and Social Sciences, 4(3): 101-106.

Anderson, L. W., & Krathwohl, D. R. (Eds.) (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.

Asimeng-Boahene, L. (2007). Creating strategies to deal with problems of teaching controversial issues in social studies education in African schools. Intercultural Education, 18(3): 231-242.

Bennett-Wimbush, K., Ambstutz, M. D., & Willoughby, D. (2015). Student perceptions of animal use in society. NACTA Journal, 59(2), 134-138.

Bernard, H. R. & Ryan, G. (2010). Analyzing qualitative data – Systematic approaches. Sage.

Bogdan, R., and Biklen, S. KI. (2003). Qualitative research for education: An introduction to theory and methods. Allyn and Bacon.

Brookfield, S. D., & Preskill, S. (1999). Discussion as a way of teaching: Tools and techniques for democratic classrooms.Jossey-Bass.

Broom, D. M. (1991). Animal welfare: Concepts and measurement. Journal of Animal Science, 69(10), 4167-4175.

Byford, J., Lennon, S., & Russell, W. (2009). Teaching controversial issues in the social studies: A research study of high school teachers. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 82(4): 165-170.

Cotton, D. (2006a). Implementing curriculum guidance on environmental education: The importance of teachers’ beliefs. Journal of Curriculum Studies, 38(1): 67-83.

Cotton, D. (2006b). Teaching controversial environmental issues: Neutrality and balance in the reality of the classroom. Educational Research, 48(2): 223-241.

Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. In A. Tashakkori and C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 209–240). SAGE Publications Ltd.

Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. D. C. Heath.

Dole, J., & Sinatra, G. (1998). Reconceptualizing change in the cognitive construction of knowledge. Educational Psychologist, 33(2/3), 109-128.

Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.

Fiske, E. P. (1991). Controversial issues as opportunities. Journal of Extension, 29(3).

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine De Gruyter.

Goodwin, J. (1993). Contrasting viewpoints about controversial issues. Journal of Extension, 31(3), 1-4.

Guba, E. G. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries. Educational Communication and Technology Journal, 29(1), 75-91.

Harwood, A. M., & Hahn, C. L. (1990). Controversial issues in the classroom. Office of Educational Research and Improvement.

Herzog, H. A., & Mathews, S. (1997). Personality and attitudes toward the treatment of animals. Society & Animals, 5(2), 169-175.

Herzog, H. A., Betchart, N. S., & Pittman, R. (1991). Gender, sex role identity and attitudes toward animals. Anthrozoös, 4(3), 184-191.

Hess, D. E. (2009). Controversy in the classroom: The democratic power of discussion. New Routledge.

Ho, L., McAvoy, P., Hess, D., & Gibbs, B. (2017). Teaching and learning about controversial issues and topics in the social studies: A review of the research. In M. Manfra, & C. Bolick (Eds), The Wiley handbook of social studies research (pp. 319-335). John Wiley and Sons.

Howell, D. C. (2007). Statistical methods for psychology. Thomson Wadsworth.

Ikerd, J. E. (2008). Crisis & opportunity. University of Nebraska Press.

Lewin, K. (1952). Group decision and social change. In G. E. Swanson, T. M. Newcomb, & E. L. Hartley (Eds.), Readings in social psychology (rev. ed., pp. 197-211). Holt.

Murphy, K., & Davidshofer, C. (1988). Psychological testing: Principles and applications. Prentice-Hall.

Murphy, K., & Mason, L. (2006). Changing knowledge and beliefs. In Alexander P. A. and P. H. Winne (eds). Handbook of educational psychology (pp. 305-324). Erlbaum.

Nordstrom, P. A., Richards, M. J., Wilson, L. L., Coe, B. L., Fivek, M. L., & Brown, M. B. (2000). Assessing student attitudes toward animal welfare, resource use, and food safety. Journal of Agricultural Education, 41(3), 31-39.

Owens, D. C., Sadler, T. D., & Zeidler, D. L. (2017). Controversial issues in the science classroom. Phi Delta Kappan, 99(4): 45-49.

Poole, D. H., Moore, J. A., & Lyons, S. E. (2016). Changes in student perception of food animal agriculture following discussion of controversial topics. NACTA Journal, 60(3), 313-317.

Robertson, E. (2018). Teaching controversial issues in American schools. Democracy & Education, 26(1), 1–3.

Soley, M. (1995). If it’s controversial, why teach it? Social Education, 60(1): 9-14.

Terry, R., & Lawver, D. E. (1995). University students’ perceptions of issues related to agriculture. Journal of Agricultural Education, 36(4), 64-71.

Werner, C. M., & Stanley, C. P. (2011). Guided group discussion and the reported use of toxic products: The persuasiveness of hearing others’ views. Journal of Environmental Psychology, 31(4), 289-300.

Werner, C. M., Sansone, C., & Brown, B. B. (2008). Guided group discussion and attitude change: The roles of normative and informational influence. Journal of Environmental Psychology, 28(1), 27-41.

Wisdom, J., & Creswell, J. W. (2013). Mixed methods: Integrating quantitative and qualitative data collection and analysis while studying patient-centered medical home models [AHRQ Publication No. 13-0028-EF]. Agency for Healthcare Research and Quality.

Zimmerman, J., & Robertson, E. (2017). The case for contention: Teaching controversial issues in American schools. University of Chicago Press.

Assessing Teacher Practices Related to Precision Agriculture in Secondary Agriculture Education

Abigail E. Heidenreich, Purdue University Cooperative Extension,

Christopher A. Clemons, Auburn University,

James R. Lindner, Auburn University,

Wheeler Foshee, Auburn University,

PDF Available


Agricultural education was designed to reflect the agriculture industry, and since the recent increase in technology use in the industry, little research has been done to investigate what agricultural technologies are used in secondary agriculture classrooms. Secondary agriculture instructors in Alabama and Illinois participated in this study and provided descriptive data about their personal characteristics and their decision to incorporate precision agriculture, as well as barriers that prevent them from incorporating precision agriculture concepts. This study identifies the curriculum involving precision agriculture that is currently being taught and gains insight into teachers’ decisions to integrate precision agriculture in their classrooms. Teachers indicated the importance and relevance of precision agriculture, but only half of the participants incorporate related concepts into their curricula. A Chi Square test revealed no significant relationships between the personal characteristics of teachers and their decision to incorporate precision agriculture concepts. The most important topics in precision agriculture were identified by participants as: GPS, Soil Sampling/Land Management and Genetic Modification. Teachers indicated a need for professional development or teacher education focused on precision agriculture in multiple fashions and supports the need for similar education in the agriculture industry.


Agriscience education is a lifelong journey of instilling foundational content skills, developing experiential learning opportunities for a well-trained 21st century student, and focused professional development for the agriscience teaching profession. Secondary agricultural educators have consistently demonstrated interest and value in promoting agricultural technologies for student learning. Agriscience educators have been urged to push the bounds of instructional innovation for over 115 years as reported by Wallace’s Farmer (1908) and cited by Hillison (1995) “if the director could introduce the teacher to lay aside the book and present problems likely to come up in farm life, it would tend to make a good deal better farmer[s] out of the next generation” (p. 8). Although the statement was limited to traditional agriculture students of the early 1900’s the pragmatic context is just as profound today. Understanding practices and rationale for the inclusion of precision agriculture content in new and existing curricula may serve as a model for other programs and schools seeking to enhance the practicality of today’s modern agricultural education classroom. Precision agriculture inclusion is a vital component of instructional innovation in the secondary agriscience education classroom (Palak & Walls, 2009) and requires a unique set of teaching and learning competencies to reflect historical agricultural changes (Ruffing, 2006).  Identifying the tenets that promote or inhibit the adoption of innovation (Rogers, 2003) relating to precision agriculture content in the secondary agriscience classroom is vital to continued growth and success of global agriculture. Glenn (1997) wrote “public support for technology instruction is strong and vocal, and there is an expectation that no school can prepare students for tomorrow’s society of new technologies are not available for students” (p. 123). To understand the perceptions of secondary agriscience education teachers’ instruction of precision agriculture we need to understand the rationale in which precision agriculture content is embedded in agriscience curricula. Identifying the relevance of precision agriculture in existing course pathways will explain the perceived importance of precision agriculture instruction to secondary agriscience teachers. Determining the perceptions and barriers of the curricula associated with precision agriculture instruction as described by Kotrlik et al. (2003) may identify detractors limiting precision agriculture content in agriscience courses.

McBratney et al. (2005) defined precision agriculture as “the[sic] kind of agriculture that increases the number of (correct) decisions per unit of area of land per unit of time with associated net benefits” (p. 8). Precision agriculture has been characterized as using standardized methods such as crop rotation and fertilizer application to increase yields. Advances in information technology have created the opportunity to farm in a more customizable way that allows agricultural producers to make informed management decisions (Lowenberg-DeBoer, 2015). Consumer demands for efficiency and environmental conscientiousness in agriculture have reduced inputs, increased efficiency, and improved yields. These practical applications shape the agricultural industry into a sustainable and efficient production model for the growing population. Global positioning systems, soil mapping, variable rate planting, unmanned aerial vehicles, and yield mapping represent new and emerging technologies and serve as an opportunity for inclusion in secondary agriscience education classrooms. Kotrlik et al. (2003) reported the difficulty of integrating technology instruction in secondary agriscience education classrooms as difficult, time consuming, and resource intensive.

The training and education for consumers of technology as well as specialists who are able to install, troubleshoot, maintain, educate, and develop emerging technology is increasing in demand. Kitchen et al. (2002) reported a lack of sufficient and effective education opportunities for producers, teachers, and students of precision agriculture technologies exist in modern training programs. Existing research has described the many challenges of technology adoption among agricultural producers, agriscience teachers, and students (Ertmer, 1999; Redmon et al., 2003; Smith et al., 2018). As new technologies emerge instructional methods must evolve to ensure career readiness for agriscience education students. Budin (1999) stated technology instruction should be reconceptualized regarding the specifics of how technology fits in the curriculum knowledge requirements for instructional delivery, and the assessment of technology instruction for students learning. Wood et al. (2005) identified five factors attributed to the hesitation of integrating and adopting technology in the secondary agriscience classroom: lack of support, restricted technical access, student application issues, technical problems, and teacher’s attitudes and perceptions integrating technology in curricula. The benefits of incorporating technology in the secondary classroom have been studied extensively (Gorder, 2008) while Clemons et al. (2018) reported professional development in agricultural technology and STEM instruction was a continuing area of need for secondary teachers.  Educational integration of precision agriculture and STEM applications in secondary agriculture classrooms benefit students through practical application and career preparation. Prior research indicated positive relationships between the use of STEM and agricultural classrooms. Smith et al. (2015) outlined the relationships between STEM and agriculture, noting that “agriculture teachers are confident in their ability to integrate science concepts…students who engage in math integrated agricultural power and technology class scored higher on a postsecondary math placement test” (p. 182-201). Many agriculture teachers unknowingly incorporate STEM into the existing agriscience education curricula . As agricultural technologies emerge, finding ways to incorporate precision agriculture topics that include STEM principles will not pose a challenge to teachers (Stubbs & Myers, 2016).

Although teacher self-efficacy regarding best pedagogical methods for STEM instruction could be an issue requiring enhance professional development. Many precision agriculture concepts already encompass science, technology, engineering, and mathematics. The combination of technology uses to solve specific problems are endless; engineering and mathematical components of precision agriculture technologies are necessary for the technology itself to function and can easily be investigated by students in a variety of settings and course topics. The flexibility of precision agriculture technologies across topics and educational structures is an enormous asset to teachers who choose to incorporate them into their coursework.

Conceptual Framework

Rogers’ (2003) Diffusion of Innovation Theory is comprised of four components: innovation, communication channels, time, and social systems. The innovation element of Diffusion of Innovation Theory is composed of ideas that are considered new or emerging practices. Technology concepts are often innovative ideas and follow the Diffusion of Innovation Theory as people develop new ways to utilize technology.

Rogers (2003) discusses how homophily and heterophily affect the spread of ideas, stating that ideas flow more freely among homophilous individuals: individuals who are similar and work together towards mutual goals. Heterophilous individuals tend to be quite different from each other and therefore have a more difficult time communicating and agreeing on the importance of ideas and innovations. Time is considered by Rogers (2003) to be the measurement tool of the entire process of learning about innovations to adopting them. The innovation-decision process consists of an individual’s course of learning over time that begins with learning of an innovation, learning about the innovation, forming an opinion on the innovation, and results in either adoption or rejection of the innovation (Rogers, 2003).

Social systems are the final component of Rogers’ Diffusion of Innovation Theory. Social systems can be characterized as networks of individuals or units working together to accomplish a common goal, often groups of people or organizations. The leadership of some individuals or the normality of the group affect the flow of information and how it reaches individuals (Rogers, 2003). This element shares many characteristics with the idea of human capital, which describes how individual’s professional and personal networks affect their decision-making process (Hunecke et al., 2017).

Rogers’ Diffusion of Innovation Theory develops the process of innovation adoption and describes how groups of individuals within a social system can be identified based on the time it takes them to adopt innovations and the attributes that commonly affect their decision-making process. These categories are innovators, early adopters, early majority, late majority, and laggards (Rogers, 2003). Innovators, individuals who are comfortable with uncertainty, are capable of higher-level thinking in regards to concept application. Early adopters are characterized as being slightly more contemplative than innovators and, are led by their opinions on the innovation and, evaluate the innovation subjectively. Individuals that comprise the early majority group rarely lead the way and are willing to adopt innovations. Early majority individuals often take longer than both innovators and early adopters to contemplate adoption of innovations and rely on their predecessor adopters for signs of success. Late majority adopters are cautious by nature and rely heavily on social norms to sway their decisions. They require little uncertainty surrounding the innovation in question. In comparison, laggards are the last group to adopt innovation. Laggards resist innovation adoption and often doubt the success of an innovation, exercising acute caution in the decision-making process.

The characteristics of innovation adopters described in Rogers’ (2003) Diffusion of Innovation Theory are similar to attributes that influence decisions, intentions, and behaviors described in Fishbein and Ajzen’s (1975) Theory of planned behavior. Fishbein  and Ajzen (1975) stated that an “individual’s intention to perform a behavior (behavior x) is influenced by their attitudes towards that behavior as well as their beliefs about the consequences of that behavior” (p. 16). Intention to perform a behavior (behavior x) is also influenced by subjective norms and normative beliefs about that behavior (Fishbein & Ajzen, 1975). The confluence of these theories considers behavior x to be the adoption of an innovation or idea.

Individuals are influenced by their attitudes and beliefs towards adopting new innovations (Fishbein & Ajzen, 1975). A similar example could be found with the opposite result, utilizing an innovator or early adopter as the instructor or individual. This individual’s attitudes and beliefs towards adopting new ideas are positive, therefore they are more likely to incorporate precision agriculture technology into their coursework.

Purpose and Objectives

This study investigated agriscience teacher perceptions of curriculum involving precision agriculture technology and their insights regarding the integration of agricultural technology curriculum. The objectives of this study were: describe the courses and curriculum currently being used to teach precision agriculture concepts, describe the most important topics in precision agriculture and the relevance of precision agriculture in the areas of education and agriculture, describe potential relationships between participant personal characteristics and their incorporation of precision agriculture concepts in their classrooms, and describe the barriers that may prevent teaching precision agriculture.


The target population for this study were certified agriscience teachers in Alabama (N = 302) and Illinois (N = 391). Participants were identified using a contact data base provided by the professional agricultural education organizations in each state. Participants were randomly selected from each state using Cochran’s (1977) theorem, Alabama (n = 169) and Illinois (n = 196) for appropriate sample size. Characteristics of the study participants included 60 male teachers (73.2%) and 22 female (26.8%) teachers with 69 (84.1%) indicating rural school location, 12 (14.6%) from suburban schools, and 1 (1.2%) from urban areas. Participants’ teaching experienced ranged from 0-5 years (n = 22, 26.8%) 6-10 years (n = 21, 25.6%), 11-20 years (n = 16, 19.5%), 21-30 years (n = 18, 22) and greater than 30 years (n = 5, 6.1%). The final questionnaire was distributed to 373 participants with (n = 37) from Alabama and (n = 36) from Illinois yielding a 24.00 questionnaire response rate. Three attempts were made through email and two telephone conversations to increase the response rate. The total response rate was 88 completed questionnaires; however, 15 participants did not indicate their state. Non-response bias was addressed through oversampling 20% of the available population. Using Lindner et al. (2001) method 3 analysis and comparison of early versus late respondents was conducted using a t-test (Table 1) which did not identify any significant differences between timing and data results.

Table 1
Comparison of Early and Late Respondents
StatementtdfSig 2-tailed
Relevance of Precision Ag in Agriculture Job Market1.6479.11
Relevance of Precision Ag in Coursework/Content0.8280.42
Relevance of Precision Ag in Agriculture Industry0.7580.45
Relevance of Precision Ag in Classroom Technology.7080.48
Incorporation of Precision Agriculture0.00861.00

The review of existing literature did not identify an instrument appropriate for this study. Development of the questionnaire was completed by the researcher, two academic faculty in the agriscience education field, and an agriculture and natural resources extension agent. During the development of the instrument, fifteen preliminary research statements were selected to address participant perceptions of precision agriculture and potential barriers to incorporating precision topics and eighteen items to collect participant characteristics. Internal validity of the questionnaire was addressed by the instrument development team and eight individuals were selected to participate in a pilot test from (N = 4) from Alabama and (N = 4) from Illinois. Pilot study participants were representative of the population, but were not included in the sample of the population utilized for the study. The individuals selected to provide feedback in the pilot test were selected by the researcher based on their knowledge of research and their likeliness to provide honest and applicable analysis of the instrument. Pilot study participants were asked to review the instrument and provide input on the potential ambiguity of statements, sentence structure and other changes that may be necessary. Results from the pilot test indicated necessary changes to the instrument including ambiguity of specific statements and organization of the overall survey instrument.  The analysis of data utilized descriptive statistics for describing the sample personal characteristics in each state. Borich’s (1980) analysis was conducted to measure participants confidence and level of importance related to precision agriculture concepts and willingness to include precision agriculture in the curriculum. To further understand if a relationship existed between observed categorical values and theoretical expectations, a Chi Square for Goodness of Fit analysis was conducted.


Objective one sought to describe the courses and curriculum containing units or lessons pertaining to precision agriculture. Participants indicating their incorporation of precision agriculture were asked to identify courses they teach which contain units or lessons pertaining to precision agriculture (Table 2). The results indicated that 27 respondents (61.0%) incorporate precision agriculture concepts in introduction to agriculture,16 (36.0%) in agribusiness courses, and n =13 (30.0%) incorporated precision agriculture in horticulture classes. Other courses were identified by respondents as agronomy, ag science, general agriculture, plant biology, ag sales and marketing, physical science applications in agriculture, crop and soil science, and advanced agriculture. Animal science and agricultural construction accounted for n = 8 (18.0%) respectively. Agricultural leadership courses, n =  5 (11.0%), forestry courses, n = 4 (9.0%), aquaculture courses, n = 2 (5.0%), and n = 11 (2.0%) in Cooperative Classes reported significantly less incidences of curriculum inclusion.

Table 2
Courses representing the inclusion of units or lessons pertaining to precision agriculture.
Courses in Agriscience Classrooms containing Precision Agriculturef%
Introduction to Agriculture2761
Ag Mechanics1330
Animal Science818
Ag Construction818
Agricultural Leadership511
Cooperative Class12

Participants were organized according to their incorporation of precision agriculture instructional materials within existing curricula (Table 3). The greatest number of participants (n = 26, 59.0%) indicated self-created curriculum materials were used for the instruction of high school agriscience teachers. Online resources (n = 23, 52.0%), hands on technology (n = 20, 45.0%), textbook (n = 14, 32.0%), purchased/packaged curriculum (n = 10, 22.0%), and “other” (n =3, 6.0%) were indicated as resources. The use of simulators (n = 2, 4.0%) was the least used type of instructional materials.

Table 3
Curriculum Resources Most Often Utilized In Precision Agriculture Instruction
ResourcesResources most commonly used
Self-created curriculum2659.0
Online resources2352.0
Hands on technology2045.0
Purchased/packaged curriculum1022.0

Participants indicated their perceptions related to the future of precision agriculture instruction. Participants were provided four statements (Table 4) pertaining to education in their classroom or in agriculture and indicated their opinion of relevance 5-10 years in the future on a scale from 5, extremely relevant, 4, somewhat relevant, 3, no change in relevance from today, 2,  somewhat irrelevant, and 1, extremely irrelevant. Participants perceptions of future instruction in precision agriculture were overwhelmingly relevant for future employment opportunities for students. In their classroom technologies, 96% of teachers indicated that precision agriculture topics were either extremely relevant or somewhat relevant. Teacher perceptions of the most important topics involving precision agriculture were analyzed depending on their incorporation of precision agriculture concepts in their classrooms. Teachers who indicated their incorporation of precision agriculture in their classrooms identified the most important topics as 20.9% (GPS), 20.9% (soil sampling or land management), 14.0% (variable rate technology), 9.3% (yield monitoring), 4.7% (automated production Systems), 4.7% (unmanned aerial systems or vehicles), 23.3% (genetic modification), 2.3% (chemical technology), and 0% (Satellite Imaging).

Table 4
Topics of Secondary Agriscience Instructional Importance in Precision Agriculture
Topics of ImportanceIncorporate ConceptsDo Not Incorporate Concepts
Global Positioning Systems920.91128.9
Genetic Modification1023.3821.1
Soil Sampling/Land Management920.9718.4
Variable Rate Technology614.0410.5
Yield Monitoring49.325.3
Automated Production Systems24.725.3
Unmanned Aerial Systems/Vehicles24.725.3
Satellite Imaging0025.3
Chemical Technology12.300

Teachers indicating no inclusion of precision agriculture concepts in their classrooms identified the most important topics (Table 5) in precision agriculture as: 28.9% (GPS), 21.1 % (genetic modification), 18.4 % (soil sampling or land management), 10.5 % (variable rate technology), 5.3 % (satellite imaging), 5.3 % (yield monitoring), 5.3 % (automated production systems), 5.3 % (unmanned aerial systems or vehicles). Participants not currently teaching precision agriculture reported chemical technology as not important for instruction.

Table 5
Participants Perceptions of Future Relevance of Precision Agriculture Topics
 Extremely RelevantSomewhat RelevantNo Change in RelevancySomewhat IrrelevantExtremely Irrelevant
Areas of Relevancef%f%f%f%f%
Agriculture Industry72881012000000
Agriculture Employment61752025000000
In Classroom Technologies46563340340000

A chi-square goodness of fit test was used to identify the potential for relationships that may exist between the participants’ personal characteristics and their decision to incorporate precision agriculture concepts into their curricula (Table 6). A significant relationship would signify that a personal characteristic would have an effect on their decision to incorporate precision agriculture. The results of this objective did not identify any significant relationships between participant personal characteristics and their decision to incorporate precision agriculture concepts into their curricula.

Table 6
Contingency Table by Personal Characteristics and Incorporation of Precision Agriculture Topics
Personal characteristicsndfSig
Years teaching824.87
Education level822.42
School location822.34
Student enrollment812.13

Conclusions, Implications, and Recommendations

Agriculture teachers indicated limited integration (50.0%) of precision agriculture instruction within their existing curriculum. This finding supports Rogers (2003) findings that individuals will adopt innovative approaches in a timely manner, while others may resist implementation because of doubt related to the potential for success of the innovation. Participants (50%) in this study reported their interest in curricula integration would best be described as innovators; those demonstrating higher order thinking in relation to adoption of concepts to application. Participants indicating the incorporation of precision agriculture concepts occurred in traditional secondary agriscience courses: introduction to agriculture, agribusiness, ag mechanics, and horticulture. In comparison, participants identified potential barriers to integration: funding, equipment, curriculum, experience, and professional development. These findings support the barriers of technology reported by Wood et al. (2005).

The participants indicated their most important topics in precision agriculture as GPS, soil sampling/land management, and genetic modification. Teachers also described the resources they use as their curricula, resulting in the most common resources being self-created curriculum (59%), online resources (52%), and hands-on technology (45%). When asked about their perception of the future relevance of precision agriculture in their classrooms and in their coursework, participants indicated extremely relevant, somewhat relevant or “no change in relevance from today. Fishbein and Ajzen (1975) postulated the means in which individuals’ intention to perform a behavior is influenced by their attitudes towards the consequences of the behavior. The conclusions of this study support the theory of planned behavior at the various levels of integrating precision agriculture related curricula. When asked the relevance of precision agriculture topics in the agriculture industry and in the agriculture job market, participants reported precision agriculture being extremely relevant or somewhat relevant in 5-10 years. Participant support coupled with forecasting future trends in agricultural employment arenas tended to be positive. Participants perceived value in precision agriculture curricula while respecting the role this content will have in their student’s future employment.

Participants identified the most important lessons for integrating precision agriculture curriculum regardless of their decision to incorporate precision agriculture into their curricula: GPS, Soil Sampling/Land Management, and Genetic Modification. Participants were asked to indicate the relevance of precision agriculture 5-10 years in the future in four areas: (in your classroom, in your coursework/content), 61.0% indicated their perception of precision agriculture as “extremely relevant” while 39.0%indicated precision agriculture being either “somewhat relevant” in the future or “no change in relevance from today. Participants overwhelmingly agreed (75.0%) that precision agriculture will be valued in the agriculture industry and in the agriculture job market while 25.0% indicated precision agriculture being somewhat relevant 5-10 years in the future. Participants indicating hesitation to the level of relevance may be reflective of Wood et al. (2005) suggesting factors associated with technology integration in agricultural education programs.

It is recommended that further research focusing on precision agriculture in agriculture education be conducted. This recommendation supports the findings of Glenn (1997) by advocating for public support related to technology instruction in public school systems. As the agriculture industry grows and advances, so should agriculture education and research efforts. The identification of possible content areas or educational concepts to better prepare students entering careers in precision agriculture, should be investigated and include individuals currently pursuing careers in precision agriculture. By comparing the education patterns of those who currently hold careers in precision agriculture, preparatory education could become more specific and therefore more beneficial to those wishing to enter a career in precision agriculture.

A need exists for professional development and teacher education focusing on precision agriculture and was supported by Palak and Walls (2009). Future studies should identify specific areas within precision agriculture that would be most beneficial to teachers and, in turn, their students. This recommendation may be limited by what (Ertmer, 1999; Redmon et al., 2003; Smith et al., 2018) reported as challenges associated with technology adoption among agriculturalists. A compilation of resources for teachers to use in building curriculum is needed. Reliable information that is accurate and representative of what occurs in the agriculture industry should be gathered and presented to teachers for use in their classrooms and should be updated annually to best reflect the technologies used in the agriculture industry. Similarly, partnerships between agriculture education and the companies that specialize in precision agriculture technologies should be formed so that teachers are equipped with the tools needed to educate their students. These industry partners are imperative to keeping secondary agriculture education relevant and sparking the interest of students to work in the agriculture industry.

Precision agriculture is a progressive and emerging topic in agriculture that is facing farmers with the decision to either move with the flow of technology or get left behind. Many people within agriculture, let alone outside the field, do not understand precision agriculture or what it entails. This leads to confusion, misinformation and general misconceptions surrounding the topic of precision agriculture, which underlines the importance of familiarizing future agriculturalists with the precision-rich agricultural future they are to inherit. Precision agriculture can be identified in various arenas: innovative technology development and the application of technology in real life. We, as agricultural educators, must do our part in educating agriculturalists on the best practices for applying this emerging technology to its respective goal. Secondary agriculture educators work with students who live in this agriculturally rich world every day, they are our connection to the future of agriculture. By incorporating precision agriculture technologies that are already being used in agriculture, students will be better prepared.

Teaching students to care for the environment is becoming prevalent in secondary agricultural education curriculum and precision agriculture content would be the next evolutionary step. Environmental science and stewardship practices define a component of production agriculture education and the inclusion of concepts which provide data based and technological components reinforce environmental science curriculum. The implications of combining precision agriculture and environmental science will aid in the development of students STEM processes and the ability to implement STEM practices in a meaningful and productive manner. Continuing education for practicing agricultural education teachers should contain concepts and instruction in precision agriculture. Professional development opportunities would allow teachers to become more comfortable with the content and in the development of standalone modules or incorporation of precision agriculture concepts within existing curriculum. Agricultural education teachers should be provided pre-service training through Colleges of Agriculture or preparatory work in Colleges of Education as familiarity with the content would reduce anxiety and doubt for younger teachers and give direction to veteran teachers looking to update their course materials.


Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. Journal of Teacher Education, 31(3), 39-42.

Budin, H. (1999). The computer enters the classroom. Teachers College Record, 100(3), 656-699.

Clemons, C. A., Heidenreich, A. E., & Lindner, J. R. (2018). Assessing the technical expertise and content needs of Alabama agriscience teachers. Journal of Agricultural Education, 59(3), 87-99.

Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: Explaining an apparent paradox. American Educational Research Journal, 38(4), 813–834.  

Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47– 61.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to Theory and research (Addison-Wesley Series in Social Psychology). Addison-Wesley.

Gorder, L. M. (2008). A study of teacher perceptions of instructional technology integration in the classroom. Delta Pi Epsilon Journal50(2), 63-76.

Glenn, A. D. (1997). Technology and the continuing education of classroom teachers. Peabody Journal of Education, 72(1), 122-128.

Hillison, J. (1995). The Coalition that Supported the Smith-Hughes Act or a Case for Strange Bedfellows. Journal of Vocational and Technical Education11(2), 4-11.

Hunecke, C., Engler, A., Jara-Rojas, R., & Poortvliet, P. M. (2017). Understanding the role of social capital in adoption decisions: An application to irrigation technology. Agricultural Systems, 153, 221-231.

Kitchen, N. R., Snyder, C. J., Franzen, D. W., & Wiebold, W. J. (2002). Educational needs of precision agriculture. Precision Agriculture3(4), 341-351.

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42(4), 43-53.

Lowenberg-DeBoer, J. (2015). The precision agriculture revolution. Foreign Affairs, 94(3), 105-112.

McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7-23.

Palak, D., & Walls, R. T. (2009). Teachers’ beliefs and technology practices: A mixed-methods approach. Journal of Research on Technology in Education, 41(4), 417–441.

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6.

Redmon, D. H., Kotrlik, J. W., & Douglas, B. B. (2003). Factors related to technology integration in instruction by marketing education teachers. Journal of Career and Technical Education, 19(2), 29–46.

Rogers, E. M. (2003). Diffusion of innovations. Free Press.

Ruffing, K. (2006). The history of career clusters. (State Career Clusters Initiative).

Smith, E. H., Stair, K. S., Blackburn, J. J., & Easley (2018). Is there an app for that?: Describing smartphone availability and educational technology adoption level of Louisiana agricultural educators. Journal of Agricultural Education, 59(1), 238-254.

Smith, K. L., Rayfield, J., & McKim, B. R. (2015). Effective practices in STEM integration:

Describing teacher perceptions and instructional method use. Journal of Agricultural Education, 56(4), 182 – 201.

Stubbs, E. A., & Myers, B. E. (2016). Part of what we do: Teacher perceptions of STEM integration. Journal of Agricultural Education, 57(3), 87-100.

Wood, E., Mueller, J., Willoughby, T., Specht, J., & Deyoung, T. (2005). Teachers’ perceptions: Barriers and supports to using technology in the classroom. Education, Communication & Information, 5(2), 183–206.

Teacher Disengagement in High Stakes Learning Environments: An Ugly Data Perspective

Ashley M. Yopp, University of Georgia,

Billy R. McKim, Texas A&M University,

Yvonna S. Lincoln, Texas A&M University,

PDF Available


A lack of engagement has been reported to contribute to an ever-widening gap between how students develop knowledge, skills, and abilities and how teachers provide instruction. At the onset of this study, the purpose was to understand how depth and sequence of experience influenced student engagement, yet an emergent etic perspective surfaced. Data were collected from hundreds of hours of student interviews and observations, student and teacher reflexive journals, and classroom dialogue. Results of this study included a personal autoethnographic narrative describing the complex and unforeseen realities of (dis)engagement experienced by teachers and students. At the conclusion, it was evident the scope of the study needed to be expanded to not only describe the influence of how depth and sequence of experience engaged and, in some cases, disengaged students and teachers alike, but also the role meaningful connection plays in teaching in high stakes learning environments.


Engaged students are more motivated to learn but understanding how to engage students is a complex task (Coates, 2007). Teaching and learning are not mutually exclusive. The ability of a teacher to engage students is met with an unlimited number of extraneous variables and ever-changing policies that continuously disrupt their daily approach. Issues of student engagement become more difficult in high-stakes educational settings. According to the National Research Council (1999), the context and standards of high-stakes environments have unintended consequences that discourage teachers from improving instruction to engage students. Additionally, teachers exhibit more controlling behaviors and are less likely to use practices that support student engagement, including exploration and experimentation (Sheldon & Biddle, 1998; Bain, 2004).

The challenges of teaching today’s student require teachers to adapt to a new reality that is far from the classroom many educators experienced as students. Priority 4 of the American Association for Agricultural Education National Research Agenda (Edgar et al., 2016) included the need to understand “meaningful, engaged, learning opportunities is paramount to future learning environments,” signaling a “paradigm shift” in the way teachers prepare students for the 21st century (pg. 38). However, perceptions that foster ideas of “edutainment” and quick fixes to student engagement only create misrepresentations of the problems teachers are facing in their classrooms (Sorathia & Servidio, 2012). Teaching, without renewed perspective of learning, may create an ever-widening gap between how students develop knowledge, skills, and abilities and how teachers provide instruction. In adapting the learning environment to also “entertain” students, teachers are taking on additional responsibilities in the classroom; teachers’ past experiences and current social, emotional, and mental states can largely affect these additions. To understand engagement, holistic accounts of student and teacher experiences should be considered. Assessing both parties allows researchers to identify levels of engagement in relation to classroom culture and learning expectations.

The complex web of perspectives, approaches, and settings presents the need to understand student and teacher engagement at a basic level. Bain (2004) suggested “the best teaching cannot be found in particular practices… but in the attitudes of teachers, in their faith in students’ abilities to achieve, in their willingness to take students seriously, and let them assume control of their own learning” (p. 78-79). In line with the learning process, teachers must consider their inevitable impact on student learning – experiences, both positive and negative, will impact student learning as well as the overall classroom environment. Although research has contributed to varied components of teaching and learning individually, a collective and reciprocal understanding could illustrate possible opportunities for teachers and students to engage in any learning environment – including all five disciplinary areas of our profession (agricultural communications, agricultural leadership, school-based agricultural education, extension and outreach education, and agricultural education in university and post-secondary settings).

What is Student Engagement?

Engaged learning practices used to develop students into in-depth learners, instead of passive receptors, have been essential components of educational theory for years (Johnson et al., 2001; National Research Council, 2009). Drawing on constructivism, engaged learning requires students construct knowledge with their own experiences instead of accepting the experiences of an all-knowing teacher (Piaget, 1976). In higher education, Chickering and Gamson (1987) provided a set of principles to engage undergraduates in learning; principles include student-faculty interaction, student cooperation and reciprocity, and active learning.

Developing a specific definition of student engagement has become increasingly important as researchers and administrators work toward practices to improve student performance. Krause and Coates (2008) defined student engagement as “the extent to which students are engaging in activities that higher education research has shown to be linked with high-quality learning outcomes” (p. 493).  Similarly, Hu and Kuh (2001) defined engagement as “the quality of effort students themselves devote to educationally purposeful activities that contribute directly to desired outcomes” (p. 3). Harper and Quaye (2009) argued engagement was a more complex matter that required more than an understanding of time and effort. In their view, involvement without feeling engaged was simply compliance; students must feel an emotional connection to make meaning of their experience.

What are the Benefits of Engaging Students and Teachers?

Dewey (1938) defined the most powerful learning experiences as those that engaged the human mind in meaning-making. Dewey believed the most educative learning experiences allowed learners to solve problems and build understandings through interaction with the world around them. Although students were the primary concern of most researchers in the literature, Magolda (2005) contended they’re not the only ones to benefit from increased engagement in the learning process. The reciprocal environment constructed to engage learners fosters increased teacher engagement as well (Magolda, 2005). Although the literature is rarely focused on the benefits increased student engagement has on teachers (at any level), the benefits can be inferred. For example, increased faculty-student interaction resulted in greater job satisfaction (Bensimon & Dowd, 2009) and feelings of connectedness for faculty members (Kuh, 2009).

Although the benefits of incorporating student engagement practices are well documented in the literature, there is little to illustrate the consequences of disengagement beyond mere observation of what teachers may perceive as disengaged behaviors. Further, rarely have both student and teacher data been viewed simultaneously to understand the reciprocal nature of (dis)engagement in teaching and learning.


This study is a snapshot of a larger study describing and comparing how and when experience engages students in the learning. Although this study was an unanticipated outcome the phenomena of teacher disengagement lends insight to challenges to teaching post-secondary courses in agriculture. Therefore, the purpose of this study was to illustrate or story the phenomena of teacher disengagement as an emergent etic perspective and consequence of implementing deep, prolonged instructional experiences in a post-secondary environment.

Research question: How can implementing deep, prolonged instructional experiences in a post-secondary environment affect student and teacher (dis)engagement?

Theoretical and Conceptual Framework

Shame Resilience Theory (SRT; Brown, 2006) provided structure to understand how individuals experience shame in high stakes environments. Brown’s theory, including the shame web depicted human interaction, specifically female in current research, can be explained best by understanding the variables associated with shame and the relationship between experiences with shame and performance standards. Initially, Social Cognitive Theory (SCT; Bandura, 1986) provided bounds for data collection; SCT allowed data to be categorized by the interaction between people, environment, and behavior. By viewing personal characteristics as reciprocally altered by behaviors and environments, researchers can view people as both creators and products of their experiences and understand the way individual thoughts and feelings affect the different ways people approach the world (Bandura, 1986). In providing detailed accounts of individuals’ personal experiences, researchers can view academic experiences with a more fluid set of expectations, including experiences with shame.

Originally, data collected during student and teacher reflections were framed using SCT (Bandura, 1986) in an attempt to categorize deep, prolonged instructional experiences in a post-secondary environment using personal and environmental determinants as stable concepts or variables. After analyzing the data, Shame Resilience Theory (Brown, 2006) proved to frame the data in a more descriptive and honest way, including additional variables for behaviors recorded in the original study. Admittedly, data from the original study proved more colorful when viewed in the context of SRT. The change from SCT (Bandura, 1986) to SRT (Brown, 2006) allowed for data to be analyzed in full context of the experience with greater understanding of the connection between variables associated with shame and performance expectations within a high stakes learning environment.


This autoethnography was part of a larger study that spanned one calendar year—three academic semesters (spring, summer, and fall). Although the findings were focused on phenomena of teacher disengagement, the context of the course, activities, and students enrolled contributed to the findings. The larger inquiry included four cohorts with varying levels of deep, prolonged experience. Forty-two students (six male, 36 female), between 18 and 25 years of age, agreed to participate after enrolling in one of four sections of an undergraduate social science research methods course. Students represented four majors: agricultural leadership development, agricultural science, agricultural communications and journalism, and animal science.

When this study was conducted, I was a graduate student, and I co-taught the research course with my dissertation advisor and committee chair. Our students were involved as both participants and researchers. Specific learning objectives were aimed at developing students’ abilities to access information, think critically, and present and support reasoned arguments. However, students studied engagement by evaluating theories, collecting data from other populations, while also being introspective about their own engagement in the course.


Although the larger study was an abductive, longitudinal, quasi-experiment the emergent etic perspective storied here was autoethnographic in nature. Autoethnography is a method of rigorous self-reflection and reflexivity that relies on the personal experiences of the researcher to describe and evaluate beliefs, practices, and experiences (Ellis & Bochner, 2006; Adams & Manning, 2015). By nature, “autoethnography is messy, uncertain, and emotional” (Adams et al., 2014, p. 19). The ability to use a research method to both accommodate for and acknowledge the difficult realities of social life helped make meaning of my experiences struggling within a larger hyper-structured research design.  

Sources of Data

Data were extracted from more than 200 hours of interviews, four cohorts, and six hours of class per week, additional research meetings, conversations, and informal interactions of unknown amounts of time, and six weeks of immersive field experience. Additionally, quantitative data were collected from four commercially available instruments and used as artifacts to further increase the credibility of findings through data triangulation (Lincoln & Guba, 1985). Although the findings presented in this study only include data from observations, reflexive journals, student-teacher dialogue, and countless hours of rigorous introspection, the influence other sources of data may have had on my interpretation cannot be untangled. 

The Human Instrument

Lincoln and Guba (1985) provided characteristics that “qualify the human being as the instrument of choice for naturalistic inquiry” (p. 193). Unlike most quantitative instruments, human beings are adaptable and “like a smart bomb, the human instrument can locate and strike a target without having been preprogrammed to do so” (Lincoln & Guba, 1985, pp. 193-194). As the primary instrument of data collection, I viewed this process from a nonlinear perspective, but had the flexibility to use quantitative artifacts as sources of data. Data, regardless of method or source, were used to mold, adapt, and continuously calibrate the human instrument.

Observations, Journals, & Dialogue

Observations were made before, during, and after each class and research meeting and during the entire field experience. As an active participant in the experience, I was able to capture interaction, be inductive, and observe behaviors beyond what students would divulge during an interview (Patton, 2015). Observations brought my own perceptions to light as well as the perceptions of students as recorded in their reflexive journals.

In addition to my own journal, the reciprocal nature of the larger study required my students keep reflexive journals to reflect critically on the “human as instrument” (Guba & Lincoln, 1981). Journals served as a reservoir for thoughts, feelings, observations, and field notes. Together, we chronicled the learning process while calibrating our instruments through self-discovery and interrogation (Lincoln et al., 2011). Journals provided insight to distinctive voices each of us brought to the classroom and led to a greater understanding of the multiple perspectives that framed the learning process (Alcoff & Potter, 2013).

Students engaged in a constant exchange of thoughts and ideas that served as both a source of data and method of learning. Specific attention was given to Socratic dialogue to help unlock implicit ways of thinking and insights not previously explored by the group (Given, 2008). Many times, our Socratic sessions would occur spontaneously outside of the bounds of class meetings and usually near a white board. Our concepts, models, and brainstorms were captured in photos to visually recall and interpret the experience along the way.

Trustworthiness of Findings

Lincoln and Guba (1985) outlined techniques for establishing trustworthiness to ensure findings are reached in a systematic and disciplined manner. Trustworthiness techniques mirror evaluation criteria found in quantitative research and provide increased “inspectability” of data and findings. I used multiple techniques to enhance trustworthiness of findings including prolonged engagement, persistent observation, triangulation, audit trail, peer-debriefing, member-checks, reflexivity, and thick, rich description. Extensive records (reflexive journals, sketchbooks, pictures of conceptual designs and models, and process and personal memos) were kept for confirmability and constant comparison of significant statements, codes, and emergent themes. A coding structure was used to ensure a detailed audit trail and is as follows:

Student data: Ex: 014_BR2_079

  1. Student participant code (01 – 042); 2) Source of data (BR = Black and Red journal, SB = Sketchbook); 3) Page number = (001 – 175)

Teacher data: Ex. FN_BR3_104

  1. Research activity (OBSV=Observation, FN = Fieldnote, RF = Reflection.); 2) Source of data (BR = Black and Red journal, SB = Sketchbook); 3) Page number = (001 – 175)

Data Coding, Analysis, and Presentation

The task of understanding ethnographic data lies in the ability to condense mass amounts and sources of data (Merriam, 1998). I originally approached coding in a very inductive manner, using in vivo coding, descriptive codes, and deductive codes based on the framework of SCT (Miles et al., 2014). However, the use of SCT proved to hinder the analytic process when considering the reflexive nature of my own data. I continued with the coding process despite my frustrations; I inductively analyzed and coded data, developed additional codes to describe unexpected elements that emerged, and placed each into a matrix where they were continuously sorted into primary, secondary, and tertiary themes. It was not until much later that I discovered the process of analyzing data was not an exact science. Therefore, data were then viewed as analytic memos where I recorded additional elements of how the coding process took shape (Saldaña, 2016). The resulting findings were presented with student data alongside my own using verisimilitude—a literary strategy that captures the researchers’ thinking processes and attempts to realistically convey the intricacies of the experience with thick, rich description—thereby, enabling readers to reconstruct the experience for themselves (Creswell, 2009; Lincoln & Guba, 1985).

With consideration for my relationship to the data and the difficulty experienced during analysis, SRT (Brown, 2006) was introduced as an alternative to SCT and data were analyzed for evidence of emotive response, specifically shame. Shame data, in the context of high stakes learning environments, must be considered when studying unintended consequences of student-teacher (dis)engagement. In Shame Resilience Theory: A Grounded Theory Study on Women and Shame, Brown identified five main concerns of shame: what are the participants describing, what do they care about, what are they worried about, what are the participants trying to do, and what explains the different behaviors, thoughts, and actions (Brown, 2006, p. 44).


Words are Hard

Native Language 

I began this process in search of a way to make learning research more engaging. After considering various methods of classroom engagement, my teaching partner and I decided to forgo traditional teaching methods by avoiding the use of research terminology in class. Instead, we used common language so students might discover terms on their own and attach those words to experiences as they came about. For me, it was pretty easy to adhere to our native language because as a graduate student, research terminology was new to my everyday vocabulary. However, my partner had been using research jargon for eight years and the transition was difficult. Words are Hard quickly became a classroom hashtag and constant reminder to communicate in a way our students understand. 

Research as a Second Language 

The hashtag, #wordsarehard, became a fun “game” for our students. Our open and transparent process left very few things unsaid in our classroom, and students quickly caught on to the struggle we were experiencing with words. For students, myself included, research was a second language and “unlocking” new words was exciting… at first. For example, after observing other [University] students at various locations on campus, our students began to describe the various behaviors, environments, and personal characteristics they had observed. As one student wrote in his journal, “[Teacher] gets so excited when we figure things out. I need to Google Social Cognitive Theory” (07_BR1_014). My journal entry echoed their observation that day. FN_BR1_029: It’s working! It’s really working! #wordsarehard #proudteacher. I was motivated to provide them with experiences and attach terminology after they understood meaning. It seemed crazy, but research was becoming our second language and after years of learning terms just to pass a test, we were interested in how they became a permanent part of our vocabulary. 

Language Acquisition through Experience 

As time passed, words including “sample,” “instrument,” and “analysis” started to creep into our classroom discussions. Instead of discussing what might occur during an observation, interview, or face-to-face survey, students experienced issues first-hand and shared their successes and failures with our class. The chance to rifle through their experiences made it easier to share new terminology as we evaluated the process of understanding people. Although students seemed to be refreshed (or maybe just relieved) by the lack of terminology, a few also expressed a bit of confusion and annoyance with the process. One student was hesitant to speak up in class, but wrote “How is observing some people at the [student center] relevant to any kind of actual research” (013_BR1_018). Another student wrote, “Just give [the terms] to me. I know how to do research! I’m tired of waiting around for you to give me information” (06_BR1_027). I wanted to understand their point of view but was irritated with their impatience. After returning to interview and preliminary data, I saw the shared connection. Both students were double majors in animal science and predisposed to research in the basic sciences. In a way, they were ahead of the rest of the class (and always would be), but reflections provided more insight as each progressed. One wrote, “Observations seemed like useless collections of information. I now see it was the beginning of understanding a larger process” (013_BR1_018). 

Native Language Attrition 

Much to my surprise, as students gained efficacy with research terminology, I did too. Soon, my normal contributions to office banter were replaced with “what’s your unit of analysis,” and “what if we used a different conceptual framework?” I noted this transition after reflecting on time back home with friends. FN_BR3_062: When will I realize that not every lunch requires #researchtalk? I’m blabbering. THEY DO NOT CARE. Obnoxious! I found it only got worse as time went on. Research permeated my every interaction from my first cup of coffee in the morning to the text messages I sent before bed. Phone calls with my mom became more difficult and I could no longer explain to her what I had been up to. My “research buds” shared Piled Higher and Deeper (Ph.D.) comics on Facebook poking fun at the phenomena, but I had a hard time finding humor in our shared experience. FN_BR3_079: So much for being a great communicator! Might as well live under a rock. Because I had surrounded myself with peers in the same situation, the issue didn’t really become a problem until a new crop of students began the second phase of this study. Everything I prided myself on was slipping away.

FN_SB2_012: Why can’t I connect with them? I’m a teacher, damnit! Or am I? 🙁

Lost in Translation 

In almost an inability to remember what it was like to struggle with the research process, I found it more difficult to engage the final cohort of students like the first. FN_BR3_104: There’s a gap between cohorts that I don’t really understand quite yet. They are struggling. How do I make this better? I’m at a loss here. It seemed my newfound connection to research terminology and the process of doing research left it difficult for me to connect student learning to new experiences and new experiences to student learning. The first cohort seemed to embrace new terms because they were anxious to finally get them. They anticipated them. They wanted them. The second cohort, however, didn’t seem to make connections in the same way. In some cases, the words seemed to pass by the experiences as if students were simply going through the motions. More times than I would like to admit, students wrote things like, “is she even talking to me?” or “I’m over trying to understand this class.” It hurt, but they were right. I was speaking a foreign language and oblivious that my connection was lost somewhere in translation. In feeling loss of teaching and communication skills, I was forced to reflect on differences emerging in the data, specifically my own – I spiraled into web of shame (Brown, 2006).

Gut Punch: Cognitive Dissonance & Reciprocal Engagement

“How can you expect me (student) to be engaged when you (teacher) aren’t?” (16_BR1_064)

FN_BR2_084: Stop the bus. What did she just say? Are you kidding me?!


I have no recollection of what I said in response to [student] that day, but I was completely taken aback by her comment. We had intentionally built an environment where students could feel comfortable saying things like this, but I doubt my response was indicative of that effort. I was angry. FN_BR2_084: I’m giving everything I’ve got over here. Who do they think they are? I spent the next few hours sitting at my desk ruminating on the remark. I started to wonder if we had given students too much power and freedom in the classroom. FN_BR2_085: This is why structure is important. She would never say that to [faculty member]. My rant continued on the next two pages and finally subsided with a final thought.

FN_BR2_087: Oh, wait. I told her to do that.

Cognitive Dissonance

The original remark about my perceived level of engagement resonated in eleven other student journals (all but two students’ present) that day. Students began to question my general level of interest and motivation in the course. It was pivotal. I spent weeks (and months, really) thinking about how many times I teach students to do one thing, while modeling a completely different behavior. I also considered the times I observed this type of behavior from my own teachers and mentors. This insight became a magnifying glass, of sorts, and I began examining almost all of my interactions. Could something as simple as “walking the walk and talking the talk” be paramount to this study? FN_BR3_012: “Do as I say, not as I do.” Looks like Dad’s old mantra is coming back to haunt me.

Although my reflection may seem trivial, to me it was revelatory. This study was originally designed to understand students and the experiences that engage them in learning, but all the while, I may have been looking in the wrong direction. I literally told them (on the first day of class) I wanted to find a new way. I told them I believed engagement to be a two-way process and I wanted their open and honest feedback. Yet, there I was ignoring my own levels of engagement. Even more, I was wrought with fear that others (faculty, mentors, etc.) might discover my less than stellar performance and quietly ask me to pack my things. In retrospect, that was a silly thought, but the stakes seemed so high at the time and I was far from hitting the mark. She [student 16_BR1_064] provided the one piece of information that changed the way I considered this study, twelve little words that haunted my brain for months. It broke my spirit, but enlightened my path.

Autopilot: The Harsh Reality of (Dis)engagement

“You are different, beautifully so, and people will benefit from your perspective.

Your words mean something. This experience is teaching you far more than what can be observed – it’s teaching you to believe in you.” (06_SB1_003)

The excerpt (06_SB1_003), above, was written on a postcard and taped face down into the pages of a student’s sketchbook. I thumbed through several times, never giving them too much thought, but once the tape started to give way, this postcard flipped over. It was one of ten she planned to send as little reminders to herself when she arrived back home. Lucky for me, she forgot to send them, and that afternoon, I sat by myself, read through each one, and bawled my eyes out.

FN_BR2_114: I’m exhausted.

When teachers say, “I’m exhausted”, I don’t really believe that’s what they mean. I’m sure they are tired and may think they are exhausted, but what I really hear them saying is, “I’m not excited about what I’m doing right now.” When teachers are engaged, they ignore being tired; they’re in the zone and running on fumes of passion.

FN_BR2_115: I’m really exhausted.

I recognize the blatant contradiction here, but that doesn’t change the reality of its occurrence. Comments like the one above peppered my field notes during the last six months of this study. I was ashamed to write down thoughts like, “What am I doing?” or “I don’t want to be here,” so I didn’t, but they occurred nearly three times as much. There I said it. I was on autopilot.

The shame of thinking these things, let alone including them in this study, was paralyzing. The idea of being “called out” for a less than perfect study because Iwas a less than perfect teacher was more than my pride (and future career) could take. I felt like a big ole’ phony. Surely, I wasn’t the only one to ever feel this way, right? Right? Do you think anyone else knows?

When students say, “I’m exhausted”, I don’t really believe that’s what they mean. I’m sure they are tired and may think they are exhausted, but what I really hear them saying is, “I’m not excited about what I’m doing right now.” When students are engaged, they ignore being tired; they’re in the zone and running on fumes of passion.

“I’m exhausted” (38_BR1_071).

Huh? It was like some form of black magic. My students couldn’t possibly be experiencing the same thing. We’re different. They don’t know what I know. The rare occurrence of this finding in the literature made the connection between my data and my students’ data even more difficult to accept. I needed some reassurance. FN_BR3_099: HELP!! I give up. This is impossible.

Cold Hard Truth

This study took me down a long, circuitous path. Communicating the findings (on paper) has been a monumental task, but I have told this story (to anyone who would listen) every day since it began. I wrestled with my own experiences— both teaching and learning—at every turn. I questioned and resisted what I considered to be “conformity;” I’ve been angry, frustrated, and disenchanted; and I developed a pretty large chip on my shoulder, too. FN_BR3_047: How can I communicate this experience? How do I adequately portray my own disengagement? How do I describe how much I’ve changed? I don’t even feel like a teacher anymore. I’ll never get a job after this.

To this point, the “pieces” or themes were like vignettes that lined the walls of my heart and mind for months, but they remained static without understanding the experience more holistically. The fact is, “words are hard”—hard to articulate, difficult to write, painful to digest, and often lost without the ones around them. The larger study began with specific research questions concerning the influences of experience on student engagement, however, “the path of discovery is not clearly marked, nor should it be” (Thorp, 2001, p. 37). I could have easily described student engagement throughout the entire study, outlined findings of the hyper-focused quasi-experimental design I set out to follow, and provided more direction for others to build on for the future, however, that would have alienated the most glaring pieces of data—my own. Identifying and addressing my experience within a high stakes learning environment provided insight into the concept of shame resilience. In addressing my own professional and personal experience with shame, I moved forward while highlighting what seems to be an emerging reality in high stakes academic environments – honest data reporting. In sharing this data, my feelings of powerlessness and isolation decreased when I invited others in. After letting go of the many ways this piece might be perceived and how that perception might affect my future career, I created more experiences with empathy, connection, power, and freedom than I could have ever expected (Brown, 2006).


As the case with most naturalistic inquiry, the purpose of this study was not to infer to a larger population. Rather, the intent was to understand an unanticipated and, arguably, unfortunate phenomena: Teacher disengagement. Not only is the literature describing this phenomenon vague, it may be nonexistent. While teacher engagement is critical for the learning process, student expectations seem to be the immediate point of discussion. It’s important to mention the relationship between expectations and engagement – if expectations for learning or instruction are not met engagement will suffer for both teacher and student (Majkowski & Washor, 2014).

Experience was noted in no less than five of the seven research priority areas of the National Research Agenda (Roberts et al., 2016). Further, the history of, need for, and value of integration of experience into agricultural education environments was thoroughly noted by Baker, Robinson, and Kolb (2012). Despite the expansive number of researchers who have recommended integrating experiences into the educational environment, few have noted many of the potential unintended consequences of implementing deep, prolonged instructional experiences in a post-secondary environment. The occurrence of these consequences is not likely a new phenomenon. Yet, the implications of presenting ugly data or the unintended consequences of a study are not widely present in the agricultural education literature. Therefore, several elements should be investigated and considered by future studies:

Issues with Unrealizable Objectivity

Although it may seem as if I abandoned the design of this study somewhere along the way, that is not entirely the case. The design was like a too-tight sweater, uncomfortable but difficult to throw away. The truth is I became so focused on design that I had a difficult time connecting with the most important and significant part of my study: my students. It was important for me to tell that story, to illustrate the ways in which this study changed because I changed and allow the reader to come to conclusions on their own. Ignoring the growing pains would have omitted the difficult truths of an unrealizable objectivity – something I’m afraid is all too common in our research, but rarely explored. Reflecting on the power of vulnerability within the context of shame helped untangle this phenomenon; speaking shame is a pivotal opportunity for increased personal understanding and the development of personal and professional strategies for resilience (Brown, 2006). My attachment to design, and the research process for that matter, made it difficult for me to engage in the very environment I created. My quest to understand the complex nature of people and social interaction was beset by my transition from teacher to researcher. I was no longer the responsive and adaptable educator, but instead a rigid and design-focused researcher. I experienced the shame of foregoing a past pattern of thought by adopting the accepted norms of my high stakes environment, both losing and acquiring skillsets along the way.

Meaningful Connection in High Stakes Learning Environments

The process of learning new information is only engaging for so long without a personal connection between teachers and students. Harper and Quaye (2009) argued student engagement required more than an understanding of the teacher’s time and effort. The findings of this study provide evidence to support the influence of time and effort, but also raise questions of where that time and effort should be placed for effective learning. In this case, I placed the most time and effort on the process of conducting research instead of the people involved. I lost the connection with students when I stopped being responsive to their needs. There was no meaningful emotional connection to help make students connect to their learning experience, thus altering the overall learning environment. My commitment to people, to teaching people was ignored in my attempt to produce high quality research (Brown, 2006).

Might the high-stakes environments discussed by the National Research Council (1999) be to blame for the unintended consequences of disengagement by both students and teachers? Could the pressures of producing high quality research discourage teacher-researchers from improving instruction to engage students? Or is it simply the nature of research to become detached when adhering to focused and structured designs? Future research should consider the environmental and internal factors associated with faculty and graduate student expectations as it relates to student and teacher (dis)engagement in higher education; specifically, experiences with shame in academia and willingness to report honest data. Social scientists should consider moral and ethical implications of such, especially when expressing the realities of praxis in teaching and learning.

Connection Between Student & Teacher Experience

Often times, research considers the issues of student engagement independently from teacher engagement, providing a host of strategies to foster a better learning environment. However, rarely are variables considered side-by-side in a more holistic way. In doing so, it may be easier to notice the behaviors exhibited by students are not all that different from their teachers. Future research may benefit from observing engagement as a more universal phenomenon affecting teacher and student behavior similarly. Mojkowski and Washor (2014) contend student disengagement to be a deeper issue than previously believed. Student expectations are driving disengagement concerns. Students are struggling to fit into restrictive academic environments; therefore, a shift is necessary to focus on sustained engagement practices: relationships, challenge, play, relevance, authenticity, practice, choice, application, time, and timing (pg. 9).

The Problem with Theories

Although SCT was not the guiding force of this study, it served as a point of reference when considering factors of teaching and learning. At a granular level, factors suggested to change engagement were easy to understand, but the bigger challenge required I consider the way each and every interaction changed the next. It was a sequence of interactions, changes, and behaviors too large for me to see alone and the belief that a simple formula might uncover one solution was short-sighted. The simplification was intended to guide understanding, but a more complex analysis was needed. Literature identified SRT (Brown, 2006) as a means to shorten gaps of understanding and widen perspectives. SCT provided firm bounds for the study to begin while SRT provided a fluid construct for the data to exist.

The static and predictable nature of theories may lead people (especially young researchers) to believe the findings of this study (or any study for that matter) are merely formulaic in that the same person, doing the same thing, in the same way would provide the same answer every time. However, formulas are rigid and conventional, and albeit mathematical, function as a way to solve problems–human or otherwise. The sheer number of variables needed to consider the dynamic interaction between students and teachers during the process of learning is overwhelming, but should be considered, nonetheless. Might some teachers be restricted when conceptualizing teaching and learning as a formulaic process?

I contend student and teacher engagement, and thus, (dis)engagement to be more of a complex algorithm that adapts and changes. Although key “formulas” may make up an engagement algorithm, those formulas, the way they are arranged, and the many ways in which they change is more complex than what I could understand during the course of this study. Understanding the way this study emerged and the gravitational-type pull environment played in our findings would be too complex of a task without the consideration of a larger, adaptable algorithm. Future research should consider student and teacher (dis)engagement as an algorithm that stretches and changes in new, more dynamic ways. One potential method of inquiry is an algorithm based in SRT. Establishing a conceptual algorithm based on SRT may provide context for evaluating teachers in a complete emotional, social, and mental context. Understanding engagement with students and teachers demands an adaptable framework for teaching and learning.

New Methods in Agricultural Education

Ok. Hear me out. Many of the struggles of this study and my ability to adequately describe my experience may lie in our professions level of discomfort with more uncommonly used methods. As a graduate student, I worked alongside my mentors to develop a quasi-experimental study to “increase the rigor” associated with Agricultural Education research in the social sciences. All the while, this design held me back from truly understanding the phenomena at play. I struggled. I sought out additional qualitative methods, but rarely saw those methods in the pages of our journals. It seemed (to me) that I must adhere to a more structured design if I wanted to succeed. Might young researchers be hindered by our collective distaste for new methods? How can we mentor young researchers in rigorous methods spanning paradigms? What does the future of Agricultural Education (broadly defined) look like when young and old researchers alike struggle with making sense of a too tight and seemingly sterile science in a socially constructed discipline? Could the introduction of theories similar to the open-ended study of shame resilience provide a greater insight into teacher-student experiences? How do we help? Help.


Adams, T. E., Jones, S., & Ellis, C. (2014). Autoethnography: Understanding qualitative research. Oxford University Press.

Adams, T. E., & Manning, J. (2015). Autoethnography and family research. Journal of Family    Theory & Review7(4), 350–366.

Alcoff, L., & Potter, E. (Eds.). (2013). Feminist epistemologies. Routledge.

Bain, K. (2004). What the best college teachers do. Harvard University Press.

Baker, M. A., Robinson, J. S., & Kolb, D. A. (2012). Aligning Kolb’s experiential learning theory with a comprehensive agricultural education model. Journal of Agricultural Education, 53(4), 1-16.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. 

Bensimon, E. M., & Dowd, A. (2009). Dimensions of the transfer choice gap: Experiences of Latina and Latino students who navigated transfer pathways. Harvard Educational Review79(4), 632–659.

Brown, B. (2006). Shame resilience theory: A grounded theory study on women and shame. Journal of Contemporary Social Services, 87(1), 43–52.

Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7), 3–7.

Coates, H. (2007). A model of online and general campus-based student engagement. Assessment and Evaluation in Higher Education, 32(2), 121–141.

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Dewey, J. (1938). Experience and education. Macmillan.

Edgar, D. W., Retallick, M. S., & Jones, D. (2016). Research priority 4: Meaningful, engaged learning in all environments. In T. G. Roberts, A. Harder, & M. T. Brashears (Eds.), American Association for Agricultural Education national research agenda: 2016-2020 (pp. 37–40). Department of Agricultural Education and Communication.

Ellis, C. S., & Bochner, A. P. (2006). Analyzing analytic autoethnography: An autopsy. Journal of Contemporary Ethnography35(4), 429–449.

Given, L. M. (Ed.). (2008). The Sage encyclopedia of qualitative research methods. Sage Publications.

Guba, E. G. & Lincoln, Y. S. (1981). Effective evaluation: Improving the usefulness of evaluation results through responsive and naturalistic approaches. Jossey-Bass.

Harper, S. R., & Quaye, S. J. (2009). Beyond sameness, with engagement and outcomes for all: An introduction. In S. R. Harper & S. J. Quaye (Eds.), Student engagement in higher education (1-15). Routledge.

Hu, S., & Kuh, G. D. (2001, April 10-14). Being (dis)engaged in educationally purposeful activities: The influences of student and institutional characteristics [Paper presentation]. American Educational Research Association Annual Conference, Seattle, WA, United States.

Johnson, D. W., Johnson, R. T., & Smith, K. A. (1991). Cooperative learning: Increasing college faculty instruction productivity. ASHE-ERIC Higher Education Report No. 4. The George Washington University, School of Education and Human Development.

Krause, K. & Coates, H. (2008). Students’ engagement in first-year university. Assessment and Evaluation in Higher Education, 33(5), 493-505.

Kuh, G. D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, No. 141, 5–20.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage. 

Lincoln, Y. S., Lynham, S. A., & Guba, E. G. (2011). Paradigmatic controversies, contradictions, and emerging confluences, revisited. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (4th ed., pp. 97–128). SAGE.

Magolda, P. M. (2005). Proceed with caution: Uncommon wisdom about academic and student affairs partnerships. About Campus, 9(6), 16–21.

Merriam, S. B. (1998). Qualitative research and case study applications in education. Jossey-Bass. 

Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods Sourcebook (3rd ed.). Sage.

Mojkowshi, C. & Washor, E. (2014). Student disengagement: It’s deeper than you think. The Phi Delta Kappan, 95(8), 8–10.

National Research Council. (1999). High stakes: Testing for tracking, promotion, and graduation. The National Academies Press.

National Research Council. (2009). Transforming agricultural education for a changing world.

National Academies Press.

Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). SAGE. 

Piaget, J. (1976). Piaget’s theory. In B. Inhelder, H. H. Chipman, & C. Zwingmann (Eds.), Piaget and his school (pp. 11-23). Springer-Verlag.

Roberts, T. G., Harder, A., & Brashears, M. T. (Eds). (2016). American Association for Agricultural Education national research agenda: 2016-2020. Department of Agricultural Education and Communication.

Saldana, J. (2016). The coding manual for qualitative researchers (3rd ed.). SAGE.

Sheldon, K.M. & Biddle, B.J. (1998). Standards, accountability, and school reform: Perils and pitfalls. Teachers College Record, 100(1), 164–180.

Sorathia, K., & Servidio, R. (2012). Learning and experience: Teaching tangible interaction & edutainment. Procedia-Social and Behavioral Sciences, 64, 265–274.

Thorp, L. G. (2001). The pull of the earth: An ethnographic study of an elementary school garden (Doctoral dissertation). Texas A&M University, College Station, Texas.

Reconceptualizing Problem-Solving: Applications for the Delivery of Agricultural Education’s Comprehensive, Three-Circle Model in the 21st Century

Whitney Figland, Dutchtown High School,

Richie Roberts, Louisiana State University,

J. Joey Blackburn, Louisiana State University,

PDF Available


Problem-solving has been an integral tenet of school-based, agricultural education (SBAE) since its inception. However, in many ways, the pedagogy has changed considerably. This shift appears to have caused problem-solving’s pedagogical dimensions and underlying philosophical foundation to become conflated with other methods of instruction. Consequently, fundamental questions persist:“Should problem-solving be practiced as a distinct pedagogy?” And if so, “What implications exist for its use in SBAE?” In response, this philosophical study sought to examine perspectives on problem-solving and explain how it has been advanced in the discipline. A product of this investigation was the emergence of three principles that appear to be foundational to problem-solving: (1) identify problems, (2) analyze information, and (3) evaluate solutions. Distinguishing such principles helped describe how problem-solving has been operationalized historically. However, it also revealed a need to expand its current understanding and use. In response, we proposed the Integrated Problem-Solving Model for Agricultural Education to illuminate how it could be reconceptualized as a guiding philosophy for SBAE to better navigate increasingly complex issues and problems in the 21stCentury.


Over the past few decades, a variety of instructional methods have been advanced in education to encourage students to obtain the skills they need to thrive in the 21stCentury (Koichu, 2019; Ulmer & Torres, 2007). However, more recently, it has become critical for educators to adopt methods of instruction that encourage students to develop higher-order thinking skills (Fuhrmann & Grasha, 1983; Jonassen, 2000; Ulmer & Torres, 2007). One explanation for this shift is that employers often view the ability to solve problems, a higher-order skill, as essential in the workplace (Gokhale, 1995; Robles, 2012; Zimmerman & Risemberg, 1997). Nevertheless, many students are not challenged to engage in real-world problems in their schooling (Jonassen, 2000). Instead, they learn through rote memorization and other forms of direct instruction in which the instructor passively transfers knowledge – an approach that does little to prepare students for a successful career (Jonassen, 2000). As a consequence, a need has emerged to embed more opportunities for students to authentically engage in problem-based experiences that accurately reflect the world in which they operate. Previous research has demonstrated that engaging students in learning activities that challenge their problem-solving abilities can foster metacognitive growth, i.e., the ability to reflect on learning and modify one’s behavior accordingly (Sproull, 2001). For example, through the use of such an approach, students learn to grapple with problems, from simple to complex, by developing solutions that complement the knowledge and skills they developed through their coursework (Jonassen, 2000).

From a historical perspective, the problem-solving approach can be traced to classical philosophers such as Socrates and Plato, who believed that individuals came to truth by socially constructing meaning through participation in debates (Phillips, 2010). For example, The Socratic Method draws on cooperative dialogue in which individuals answer questions that stir new thoughts and ideas about the nature of knowledge and knowing (Phillips, 2010). This early approach to problem-solving appeared to serve as a basis for contemporary views on the method and helped further distinguish it as a pedagogy (Dewey, 1910; Phillips, 2010). Using this foundation, John Dewey (1910) further concretized the key dimensions of problem-solving. For instance, in Dewey’s (1910) How We Think, he outlined five tenets called the Complete Action of Thought or Reflective Thinking that included: (1) a felt difficulty; (2) location and definition of a problem; (3) creation of possible solutions; (4) test solutions; and (5) further explorations and evaluation. These processes provided a basis for conceptualizing problem-solving as a process that could be used to mature students’ intellectual development and critical thinking (Dewey, 1910). However, Dewey never used the term problem-solving in his academic work.

Despite this, Dewey, along with other educational philosophers, paved the way for problem-solving to be recognized and practiced as a pedagogy in the 20thCentury (Moore & Moore, 1984). However, discourse on problem-solving has been muddled by the introduction of terms, such as problem-based learning (PBL) and inquiry-based instruction (IBI), that although are distinct in form and function also appear to exhibit striking “pedagogical congruence” (Parr & Edwards, 2004, p. 104). As a result, a definition for problem-solving does not appear to have reached consensus. Some disciplines have responded to this issue by crafting descriptions of the pedagogy that integrate the various perspectives of philosophers, researchers, and practitioners (Crunkilton & Krebs, 1967; Jonassen, 2000; Merwin, 1977). The definition of problem-solving, therefore, varies considerably among academic disciplines. For example, in technology education, Merwin (1977) defined problem-solving as “a sequence of procedures in the thinking process that a learner employs in dealing with a problem or task” (p. 123). Jonassen (2000) added that problem-solving could also allow students to “find [answers to] the unknown.” (p. 65). In agricultural education, however, Crunkilton and Krebs (1967) defined problem-solving “as a method of teaching in which the teacher guides the class through a series of questions. . .” (p. 90). Because of such variant depictions, therefore, problem-solving’s philosophical and operational tenets remain unclear.

Nevertheless, the pedagogy appears to have been considered an integral tenet of school-based, agricultural education (SBAE). For example, the use of the pedagogy emerged in SBAE in concert with the Smith-Hughes Act of 1917 (Moore & Moore, 1984). During this period, the U.S. experienced an industrial revolution, which shifted education and catalyzed reform efforts (Roberts, 1957; Roberts & Ball, 2009; Talbert et al., 2007). This shift also piqued national interest in the enhancement of skilled laborers (Roberts & Ball, 2009). Because of these changes in U.S. society, it is believed that problem-solving became diffused as a method of instruction in SBAE (Moore & Moore, 1984) and experienced more widespread adoption (Boone, 1990; Cano & Martinez, 1991; Crunkilton & Krebs, 1967; Dyer & Osborne, 1996; Flowers & Osborne, 1988; Hammonds, 1950; Krebs, 1967; Newcomb et al., 1993; Phipps & Osborne, 1988; Torres & Cano, 1995a; Torres & Cano, 1995b). However, problem-solving has been described, represented, and depicted in a variety of ways throughout its rich history in SBAE. Such variances were made explicitly clear in submissions that described problem-solving in The Agricultural Education Magazine (The Magazine).

For example, as evinced in The Magazine, problem-solving’s use in SBAE emerged in the mid 20th Century (Hammonds, 1950; Krebs, 1967). However, in many ways, the pedagogy, and other methods of instruction, have evolved considerably in the early 21st Century as practitioners responded to key shifts in American society (Roberts & Edwards, 2015, 2018). In particular, in the early 2000s, the enactment of No Child Left Behind (NCLB) created a turning point in U.S. education policy that resulted in wide-sweeping reform efforts, which required states to adopt learning standards and assessments to monitor better and track students’ progress, especially regarding mathematics, reading, and science (U.S. Department of Education, 2001). Such changes also largely influenced approaches to teaching and learning that were depicted in The Magazine. For instance, contributors published articles on learning approaches that featured: (a) PBL, (b) IBI, and (c) experiential learning that focused on applications of science, technology, engineering, and mathematics (STEM) (Retallick & Miller, 2005; Torres & Cano, 2005a).

Although such work was pivotal to positioning SBAE as relevant, during this period, problem-solving’s pedagogical dimensions and underlying philosophical foundation also appeared to become blurred and conflated with other teaching and learning approaches. As a consequence, a dichotomy emerged in which some practitioners began to represent problem-solving as a distinct method of instruction, while others articulated it as an approach that was largely synonymous with other pedagogies (Parr & Edwards, 2004). Because of these discrepancies in the problem-solving literature, a lack of clarity exists in SBAE regarding how problem-solving should be delivered conceptually. To complicate this issue further, however, early literature in SBAE (Crunkilton & Krebs, 1967; Moore & Moore, 1984) on problem-solving argued it lacked a solid theoretical foundation and should be approached with caution when used as a method of instruction. As a consequence, two questions persist: “Should problem-solving be practiced as a distinct pedagogical approach? And if so, “What implications exist for using problem-solving in the 21stCentury and beyond?” These questions motivated the current study.


To address this issue, the purpose of this philosophical investigation was threefold: (1) describe existing perspectives and theories on problem-solving; (2) explain how problem-solving has been used as a method of instruction in SBAE; and (3) illuminate how the problem-solving could be reconceptualized to enrich the delivery of SBAE’s comprehensive, three-circle model. This research aligns with the American Association for Agricultural Education’s National Research Agenda Research Priority 7: Addressing Complex Problems. Specifically, this research addresses question one, “What methods, models, and programs are effective in preparing people to solve complex problems, interdisciplinary problems?”(Andenoro, Baker, Stedman, & Pennington, Weeks, 2016, p. 59).

Methods and Procedures

Philosophical research seeks to analyze existing axioms and beliefs in a given domain (Roberts & Edwards, 2020; Salevouris & Furay, 2015). This study, therefore, synthesized educational theories and perspectives from prominent problem-solving advocates, while also advancing new understandings for SBAE. From a philosophical perspective, problem-solving aligns with the worldview of pragmatism, which advances the belief that individuals construct meaning from their experiences as they interact with others and navigate issues and problems in a real-world context (Crotty, 1998). To meet the study’s purpose, we synthesized theoretical and practitioner-oriented work as well as empirical evidence supporting problem-solving through the use of the following sources: (a) books, (b) peer-reviewed journal articles, and (c) The Agricultural Education Magazine.

All references were subjected to internal and external criticisms to triangulate our findings (Salevouris & Furay, 2015). For instance, we evaluated each source for authenticity concerning its origin and content (Salevouris & Furay, 2015). Further, we analyzed how the investigation’s (a) findings, (b) conclusions, (c) implications, and (d) recommendations might provide inferences for future work. To accomplish this, we used a conceptual mapping technique in which we scrutinized each source’s existing similarities and discrepancies (Salevouris & Furay, 2015). For example, through mapping, we revealed each source’s interconnectedness and congruence with the study’s purpose (Salevouris & Furay, 2015). As a result, we developed key empirical assertions through the use of an analytic memoing technique (Saldaña, 2015). Then, we synthesized our findings by weaving our assertions into a narrative that described how problem-solving could be reimagined to deliver agricultural education’s comprehensive, three-circle model in transformative new ways.

Perspectives and Theories on Problem-Solving

Through our analysis, six leading perspectives – John Dewey, Rufus Stimson, Werrett Charters, William Lancelot, John Bransford, and Scott Johnson – on problem-solving appeared to most prominently shape existing thought and use of the pedagogy in SBAE as well as in teaching and learning more broadly. Our description of each perspective is provided next.

John Dewey

John Dewey largely gained prominence as a thought leader as a result of his time at the University of Chicago after creating a progressive school, called the Dewey Laboratory School, that he used to foment his philosophy and theory on experience and education (Dewey, 1910, 1938). Dewey believed that students should be viewed as active pursuers of knowledge that lived, worked, and interacted in the world as a social being (Hyland, 1993). Dewey was also a strong advocate for students actively engaging in experiences that were based on real-world issues and problems (Dewey, 1938). In particular, Dewey maintained that teaching students to think and solve problems was integral to creating successful members of society (Dewey, 1910). Further, Dewey (1910) detailed in How We Think his five-step model for reflective thinking. Dewey’s five axioms for reflective thinking included: (a) felt difficulty, (b) location and definition of the problem, (c) creation of solutions, (d) development of reasons for solutions, and (e) further exploration and evaluation (Dewey, 1910).

Rufus Stimson

Rufus Stimson has also been identified as a pivotal early leader to agricultural education in the U.S. (Moore, 1988, 2018). Perhaps, his most significant contribution to the discipline was the formalization of the project-based method, which is now recognized as the Supervised Agricultural Experience (SAE) component of agricultural education’s comprehensive, three-circle model (Camp & Crunkilton, 1985; Foor & Connors, 2010; Moore, 1988). Although Stimson (1911, 1919) did not use the term problem-solving, many of the core features of the project-based method, align naturally with the pedagogy. For example, Stimson (1911) advanced three major projects relevant for farm work: (1) improvement, (2) experimental, and (3) productive. In his description of project types, Stimson (1919) explained that each would require students to identify relevant problems, collect evidence, and design a strategy to respond to each unique issue or problem. Such work also deeply influenced his protégé Werrett Charters.

Werrett W. Charters

Werrett Charters was a student of Dewey for three years at the University of Chicago. It is because of this experience that Charters is often recognized as a disciple of Dewey and a proponent of his philosophy and beliefs on teaching and learning. However, he also made pivotal advancements to problem-solving in his own right. For instance, in Charters’ works Methods of Teaching and Teaching (1912) and Teaching the Common Branches (1924) he emphasized the importance of having students solve real-world problems that piqued their interest and motivated them to be actively engaged in the learning process (Charters, 1912, 1924). Similar to Dewey’s (1910) reflective thinking model, Charters advanced both inductive and deductive reasoning (Charters, 1924). However, Charters also theorized that inductive thinking processes could help propel students’ deductive thinking as they work through contextualized problems, form hypotheses, and arrive at concrete solutions (Charters, 1912). As a result, Charters (1924) advanced three stages of problem-solving: (a) definition of the problem, (b) creation of a hypothesis, and (c) testing and verifying the solution. Such advancements appear to have profoundly influenced how problem-solving was operationalized in its formative years in SBAE.

William Lancelot

William Lancelot was another early proponent of problem-solving in SBAE. Lancelot received his bachelor’s degree in agricultural education in 1919 and shortly after pursued his master’s degree in education at Columbia University. During his graduate studies, Lancelot was introduced to the works of Dewey and Charters, which greatly influenced by his views on education and society (Lancelot, 1944). As a result, Lancelot advocated for transitioning education from a rote memorization model to one that closely mirrors problem-solving (Lancelot, 1944). In his book Permanent Learning (1944), he described different types of problems that students may encounter during their educational experiences, how to use such problems productively, ways to integrate problems across contexts, and the uses of the problem-solving in regard to teaching and learning. Further, Lancelot (1944) conceptualized 10 steps that educators could use to implement problem-solving as a pedagogy. Similar to Dewey and Charters, Lancelot also articulated the role of inductive and deductive reasoning. Because of his deep connection to SBAE, his work appeared to influence the discipline profoundly. However, in the proceeding decades, other prominent educational leaders influenced SBAE as well.

John D. Bransford

John Bransford was an educational psychologist at the University of Washington who authored several critical works regarding cognition, learning styles, and teaching. For example, in Bransford’s and Stein’s (1984) The IDEAL Problem Solver, he introduced an approach to problem-solving that encompassed the ideas and theories of several key theorists such as Kolb (1984), Newell and Simon (1972), and Sterberg (1981). The IDEAL problem-solving model also drew on concepts from the Socratic method, the scientific method, and John Dewey’s reflective thinking model (Phipps, Osborne, Dyer, & Ball, 2008). In particular, the IDEAL problem-solving model largely reconceptualized Dewey’s reflective thinking model using the following processes: (a) identify problems and opportunities, (b) develop goals, (c) explore possible strategies, (d) anticipate outcomes, and (e) look back. It is critical to note that in the IDEAL problem-solving model, each step is fluid and may not unfold successively (Bransford & Stein, 1984). Figure 1 depicts Bransford’s and Stein’s (1984) IDEAL problem-solving model.

Figure 1

Bransford’s and Stein’s (1984) IDEAL Problem-Solving Model

Note. Adapted from “The Influence of Cognitive Diversity on Group Problem-solving Strategy” by A. J. Lamm, C. W. Shoulders, G. T. Roberts, T. A. Irani, L. J. Snyder, and J. Brendemuhl, 2012, Journal of Agricultural Education, 53(1), p. 19. Copyright 2012 by Journal of Agricultural Education. Reprinted with permission.

Scott Johnson

Anothervein of literature that has greatly influenced problem-solving theory and practice is troubleshooting. And, perhaps, the individual that has most profoundly advanced thought on troubleshooting is Scott Johnson. For example, Johnson’s (1989) technical troubleshooting model provided conceptual guidance for practitioners to support students as they navigate complex curricular problems. In the first phase of the model, students collect and interpret information through two primary sources: (1) procedural knowledge, and (2) external sources (Johnson, 1991). Procedural knowledge refers to an individual’s understandings that result from processes such as reading diagrams, using mathematical formulas, and understanding manuals (Johnson, 1989). Meanwhile, external sources of information typically originate from the knowledge that individuals glean from jobs, technical support, and evaluations (Johnson, 1989). Of note, both sources of knowledge help troubleshooters form a more concrete understanding of the problem (Johnson, 1991). Based on Johnson’s (1989) model, after individuals acquire information from the aforementioned sources, they enter an interpretation phase (Johnson, 1991). This step is critical because troubleshooters must identify which concepts are relevant based on their prior learning and experiences (Johnson, 1989). If enough information has been gathered, then the troubleshooter can then move into the hypothesis generation phase. During this step, individuals generate one or more hypotheses about the problem (Elstein et al., 1978; Frederiksen, 1984; Johnson, 1989). After the hypothesis generation phase, troubleshooters evaluate their results, which allows the troubleshooter to test their hypotheses and determine whether it should be accepted or rejected (Johnson, 1991). If the troubleshooter did not solve the problem, they restart the process, as depicted in Figure 2 (Johnson, 1991).

Figure 2

Troubleshooting Model

Note. Adapted from “A description of expert and novice performance differences on technical troubleshooting tasks” by S.D. Johnson, 1989, Journal of Industrial Teacher Education, 26(3), p. 20. Copyright 1989 by Journal of Industrial Teacher Education. Reprinted with permission.

Problem-Solving’s Use in SBAE

In addition to being articulated by leading educational theorist, problem-solving has also been advanced in SBAE since its early inception as a way to facilitate authentic learning for students (Moore & Moore, 1984; Parr & Edwards, 2004; Retallick & Miller, 2005; Torres & Cano, 2005b). As an illustration, Phipps and Cook (1956) advanced Dewey’s (1910) stages of problem-solving by contextualizing the pedagogy using examples in agriculture. Later, Crunkilton and Krebs (1967) introduced five key phases to consider when using the problem-solving in SBAE. Those phases included: (a) interest approach; (b) create objectives; (c) anticipate problems; (d) solve the problem; (e) evaluate and apply (Crunkilton & Krebs, 1967).

Further, Phipps and Osborne (1988) described their views on problem-solving in The Handbook on Agricultural Education in Public Schools. Phipps and Osborne’s (1988) approach included similar elements outlined in previous works on problem-solving. For instance, their six-step method included: (a) experience a situation, (b) locate and define the problem, (c) attempt a trial solution, (d) explore reference and information, (e) arrive at a group solution, and (f) evaluate. Finally, Newcomb et al. (1993) addressed problem-solving in Methods of Teaching Agriculture, which appears to be one of the most recent attempts to outline the pedagogy for SBAE. In this work, the problem-solving method to teaching and learning is outlined in six steps, which were grounded in the previously reported literature. Those six steps to teaching the problem-solving approach in agricultural education included: (a) interest approach, (b) objectives to be achieved, (c) problems to be solved or answered, (d) problem solution, (e) test solutions through application, and (f) evaluate solutions (Newcomb et al., 1993). Therefore, through our analysis, it appeared that leading perspectives on problem-solving and prominent literature in SBAE demonstrated significant “pedagogical congruence” (Parr & Edwards, 2004, p. 104). As a consequence, a synthesis of these concepts was warranted to advance thought on problem-solving for SBAE.

Synthesis: Advancing the Shared Principles of Problem-Solving

To advance new understandings, we distilled shared principles from the leading perspectives on problem-solving and the SBAE literature. To accomplish this, we grounded our approach in a concept known as consilience, first introduced by William Whewell (1840). Consilience represents the merging of stands of knowledge from various disciplines, perspectives, and domains to offer new understandings of a phenomenon (Whewell, 1840). Using this approach, we engaged in a mapping technique to visualize each perspective’s similarities and discrepancies while also acknowledging that some authors might not have specifically used the term problem-solving but in essence were describing a similar concept. A product of this procedure was the emergence of three shared principles that appear to be foundational to existing descriptions and representations of problem-solving as a pedagogy. To promote understanding, we chose to represent the shared principles using practical language in hopes that practitioners, researchers, and theorists alike might find them useful. Given such caveats, we offer the three principles of problem-solving that emerged from our analysis: (1) identify problems, (2) analyze information, and (3) evaluate solutions.

Principle #1: Identify Problems

A fundamental characteristic of problem-solving is ensuring that students have the knowledge and skills they need to identify relevant problems (Bransford & Stein, 1984; Crunkilton & Krebs, 1967; Dewey, 1910, 1938; Charters, 1912, 1924; Lancelot, 1944; Newcomb et al., 1993; Phipps & Osborne, 1988). This notion applies to whether problems are presented in the context of a classroom or in a more authentic learning environment (Dewey, 1910, 1938). To equip students with such skills, however, requires introducing them to foundational agricultural knowledge so that they can begin to understand connections, notice disturbances, and appropriately detect when an issue or problem exists (Lancelot, 1944). Therefore, developmental appropriateness is of central importance to ensure that students are prepared as they gain exposure to problems (Charters, 1924), especially in the context of SBAE. As a consequence, SBAE teachers should frame problems in ways that challenge students, but that do not trigger forms of dissonance that may be interpreted as uneducative (Dewey, 1910). Through a synthesis of the literature, it became apparent that to ensure students are able to identify problems successfully, SBAE instructors must scaffold them in ways that allow students to mature before they confront issues and problems of a greater cognitive complexity (Bransford & Stein, 1984; Charters, 1912, 1924; Crunkilton & Krebs, 1967; Dewey, 1910; Goossen et al., 2017; Lancelot, 1944).

Principle #2: Analyze Information

As inevitable and ubiquitous as problems are in everyday life, human beings often resist analyzing trends and other relevant data to arrive at possible solutions (Dewey, 1910; Phipps & Osborne, 1988). An essential principle of problem-solving, therefore, is to analyze information. Through our synthesis, we noted that authors of seminal works on problem-solving described a plethora of ways to collect and analyze relevant evidence. For example, articulated strategies included conducting observations (Dewey, 1910, 1938), analyzing test and control specimen (Charters, 1924), as well as generating a hypothesis based on individuals’ procedural or external sources of knowledge and then assembling relevant corroborating or disconfirming evidence (Johnson, 1989). Despite the diversity in strategies available, however, SBAE teachers should ensure that students systematically collect information and evaluate it using rigorous procedures (Bransford & Stein, 1984; Charters, 1924; Dewey, 1910, 1938; Johnson, 1989, 1991).

Principle #3: Evaluate Solutions

Because problem-solving is a process, the solution emerges over time, through trial and error (Bransford & Stein, 1984; Charters, 1912; Crunkilton & Krebs, 1967; Dewey, 1910, 1938; Lancelot, 1944; Newcomb et al., 1993; Phipps & Osborne, 1988). Due to the dynamic nature of such, the evaluation of a solution is in a constant state of flux by which new discoveries can alter the beginning, middle, or late phases of the problem-solving process (Dewey, 1938; Johnson, 1989, 1991). This developmental view of the final principle, therefore, recognizes that as students learn and acquire information, an iterative progression transpires in which they co-influence past, present, and future solutions to numerous issues and problems (Dewey, 1938; Charters 1924). It is through this non-linear process; therefore, that SBAE students can critically reflect and begin to authentically evaluate whether their solution to a given problem is viable.

Reconceptualizing Problem-Solving for SBAE

Embedded in the three principles of problem-solving are features that stand as prominent attributes of the pedagogy. Therefore, our synthesis of ideas, theories, and models was a necessary step to illuminate how problem-solving has been advanced and used as a method of instruction. However, this process also revealed the need to expand our current view and understanding of problem-solving in SBAE. We maintain that such a reconceptualization could crystalize new possibilities for future research, theory, and practice.

For example, although problem-solving has largely been represented as a method of instruction, and rightfully so, we maintain that problem-solving’s current limits and parameters in SBAE could be expanded so that it may also be viewed as a guiding philosophy for the discipline. To that end, we offer (see Figure 3) the Integrated Problem-Solving Model for Agricultural Education to demonstrate how this idea could be operationalized in SBAE. In the model’s development, our goal was to enrich agricultural education’s comprehensive, three-circle model by embedding the core principles of problem-solving – identify problems, analyze information, and evaluate solutions – in a way that would aptly depict the synergistic and complementary power of this merger.

Figure 3

Integrated Problem-Solving Model for Agricultural Education

Note. The principles of problem-solving are shaded to demonstrate their permeability through and between each dimension of SBAE.

Foundationally, therefore, the model advances the notion that problem-solving is entrenched through and between each dimension of agricultural education. Consequently, the principles of problem-solving are interwoven with the three components of agricultural education: (a) classroom and laboratory, (b) Supervised Agricultural Experience (SAE), and (c) The National FFA Organization (FFA). It is important to note that the principles of problem-solving are not exclusive to a single dimension of agricultural education. Instead, they should be considered permeable as the problem-solving process unfolds for students through trial-and-error.

To contextualize the model, we developed the following example to demonstrate how the model might be used in SBAE. To begin, consider a student enrolled in an Introduction to Horticulture course (Classroom and Laboratory) who noticed that the Poinsettias she planted in class a few weeks prior appeared to be stunted in growth (Principle #1: Identify Problems). To capitalize on the learning embedded in this problem, her SBAE teacher encouraged her to reflect on the learning concepts introduced earlier in the semester. After a few minutes, she answered, “Maybe it is because the plants are under the shade cloth, so they are not getting enough sunlight.” Her SBAE teacher responded, “That is a great start, perhaps, you should design a project (SAE) that will allow you to collect data to determine whether or not your hypothesis is correct.” Over the next few weeks, she collected data using control and experimental trials, and as a result, began to observe trends through an analysis of relevant information (Principle #2: Analyze Information). After this procedure, she drew the conclusion that because Poinsettias are a tropical flower, they were not getting enough direct sunlight when placed under a shade cloth in the greenhouse. She also developed a solution to this problem for individuals who may be experiencing similar issues. Because her SBAE teacher perceived she had done quality work, he encouraged her to carry out additional trails so this knowledge could be used to impact the community through a service project (FFA). As a result, she decided to work with the local FFA Officer Team to organize a professional development opportunity for senior citizens based on the knowledge she had acquired through her classroom and Supervised Agricultural Experiences (SAEs). During this session, she also asked the senior citizens to provide feedback on their experience so that she could more carefully evaluate the solutions she provided regarding growing Poinsettias (Principle #3: Evaluate Solutions). As illustrated above, the SBAE teacher wove the three principles of problem-solving throughout each programmatic dimension of agricultural education – classroom and laboratory, SAE, and FFA – for his student. Such use of problem-solving may be easy to dismiss as common sense. However, we counter this position on several grounds. First, what may appear to be common sense for some, may not be viewed as such by those who are new to the discipline, have little experience, or have only considered limited perspectives on problem-solving. And finally, existing descriptions of problem-solving in SBAE do not appear to have represented it in ways that capture the intricacies of the reconceptualization advanced in our philosophical discussion.


Problem-solving has evolved considerably since its early origins. For example, initially, it was depicted as a distinct method of instruction (Charters, 1912). However, since that time, it appears to have become conflated with other pedagogical approaches (Parr & Edwards, 2004). As a consequence, the tenets of problem-solving became ambiguous over time (Crunkilton & Krebs, 1967; Moore & Moore, 1984). In this investigation, therefore, we sought to examine existing perspectives on problem-solving and explain how problem-solving has been used as a method of instruction. Through our analysis, we conclude that six leading perspectives – Dewey, Stimson, Charters, Lancelot, Bransford, and Johnson – appeared to most profoundly influence the ways in which the pedagogy has been operationalized. From these leading perspectives, we also conclude that three shared principles of problem-solving could be distilled: (1) identify problems, (2) analyze information, and (3) evaluate solutions. The first principle, identify problems, reflected that need for educators to scaffold problems in ways that are challenging but also developmentally appropriate so that students can gain confidence before attempting to solve problems of a greater complexity (Bransford & Stein, 1984; Crunkilton & Krebs, 1967; Dewey, 1910, 1938; Charters, 1912; 1924; Lancelot, 1944). Meanwhile, the second principle, analyze information, represented the need for students to collect and analyze quality data using rigorous procedures before drawing conclusions about a problem (Bransford & Stein, 1984; Charters, 1924; Dewey, 1910; Johnson, 1989, 1991).

The last principle, evaluate solutions, suggested that because problem-solving is a process, students should evaluate their solutions to problems over time through trial and error (Bransford & Stein, 1984; Crunkilton & Krebs, 1967; Dewey, 1910, 1938; Charters, 1912, 1924; Lancelot, 1944; Newcomb et al., 1993; Phipps & Osborne, 1988). Although our distillation of the shared principles helped describe how the pedagogy has been operationalized as a method of instruction, it also called attention to the need to expand our current use of problem-solving. Therefore, we introduced the Integrated Problem-Solving Model for Agricultural Education, which advanced the principles of problem-solving embedded through and between each component of agricultural education’s comprehensive, three-circle model: (a) classroom and laboratory, (b) SAE, and (c) FFA. We argue, therefore, that problem-solving can not only be operationalized as a pedagogy but also a guiding philosophy for SBAE moving forward.  

Implications, Recommendations, and Discussion

In recent decades, a growing number of voices from business, government, and higher education have called for more curricular focus to be placed on enhancing agriculture graduates’ ability to communicate, think critically, and innovate (Blickenstaff et al., 2015; Fields et al., 2003). By fostering these process-oriented skills, it is reasoned that future agriculturalist who enter the workforce will be better prepared to traverse a world fraught with complexities that require them to adapt and solve problems on issues such as climate change, disease, global hunger, and water scarcity (National Research Council, 2014; Roberts et al., 2020; Warren English et al., 2018). In response, this philosophical investigation illustrated the ways in which SBAE could draw on its problem-solving foundations to reposition itself, as the headwinds of change threaten to intensify in the 21stCentury and beyond (Brown, 2016). However, such a reorientation will be complex for the discipline to adopt, with even basic discussions about this change, presenting numerous conceptual and practical hurdles.

As a consequence, we offer the following possibilities for future research and practice. First, more dialogue is needed about problem-solving, when conceptualized as both a method of instruction as well as a guiding philosophy for SBAE. To achieve this, perhaps professional development sessions could be offered by the American Association for Agricultural Education (AAAE) and the National Association of Agricultural Education (NAAE). A concerted effort should also be dedicated to diffusing the Integrated Problem-Solving Model for Agricultural Education. As such, we recommend the model be shared, along with illustrative case study examples, in The Magazine as well as the FFA New Horizons. Teacher educators should also introduce the model to preservice teachers by having them consider innovative ways to integrate such into their future SBAE programs. We also suggest that podcasts, popular press articles, and other communication mediums promote SBAE students, advisors, and programs that use the model in exemplary ways. Finally, we recommend the use of social network analysis to analyze the model’s diffusion challenges better by identifying opinion leaders who influence others in SBAE at the node, dyad, and network levels (Borgatti et al., 2018).

Although problem-solving has a deeply entrenched philosophical foundation in SBAE (Moore & Moore, 1984), more work is needed to explore its dimensions. Therefore, we recommend that research be conducted to examine the programmatic outcomes associated with use of problem-solving as a guiding philosophy. For example, does such an approach improve students’ career readiness, creativity, critical thinking, engagement, learning, and motivation (Roberts & Robinson, 2018)? Further, what motivates a SBAE instructor to adopt such a philosophy in an individual program? Additional research is also needed to examine the outcomes of problem-solving’s use as a method of instruction in SBAE. As an illustration, how do the ways SBAE teachers conceptualize, use, and talk about problem-solving affect student outcomes? And do students who solve problems through team-based learning approaches learn better than those assigned individual problem-solving projects (Figland et al., 2020)? These corollary questions warrant further examination.


Andenoro, A. C., Baker, M., Stedman, N. L. P., & Pennington Weeks, P. (2016). Research priority 7: Addressing complex problems. In T. G. Roberts, A. Harder, & M. T. Brashears, (Eds.), American Association for Agricultural Education national research. University of Florida.

Blickenstaff, S. M., Wolf, K. J., Falk J. M., & Foltz. J. C. (2015). College of agriculture faculty perceptions of student skills, faculty competence in teaching areas and barriers to improving teaching. NACTA Journal, 59(3), 219-226.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks (2nd ed.). Sage.

Boone, H. N. (1990). Effect of level of problem-solving approach to teaching on student achievement and retention. Journal of Agricultural Education, 31(1), 18-26.

Bransford, J. D., & Stein, B. S. (1984). The IDEAL problem solver: A guide for improving thinking, learning, and creativity. WH Freeman and Company.

Brown, K. (2016). Resilience, development and global change. Routledge.

Camp, W. G. & Crunkilton, J. R. (1985). History of agricultural education in America: The great individuals and events. Journal of the American Association of Teacher Educators in Agriculture, 26(1), 57-63.

Cano, J., & Martinez, C. (1991). The relationship between critical thinking ability and level of cognitive performance of selected vocational agriculture students. Journal of Agricultural Education, 32(1), 24-29.

Charters, W.W. (1912). Methods of teaching. Row, Peterson & Company.

Charters, W.W. (1924). Teaching the common branches. The Riverside Press.

Crotty, M. (1998). The foundations of social research: Meaning and perspective in the research process. Sage.

Crunkilton, J. R., & Krebs, A. H. (1967). Teaching agriculture through problem-solving. The Interstate Printers & Publishers, Inc.

Dewey, J. (1910). How we think. Dover Publications.

Dewey, J. (1938). Experience and education. Simon and Schuster.

Dyer, J. E., & Osborne, E. W. (1996). Effects of teaching approach on problem-solving ability of agricultural education students with varying learning styles. Journal of Agricultural Education37, 36-43.

Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1978). Medical problem solving analysis of clinical reasoning. Evaluation & the Health Professions, 13(1), 5-36.

Fields, A. M., Hoiberg, E., & Othman, M. (2003). Changes in colleges of agriculture at land-grant institutions. NACTA Journal, 47(4), 7-15.

Figland, W. L., Blackburn, J. J., & Roberts, R. (2020). Undergraduate students’ perceptions of team-based learning during an introductory agricultural mechanics course: A mixed methods study. Journal of Agricultural Education, 61(1), 262-276.

Flowers, J., & Osborne, E. W. (1988). The problem-solving and subject matter approaches to teaching vocational agriculture: Effects on student achievement and retention. The Journal of Agricultural Education, 29(1), 20-26.

Foor, R. M. & Connors, J. J. (2010). Pioneers in an emerging field: Who were the early agricultural educators? Journal of Agricultural Education, 51(3), 23-31.

Fuhrmann, B. S., & Grasha, A. F. (1983). The past, present, and future in college teaching: Where does your teaching fit? Little, Brown, and Company.

Gokhale, A. A. (1995). Collaborative learning enhances critical thinking. Journal of Technology Education, 7(1), 22-30.

Goossen, C. E., Roberts, R., Kacal, A., Whiddon, A. S., & Robinson, J. S. (2017). The effect of the inquiry-based teaching method on students’ content knowledge and motivation to learn about biofuels, Journal of Southern Agricultural Education Research, 66(1), 1-18.

Hammonds, C. (1950). Teaching agriculture. McGraw-Hill Inc.

Hyland, T. (1993). Vocational reconstruction and Dewey’s instrumentalism. Oxford Review of Education19(1), 89-100.

Johnson, S. D. (1989). A description of expert and novice performance differences on technical troubleshooting tasks. Journal of Industrial Teacher Education, 26(3), 19–37.

Johnson, S. D. (1991). Productivity, the workforce, and technology education. Journal of Technology Education, 2(2), 32-49.

Jonassen, D. H. (2000). Toward a design theory of problem-solving. Educational Technology: Research and Development, 48(4), 63-85. http://link.springer.come/article/10.007%2FBF02300500LI=true#page-1

Koichu, B. (2019). Problem posing in the context of teaching for advanced problem solving. International Journal of Educational Research, 103(1), 1-24.

Kolb, D. A. (1984). Experience as the source of learning and development. Prentice Hall.

Krebs, A. H. (1967). For more effective teaching (2nd ed.). The Interstate Printers and Publishers, Inc.

Lamm, A. J., Shoulders, C., Roberts, T. G., Irani, T. A., Snyder, L. J. U., & Brendemuhl, J. (2012). The influence of cognitive diversity on group problem solving strategy. Journal of Agricultural Education53(1), 18-30.

Lancelot, W. H. (1944). Permanent learning. John Wiley & Sons.

Merwin, W. C. (1977). Models for problem-solving. The High School Journal61(3), 122-130.

Moore, G. E., & Moore, B. A. (1984). The problem-solving approach to teaching: Has it outlived its usefulness? Journal of the American Association of Teacher Educators in Agriculture, 25(2), 3-10.

Moore, G. E. (1988). The forgotten leader in agricultural education: Rufus W. Stimson. Journal of the American Association of Teacher Educators in Agriculture, 29(3),50-58.

Moore, G. E. (2018). Identifying the first generation leaders in agricultural education: The lost Stimson manuscript. Journal of Agricultural Education, 59(4), 137-158.

National Research Council. (2014). Spurring innovation in food and agriculture: A review of the USDA agriculture and food research initiative program. National Academy Press.

Newcomb, L. H., McCracken, J., Warmbrod, J. R., & Whittington M. S. (1993). Methods of teaching agriculture. Pearson.

Newell, A. S., & Simon, H. A. (1972). Human problem-solving. Englewood Cliffs.

Parr, B., & Edwards, M. C. (2004). Inquiry-based instruction in secondary agricultural education: Problem-solving an old friend revisited. Journal of Agricultural Education45(4), 106-117.

Phillips, C. (2010). Socrates café: A fresh taste of philosophy. WW Norton & Company.

Phipps, L. J., & Cook, G. C. (1956). Handbook on teaching vocational agriculture. The Interstate Printers and Publishers, Inc.

Phipps L. J., & Osborne, E. W. (1988). Handbook on agricultural education in public schools. The Interstate Printers & Publishers, Inc.

Phipps, L. J., Osborne, E. W., Dyer, J. E., & Ball, A. (2008). Handbook on agricultural education in public schools (6th ed.). Thomson Delmar Learning.

Retallick, M. S., & Miller, W. W. (2005). Learning for life through inquiry. The Agricultural Education Magazine, 78(3), 17-19.

Roberts, R., & Edwards, M. C. (2015). Service-learning’s ongoing journey as a method of instruction: Implications for school-based, agricultural education. Journal of Agricultural Education, 56(2), 217-233.

Roberts, R., & Edwards, M. C. (2018). Imaging service-learning in The Agricultural Education Magazine from 1929-2009: Implications for the method’s reframing and use. Journal of Agricultural Education. 59(4), 15-35.

Roberts, R., & Edwards, M. C. (2020). Overcoming resistance to service-learning’s use in the preparation of teachers for secondary agricultural education: A reframing of the method’s diffusion challenges. Journal of International Agricultural and Extension Education, 27(1), 15-33.

Roberts, R.& Robinson, J. S.(2018).The motivational changes preservice agricultural education teachers endure while facilitating quality supervised agricultural experiences: A six-week project-based learning experience. Journal of Agricultural Education, 59(1), 255-270.

Roberts, R.,& Stair, K. S., Granberry, T. (2020). Images from the trenches: A visual narrative of the concerns of preservice agricultural education teachers.

Journal of Agricultural Education, 61(2), 324-338.

Roberts, R. W. (1957). Vocational and practical arts education: History, development, and principles. Harper and Brothers.

Roberts, T. G., & Ball, A. L. (2009). Secondary agricultural science as content and context for teaching. Journal of Agricultural Education, 50(1), 81-91.

Robles, M. M. (2012). Executive perceptions of the top soft skills needed in today’s workplace. Business Communication Quarterly75(4), 453-465.

Saldaña, J. (2015). Thinking qualitatively: Methods of mind. Sage.

Salevouris, M. J., & Furay, C. (2015). The methods and skills of history: A practical guide (4th ed.). John Wiley and Sons, Inc.

Sproull, B. (2001). Process problem solving: A guide for maintenance and operations teams. Productivity Press.

Sternberg, R. J. (1981). Intelligence and no entrenchment. Journal of Educational Psychology73(1), 1-16.

Stimson, R. W. (1911). The vocational agricultural school. With special emphasis on part-time work in agriculture. University of Chicago Press.

Stimson, R. W. (1919). Vocational agricultural education by home projects. Macmillan.

Talbert, B. A., Vaughn, R., Croom, D. B., & Lee, J. S. (2007). Fundamentals of agricultural education (2nded.).The Interstate Printers & Publishers, Inc.

Torres, R. M., & Cano, J. (1995a). Examining cognition levels of students enrolled in a college of agriculture. Journal of Agricultural Education, 36(1), 46-54.

Torres, R. M., & Cano, J. (1995b). Increasing thinking skill through HOT teaching. The Agricultural Education Magazine, 68(6), 8-9.

Ulmer, J. D., & Torres, R. M. (2007). A comparison of the cognitive behaviors exhibited by secondary agriculture and science teachers. Journal of Agricultural Education48(4), 106-116.

U.S. Department of Education. (2001). The condition of education, 2001. Author.

Zimmerman, B. J., & Risemberg, R. (1997). Self-regulatory dimensions of academic learning and motivation. In G. D. Phye (Ed.), The educational psychology series. Handbook of academic learning: Construction of knowledge (pp. 105-125). Academic Press.

Warren English, C., Alston, A. J., Graham, A. & Roberts, R. (2018). An analysis of North Carolina superintendents’ views regarding the presence of future-ready graduate attributes within the instructional environment. Journal of Southern Agricultural Education Research, 68(1), 1-15.

Whewell, W. (1840). The philosophy of inductive sciences. Parker.

Prioritizing the Professional Development Needs of First-Year School-Based Agricultural Education Teachers Regarding Career Development Events

Christopher J. Eck, Clemson University,

J. Shane Robinson, Oklahoma State University,

Robert Terry Jr., Oklahoma State University,

PDF Available


Identification of the professional development needs of secondary school teachers is critical to improve teacher capacity. Inservice and preservice school-based agricultural education (SBAE) teachers need a broad spectrum of professional development to be prepared for the variety of duties and expectations demanded of the position. This study used the Borich needs assessment model to identify and prioritize the professional development needs of first-year SBAE teachers in Oklahoma regarding their interest in and competence to train students in the various state-specific career development events (CDEs). Thirty-seven first-year SBAE teachers in Oklahoma participated in the study. The findings revealed that the teachers deemed all 27 CDEs to be important; although, they were not necessarily interested in teaching them all. The CDEs with the highest priority included Livestock Evaluation, Veterinary Science, Meats Evaluation and Technology, Food Science and Technology, and Agricultural Sales. As the agricultural industry and the educational sphere continue to change, so too must those who endeavor to serve in communities and teach agricultural education. As such, identifying, prioritizing, and ultimately addressing the needs of SBAE teachers must be ongoing and sustained over time.


Identifying the professional development needs of secondary school teachers is critical for a multitude of reasons (National Council for the Accreditation of Teacher Education [NCATE], 2010). The identification of needs can improve the capacity of inservice teachers and empower teacher preparation programs to improve future teacher readiness (NCATE, 2010). The same is true for SBAE teachers. Shultz et al. (2014) recognized the need to provide a broad spectrum of skill and knowledge development for both inservice and preservice SBAE teachers due to the vast array of duties and expectations associated with the position (Eck, Robinson, Ramsey, & Cole, 2019; Roberts & Dyer, 2004). Terry and Briers (2010) indicated 21 different roles associated with being a SBAE teacher in addition to the three components identified by the National FFA Organization (2015), i.e., classroom/laboratory instruction, FFA, and supervised agricultural experiences (SAE). These various roles help to provide career awareness to secondary students while also preparing them for their future (Wardlow & Osborne, 2010).

“Agricultural education prepares students for successful careers and a lifetime of informed choices in the global agriculture, food, fiber, and natural resources systems [AFNR]” (The National Council for Agricultural Education, 2012, para. 3). To help facilitate this mission, national AFNR content standards (The National Council for Agricultural Education, 2015) were developed to provide rigorous curricular focus associated with the eight career clusters. These standards were not only intended for classroom instruction but instead were designed to impact all components of a complete program (The National Council for Agricultural Education, 2015).

SBAE exists, in part, to educate and develop students for careers in the agricultural industry (Roberts & Ball, 2009). Fortunately, SBAE teachers can expose students to various agricultural careers through the FFA (Lundry et al., 2015). In particular, SBAE teachers prepare students in a variety of career development events (CDEs), which allow students to take the learning acquired in the classroom and apply it in a competitive setting (Croom et al., 2009; National FFA Organization, 2019). Therefore, assessing teachers’ ability to prepare students in CDEs is an important component worthy of investigation (Terry & Briers, 2010).

CDEs “develop college and career readiness skills” (National FFA Organization, 2019, para. 1) and provide students with an opportunity to apply practical knowledge learned through classroom instruction in challenging, real-world situations (Beekley & Moody, 2002). In addition to content knowledge, critical thinking and problem-solving skills are developed through the preparation and participation in CDEs (Phipps et al., 2008). The development of these additional skills and opportunities presented through CDE participation can ultimately lead to students making better, more informed decisions about their future careers (Talbert & Balschweid, 2006), which can lead to gainful employability (Connors & Mundt, 2001).

For CDEs to be transformative, however, SBAE teachers must be able to provide the necessary training to prepare students for such events. In Oklahoma, the majority of SBAE teachers typically prepare five or fewer teams; although, some prepare as many as 10 teams for the Oklahoma interscholastic event (Lundry Ramsey, Edwards, & Robinson, 2015). Regardless of the number of teams trained, the majority of SBAE teachers prepared teams for CDEs in which they had previous experience (Lundry et al., 2015). Therefore, understanding the degree to which SBAE teachers acquire the knowledge and skills necessary to prepare CDE teams is imperative.

Multiple opportunities exist for SBAE teachers to develop the knowledge and skills necessary to prepare students for CDEs. Traditional teacher preparation programs, which include coursework relative to teaching and learning, content area specific courses, and a student teaching internship (NCATE, 2010), are one way to obtain the expertise necessary to prepare students to compete across a wide variety of CDEs. Traditionally prepared SBAE teachers, who have completed an agricultural education degree through a bachelor’s or master’s degree program along with student teaching, commonly have the advantage of agricultural content-specific coursework, unlike teachers who are alternatively certified (Robinson & Edwards, 2012). However, research suggests teachers who are alternatively certified can be valuable assets to the school, bringing extensive professional experience into the classroom (Ballou, & Podgursky, 1998; Johnson et al., 2005).   

Teachers also develop their knowledge and skills by participating in professional development programs. Roberts and Dyer (2004) identified SBAE teachers have an elevated need for professional development in CDEs regardless of certification pathway. Additionally, Clemons et al. (2018) stated, “the need for focused professional development is vital to the continued success of [SBAE] and teacher growth” (p. 87). Ideally, SBAE teachers should be assessed early and often to determine their learning needs and deficiencies (Birkenholz & Harbstreit, 1987). Unfortunately, however, professional development frequently relies on a presenter telling people what they should know or do (Sharma, 2016) instead of identifying the needs of the audience.

The majority of FFA chapters in Oklahoma participate in CDEs (Lundry et al., 2015). The state-level CDE competition is held during the Oklahoma State University (OSU) interscholastic event each Spring semester on the campus of OSU. In 2019, 428 teams participated in 27 different CDEs (Oklahoma Interscholatics, 2019). CDEs range from single-member events to seven-person teams (National FFA Organization, 2019). The number of Oklahoma teams that participated in each event in 2019 are identified in Table 1 in descending order.

Table 1

Participation for the 2019 OSU Interscholastic Career Development Events (N = 428 Teams)

Livestock Evaluation   62
Land Judging36
Veterinary science31
Agricultural Communications28
Food Science and Technology   27
Agricultural Technology and Mechanical Systems   26
Meats Evaluation and Technology20
Farm and Agribusiness Management   19
Milk Quality and Products18
Dairy Cattle Evaluation and Management14
Horse Evaluation   12
Environmental and Natural Resources   11
Soil and Water Conservation   10
Employment Skills 10
Rangeland Judging9
Homesite Judging   8
Poultry Evaluation7
Turfgrass Management7
Agricultural Issues Forum3
Marketing Plan3
Agricultural Sales2

CDEs “serve as an outgrowth of instruction in the agricultural education classroom for FFA members in grades 7 to 12” (National FFA Organization, 2019, para. 1) and align with the National Agricultural, Food, and Natural Resources (AFNR) Career Cluster Content Standards (National FFA Organization, 2019). Eight career clusters make up the AFNR Content Standards, i.e., power, structural and technical systems, plant systems, natural resource systems, food products and processing systems, environmental service systems, biotechnology systems, animal systems, and agribusiness systems (The National Council for Agricultural Education, 2015). Ultimately, CDEs are aligned and implemented in SBAE programs to further the agricultural education mission which states, “Agricultural education prepares students for successful careers and a lifetime of informed choices in the global agriculture, food, fiber, and natural resources systems” (The National Council for Agricultural Education, 2012, para. 3). 

CDEs serve as a vehicle for the development of critical thinking skills and collaboration while furthering students’ interest in AFNR careers (National FFA Organization, 2019). For SBAE to continue to strive to meet its demand (Roberts & Ball, 2009), SBAE teachers must be prepared and ready to rise to the challenge, preparing students for college and careers. Therefore, understanding SBAE teachers’ deficiencies related to preparing students for CDEs is a crucial task. This task becomes more daunting, considering the diverse needs of SBAE teachers based on the pathway to certification. In particular, because first-year SBAE teachers have been known as needing the greatest amount of professional development (Layfield & Dobbins, 2002), they served as the target population for this study.

Theoretical/Conceptual Framework

The theoretical framework for this study was based on the concept of teacher self-efficacy (Bandura, 1977). Self-efficacy refers to an individual’s belief associated with achieving a desired goal or task (Bandura, 1997). Bandura (1977) identified four types of experiences impacting self-efficacy, with the greatest predictor being mastery experiences. Therefore, SBAE teachers who have experience in a given CDE might feel more efficacious in preparing students to compete in the same event than those without experience. In this study, first-year SBAE teachers in Oklahoma provided their self-perceived competency as it relates to preparing students for each CDE. Unfortunately, novice teachers have very few, if any, mastery experiences related to making students for a CDE. Therefore, they commonly rely on vicarious experiences (Bandura, 1977), which are the second greatest predictor of self-efficacy and refer to the observation of a specific skill or behavior (Bandura, 1977). Influenced by the work of Bandura (1977), teacher self-efficacy refers to an individuals’ ability to engage students in the learning environment and improve their learning outcomes (Tschannen-Moran et al., 1998). Students who learn from teachers high in teacher self-efficacy have been shown to outperform those who learn from teachers lower in teacher self-efficacy (Henson, 2001). Teacher self-efficacy is linked to increased teacher performance and career sustainability (Tschannen-Moran et al., 1998), leading to the importance of this line of inquiry with first-year SBAE teachers, as recruitment and retention continue to be a challenge (Eck & Edwards, 2019).

Purpose of the Study

The purpose of this study was to identify the CDEs in greatest need of professional development according to first-year SBAE teachers in Oklahoma. Four research objectives guided the study:

  1. Describe the personal and professional characteristics (i.e., sex, gender, pathway to certification, highest degree earned, size of program and past CDE experience) of first-year SBAE teachers in Oklahoma,
    1. Identify first-year SBAE teacher’s competency for each of the Oklahoma CDEs,
    1. Identify SBAE teachers’ interest to prepare teams for each of the CDEs, and
    1. Prioritize the CDEs, according to first-year SBAE teachers, in need of professional development using the Borich needs assessment model.

Methods and Procedures

The population of interest for this descriptive pilot study was first-year SBAE teachers in Oklahoma (N = 40) during the 2019 to 2020 school year. A time and place sampling method (Oliver & Hinkle, 1982) was employed during a required new teacher training workshop for SBAE teachers, hosted by Oklahoma Career and Technical Education staff. All (N = 40) first-year SBAE teachers in Oklahoma were required to attend the workshop. Of the 40 first-year SBAE teachers in Oklahoma, 39 were present, and 37 completed the instrument by successfully responding to all questions, resulting in a 92.5% response rate. The instrument was designed to assess SBAE teachers’ competency and interest of the 27 CDEs in Oklahoma following the Borich Needs Assessment Model (Borich, 1980). “The needs assessment model is essentially a self-evaluative procedure which relies on teachers’ judgements about their own performances” (Borich, 1980, p. 42). The model allows researchers to determine if a discrepancy exists between the two poles indicated in the instrument (Borich, 1980). This study sought to determine the discrepancy between teachers’ self-perceived interest and competency to train students in various CDEs. The resulting score will be used to identify professional development opportunities for first-year SBAE teachers in Oklahoma, as discrepancy scores with the greatest positive rank identify the highest priority for professional development (Borich, 1980). The model was utilized to measure the teachers’ interest and competence in preparing students for CDEs and preparing them for careers. To determine where deficiencies existed, Borich’s (1980) mean weighted discrepancy scores (MWDS) was employed. Specifically, the mean weighted competence rating was subtracted from the mean weighted importance rating to determine a discrepancy score. Then, every discrepancy score was multiplied by the mean importance rating to produce a weighted discrepancy score. Finally, the weighted discrepancy scores were totaled and divided by the number of respondents (n = 37) to produce a mean weighted discrepancy score (MWDS). Finally, all MWDS of each item was ranked from high to low to determine the CDEs in greatest need of professional development.

Although Oklahoma FFA conducts 29 state CDEs, only 27 were chosen for this study. The two CDEs omitted (Agricultural Education and the Freshman Agriscience Quiz Bowl) were excluded from the study due to not containing specific content knowledge related to agricultural, food, and natural resource (AFNR) standards. The 27 CDEs included in the instrument were: Agricultural Communications, Agricultural Issues Forum, Agricultural Technology and Mechanical Systems, Agricultural Sales, Agronomy, Dairy Cattle Evaluation and Management, Electricity, Employment Skills, Entomology, Environmental and Natural Resources, Farm and Agribusiness Management, Floriculture, Food Science and Technology, Forestry, Homesite Judging, Horse Evaluation, Land Judging, Livestock Evaluation, Marketing Plan, Meats Evaluation and Technology, Milk Quality and Products, Nursery/Landscape, Poultry Evaluation, Soil and Water Conservation, Rangeland Judging, Turfgrass Management, and Veterinary Science. In addition to the 27 competency and interest assessments of CDEs, participants were asked to identify their intent to prepare teams for each of the 27 CDEs, along with six demographic questions aimed to describe the participants (i.e., sex, age, pathway to certification, highest degree earned, program size, and past experiences related to CDEs). Ultimately, the population of interest for this instrument includes all SBAE teachers; therefore, the sample of first-year SBAE teachers served as an appropriate pilot group.

The instrument was developed in Qualtrics and distributed electronically to first-year SBAE teachers during their new teacher inservice at the state career and technical education office in [City] on August [add date]. Before delivery of the instrument, the research team, consisting of two faculty members and one graduate student in the [Department], evaluated the instrument for face and content validity. The team has more than 50 years of experience teaching agricultural education at the secondary (which included preparing students participating in CDEs) and higher education levels, and each helps prepare students to teach in SBAE programs. Also, all team members have conducted numerous quantitative studies, and two of the team members have used the Borich (1980) model extensively in previous research, qualifying the team as able to assess the face and content validity of the instrument. After review, the instrument was deemed acceptable for the pilot stage of this study.

The instrument complexity, length, and mobile device compatibility were assessed based on the recommendations of Dillman, Smyth, and Christian (2014). The purpose of the study was explained to the participating teachers before they were provided informed consent forms, a QR code, and a weblink to participate on their devices.

After data collection was completed, data were transferred from Qualtrics to the Statistical Program for Social Sciences (SPSS), Version 23, and Microsoft Excel for analysis. Personal and professional characteristics were analyzed in SPSS using descriptive statistics to explain the composition of first-year SBAE teachers in Oklahoma. Mean scores were calculated for interest and competence on each of the 27 CDEs to determine the overall rating from participants. Interest was measured on a four-point scale, where 1 = extremely uninterested, 2 = somewhat uninterested, 3 = somewhat interested, and 4 = extremely interested. Similarly, competence was measured on a four-point scale, where 1 = extremely incompetent, 2 = somewhat incompetent, 3 = somewhat competent, and 4 = extremely competent. For the MWDS analysis, the Excel MWDS calculator developed by McKim and Saucier (2011) was used to determine the professional development needs of first-year SBAE teachers in Oklahoma.


The first research objective sought to describe the personal and professional characteristics of first-year SBAE teachers in Oklahoma. Table 2 displays those characteristics including sex, age, pathway to certification, highest degree earned, program size, and past experiences related to CDEs. First-year SBAE teachers for the 2019 to 2020 school year in Oklahoma ranged in age from 21 to 61 years old, with just over one-half being female (51.4%). Over one-third (f = 14, 37%) entered the profession through a non-traditional certification route; however, 62.2% (f = 23) were traditionally certified, indicating they had completed an agricultural education bachelor’s or master’s degree program. Nearly 92% (f = 34) of the participants had previous experience (i.e., competed as a student in 4H or FFA, prepared a team, participated in professional development, or completed coursework) related to livestock evaluation. More than one-half (f = 19, 51%) had experiences in agricultural communications (see Table 2).

Table 2

Personal and Professional Characteristics of First-Year SBAE Teachers in Oklahoma (n = 37)

 Did not respond 12.7
Age21 to 251848.6
 26 to 3038.1
 31 to 35513.5
 36 to 4038.1
 41 to 50513.5
 51 to 6012.7
 60 +12.7
 Did not respond12.7
Certification PathwayAgEd BS1951.4
 AgEd MS410.8
 Alternatively Certified924.3
 Emergency Certified410.8
 Not Certified12.7
Highest Degree EarnedBachelor’s Degree2875.7
 Master’s Degree924.3
 Doctoral Degree00.0
Program Size (# of students)1 to 2000.0
 21 to 40821.6
 41 to 601129.7
 61 to 8025.4
 81 to 100718.9
 100 to 15038.1
 151 to 20012.7
 201 to 25025.4
 Did not respond25.4
Past CDE ExperienceLivestock Evaluation   3491.9
 Agricultural Communications1951.4
 Agricultural Sales1643.2
 Agricultural Technology and Mechanical
 Land Judging1335.1
 Meats Evaluation and Technology1232.4
 Veterinary Science1232.4
 Horse Evaluation   1129.7
 Dairy Cattle Evaluation and Management1027.0
 Food Science and Technology   1027.0
 Milk Quality and Products1027.0
 Employment Skills 924.3
 Agricultural Issues Forum821.6
 Farm and Agribusiness Management   821.6
 Environmental and Natural Resources   718.9
 Poultry Evaluation718.9
 Soil and Water Conservation   718.9
 Marketing Plan513.5
 Rangeland Judging513.5
 Homesite Judging   25.4
 Turfgrass Management25.4

Additionally, first-year SBAE teachers in Oklahoma were asked to identify their intent to prepare a team for each of the 27 CDEs during the 2019 to 2020 school year. These intentions are displayed in Table 3 in order of the highest intended participation. Livestock Evaluation (f = 28), Agricultural Communications (f = 19), Veterinary Science (n = 15), Land Judging (f = 13), and Agricultural Technology and Mechanical Systems (f = 10) were the top five CDEs for which first-year teachers intended to train. None of the teachers intended to prepare a Homesite Evaluation or Soil and Water Conservation team (see Table 3).  

Table 3

First Year Oklahoma SBAE Teachers Intent to Prepare CDE Teams (n = 37)

Livestock Evaluation   2875.7
Agricultural Communications1951.4
Veterinary Science1540.5
Land Judging1335.1
Agricultural Technology and Mechanical Systems   1027.0
Food Science and Technology   924.3
Meats Evaluation and Technology924.3
Agricultural Issues Forum718.9
Agricultural Sales718.9
Environmental and Natural Resources   718.9
Employment Skills 616.2
Poultry Evaluation616.2
Rangeland Judging616.2
Dairy Cattle Evaluation and Management513.5
Farm and Agribusiness Management   513.5
Horse Evaluation   513.5
Milk Quality and Products513.5
Marketing Plan410.8
Turfgrass Management38.1
Homesite Judging   00.0
Soil and Water Conservation   00.0

The second and third research questions sought to determine the interest and competency levels of first-year SBAE teachers in Oklahoma on a four-point scale of agreement. Livestock Evaluation resulted in the highest mean score for both CDE interest and competency of first-year SBAE teachers, and the Homesite CDE received the lowest mean scores in both areas (see Table 4). Participants deemed they were somewhat interested in Livestock Evaluation and Agricultural Communications, as evidenced by a mean score of 3.0 or greater. The remaining 25 CDEs were in the somewhat uninterested range. Regarding their competence, first-year SBAE teachers perceived themselves to be somewhat to extremely incompetent in all CDE areas except for livestock evaluation (M = 3.07, SD = .79), where they deemed themselves somewhat competent.

The fourth research question sought to prioritize the CDEs, as perceived by first-year teachers, in need of professional development using the Borich (1980) needs assessment model. Livestock Evaluation (MWDS = 3.73) was the CDE possessing the greatest need for professional development (see Table 4). Three other CDEs had an MWDS exceeding 3.0, including Veterinary Science (MWDS = 3.62), Meats Evaluation and Technology (MWDS = 3.34), and Food Science and Technology (MWDS = 3.16). In contrast, two CDEs, Dairy Cattle Evaluation and Management (MWDS = .98) and Electricity (MWDS = .96), had MWDS scores less than 1.0.  

Table 4

CDE interest, Competency, and Mean Weighted Discrepancy Scores of First-Year SBAE Teachers in Oklahoma (n = 37)

CDEInteresta  M                  SD Competencyb M                SD MWDSc
Livestock Evaluation    3.54 .61 3.07 .79 3.73
Veterinary Science 2.97 1.04 2.17 .91 3.62
Meats Evaluation and
 2.81 .78 2.07 .96 3.34
Food Science and
 2.82 .93 2.03 .85 3.16
Agricultural Sales 2.84 .93 2.34 .97 2.84
Farm and Agribusiness
 2.73 .96 2.20 1.03 2.58
Employment Skills  2.92 .97 2.59 .91 2.50
Agricultural Issues Forum 2.62 1.06 2.07 .98 2.48
 3.03 .83 2.83 .83 2.21
Environmental and Natural
 2.46 1.04 1.93 .91 2.19
Milk Quality and Products 2.68 .92 2.30 .88 2.17
Marketing Plan 2.57 .87 2.13 .94 2.15
Land Judging 2.46 .96 2.03 .85 1.99
Agricultural Technology
     and Mechanical Systems   
 2.53 1.06 2.17 .95 1.83
Floriculture 2.51 .99 2.20 1.10 1.83
Rangeland Judging 2.14 1.11 1.63 .93 1.73
Turfgrass Management 2.14 .98 1.67 .92 1.67
Soil and Water Conservation    2.19 1.05 1.77 1.01 1.66
Agronomy 2.19 .95 1.86 .85 1.65
Forestry 2.24 1.04 1.87 .90 1.64
Poultry Evaluation 2.16 1.07 1.73 .83 1.64
Horse Evaluation    2.33 1.12 1.97 1.22 1.62
Entomology 2.25 1.05 1.90 1.03 1.50
Nursery/Landscape 2.19 1.02 1.90 .96 1.42
Homesite Judging    1.78 .95 1.52 .79 1.06
Dairy Cattle Evaluation and
 2.27 .99 2.27 1.08 .98
Electricity 2.08 .92 2.00 .95 .96

Note. aInterest items were on a 4-point scale of agreement, where 1 = Extremely uninterested, 2 = Somewhat uninterested, 3 = Somewhat interested, 4 = Extremely interested. bCompetency items were on a 4-point scale of agreement, where 1 = Extremely incompetent, 2 = Somewhat incompetent, 3 = Somewhat competent, 4 = Extremely competent. cMWDS = Mean Weighted Discrepancy Score.


This study sought to identify the professional development needs of first-year SBAE teachers in Oklahoma, based on their interest and competence as it relates to preparing students for CDEs. The findings of this study resulted in multiple conclusions. Based on the positive MWDS, first-year SBAE teachers in Oklahoma deem all 27 CDEs to be of value; although, they were not necessarily interested in preparing student teams for all the CDEs. As the majority of SBAE teachers in Oklahoma typically prepare five or fewer teams (Lundry et al., 2015), teachers not having an interest in preparing students for all CDEs is realistic.

First-year teachers’ interest in CDEs exceeds their self-perceived competence to prepare students for them. Except for Dairy Cattle Evaluation and Management, which had the same ratings (M = 2.27) for interest and competence, first-year teachers rated 26 of the 27 CDEs higher on the interest scale than on the competence scale. This finding is consistent with previous research using Borich’s (1980) needs assessment model (Radhakrishna & Bruening, 1994; Robinson et al., 2007).

The CDEs with the greatest MWDS are the highest priority (Borich, 1980) for first-year SBAE teachers in Oklahoma, including Livestock Evaluation, Veterinary Science, Meats Evaluation and Technology, Food Science and Technology, and Agricultural Sales. Regarding the teachers’ past CDE experiences, these five were some of the highest regarding their participation. Specifically, 92% of these teachers had participated in the Livestock Evaluation CDE, giving support to teachers’ perceived mastery and vicarious experiences (Bandura, 1977; Tschannen-Moran et al., 1998). Such experiences play a significant role in motivating teachers to continue learning about these content areas and preparing students to participate in them. Ultimately, these five CDEs should be given the highest priority for future professional development offerings for SBAE teachers in Oklahoma.

Professional development related to CDEs in Oklahoma based on the findings of this study provides SBAE teachers an opportunity to increase their self-efficacy through mastery and vicarious experiences (Bandura, 1977). This investment in additional purposeful professional development aims to improve the individual’s teacher self-efficacy for performing tasks related to and within these CDEs. Additionally, CDE participation is intended to align with AFNR Career Cluster Content Standards being taught within the SBAE program (The National Council for Agricultural Education, 2015); therefore, the increased self-efficacy serves the teacher in multiple capacities. Teachers have the opportunity to enhance their ability to prepare students for CDEs and careers while also providing students an opportunity to acquire over 20 additional workplace skills through CDE participation (Lundry et al., 2015). 


Agricultural education faculty at OSU should look for ways to incorporate the top five CDEs (i.e., Livestock Evaluation, Veterinary Science, Meats Evaluation and Technology, Food Science and Technology, and Agricultural Sales) into the existing curriculum and plan of study. In particular, the findings should be shared with faculty who teach courses in these areas, and attempts should be made to highlight these CDEs in classes with students whenever possible. Also, students should be encouraged to volunteer for the Oklahoma FFA Interscholastic Event held at Oklahoma University each Spring by participating in a CDE area in which they lack competence and experience.

The findings of this study should also be shared with Career and Technical Education supervisors and other interested personnel who provide professional development to first-year SBAE teachers. Specifically, those delivering professional development sessions should be encouraged to focus first on the content areas involving Livestock Evaluation, Veterinary Science, and Meat Evaluation and Technology; these CDEs had the highest MWDS and therefore demand the greatest attention related to professional development in Oklahoma. Further, additional professional development for first-year teachers in Oklahoma should be considered for the remaining CDEs with elevated MWDS once the top five have been satisfied. Additions to pre-service agricultural education teacher preparation coursework focused on commonly identified CDE needs of SBAE teachers would help to further the self-efficacy of pre-service teachers as they prepare to enter the profession. These professional development opportunities could occur as ongoing workshops facilitated by content experts (i.e., university faculty or in-service SBAE teachers).

Considering recommendations for research, this study should be replicated for all SBAE teachers in Oklahoma (N = 454) (Oklahoma Career Tech, 2019), as the use of first-year SBAE teachers was intended as a pilot group for the instrument. Agricultural education faculty in other states should consider replicating this study to determine the professional development needs of their SBAE teachers related to CDEs. Replication of this study should be conducted with pre-service teachers to determine their CDE deficiencies. Understanding these gaps might allow teacher educators to advise students differently regarding their plans of study or include pertinent content related to CDEs in their existing courses and agricultural education teacher preparation programs.


The needs of SBAE teachers are diverse when considering career tenure and pathway to certification. Therefore, the demand to increase the development of teacher self-efficacy is pertinent (Eck et al., 2019; Roberts & Dyer, 2004; Shultz et al., 2014). Identifying and meeting the needs of SBAE teachers must be ongoing and sustained over time. Although Livestock Evaluation was identified as the CDE in which respondents had the most previous experience, it was still considered as the highest priority for first-year SBAE teachers in Oklahoma. Additionally, Livestock Evaluation had the greatest number of teams participate in a given year at the Oklahoma State FFA CDE Interscholastic (see Table 1) (Oklahoma Interscholatics, 2019). Furthering the understanding of first-year SBAE teachers in Oklahoma provides stakeholders an opportunity to meet the imperative task of preparing them to meet the demand highlighted by Roberts and Ball (2009) who advocated for developing students for positions within the agricultural industry, with AFNR career exploration through CDEs at the state and national levels (National FFA Organization, 2019). The development of purposeful professional development will allow SBAE teachers an opportunity to increase their self-efficacy (Bandura, 1977), as their participation in such programs serves as an investment in their education, leading to improved competence in preparing students for CDEs.


Bandura, A. (1977). Social Learning Theory. Prentice-Hall.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory.

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

Birkenholz, R. J., & Harbstreit, S. R. (1987). Analysis of the in-service needs of beginning
            vocational agriculture teachers. The Journal of the American Association of Teacher
            Educators in Agriculture, 28
(1), 41–49.

Ballou, D., & Podgursky, M. (1998). Teacher recruitment and retention in public and private

            schools. Journal of Policy Analysis and Management, 17(3), 393–417.

Beekley, B., & Moody, L. (2002). Career development events: An example of authentic learning.
            The Agricultural Education Magazine, 75(1), 16–17.

Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. Journal of
            Teacher Education, 31
(3), 39–42.

Clemons, C. A., Heidenreich, A. E., & Lindner, J. R. (2018). Assessing the technical expertise
            and content needs of Alabama agriscience teachers. Journal of Agricultural Education,
(3), 87–99.

Connors, J. J., & Mundt, J. P. (2001). Experiential education and career development events. The Agricultural Education Magazine, 73(6), 6–7.

Croom, B., Moore, G. E., & Armbruster, J. (2009). An examination of student participation in
            national FFA career development events. Journal of Southern Agricultural Education
            Research, 59
(1), 109–124.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed mode
            surveys: The tailored design method
(4th ed.). John Wiley & Sons Inc.

Eck, C. J., Robinson, J. S., Rmasey, J. W., & Cole, K. L. (2019). Identifying the characteristics  
            of an effective agricultural education teacher: A national study. Journal of Agricultural  
            Education, 60
(4), 1–18. doi:10.5032/jae.2019.04001.

Henson, R. K. (2001). Teacher self-efficacy: Substantive implications and measurement dilemmas. Invited Keynote Address. Paper presented at the Annual Meeting of the Educational Research Exchange. San Antonio, TX.

Johnson, S. M., Birkeland, S. E., & Peske, H. G. (2005). Life in the fast track: How states seek to

            balance incentives and quality in alternative teacher certification programs. Educational

            Policy, 19(1), 63–89.

Layfield, K. D., & Dobbins, T. R. (2002). Inservice needs and perceived competencies of South
            Carolina agricultural educators. Journal of Agricultural Education, 43(4), 46–55.

Lundry, J., Ramsey, J. W., Edwards, M. C., & Robinson, J. S. (2015). Benefits of career development events as perceived by school-based, agricultural education teachers. Journal of Agricultural Education, 56(1), 43–57.

McKim, B. R., & Saucier, P. R. (2011). An Excel-based mean weighted discrepancy score
            calculator. Journal of Extension, 49(2).

National Council for the Accreditation of Teacher Education (NCATE). (2010). The CAEP standards.

National FFA Organization. (2015). Agricultural education. Author.

National FFA Organization. (2019). Career and leadership development events.

National FFA Organization. (2019). Career and leadership development events.

Oklahoma Interscholatics. (2019). 2019 CDE Results. Retrieved from

Oliver, J. D., & Hinkle, D. E. (1982). Occupational education research: Selecting statistical procedures. Journal of Studies in Technical Careers, 4(3), 199–208.

Phipps, L. J., Osborne, E. W., Dyer, J. E., & Ball, A. L. (2008). Handbook on agricultural education in public schools (6th ed.). Thomson Delmar Learning.

Radhakrishna, R. B., & Bruening, T. H. (1994). Pennsylvania study: Employee and student perceptions of skills and experiences needed for careers in agribusiness. NACTA Journal, 38(1), 15–18.

Roberts, T. G., & Ball, A. L. (2009). Secondary agricultural science as a content and context for
 teaching. Journal of Agricultural Education, 50(1), 81–91.

Roberts, T. G., & Dyer, J. E. (2004). Characteristics of effective agriculture teachers. Journal of Agricultural Education, 45(4), 82–95.

Robinson, J. S., Garton, B. L., & Vaughn, P. R. (2007). Becoming employable: A look at graduates’ and supervisors’ perceptions of the skills needed for employability. NACTA Journal, 51(2), 19–26.

Robinson, J. S., & Edwards, M. C. (2012). Assessing the teacher self-efficacy of agriculture instructors and their early career employment status: A comparison of certification types. Journal of Agricultural Education, 53(1), 150–161.

Sharma, A. (2016). Professional development of teachers and teacher educators. Indian Journal of Applied Research, 6(4), 24–43.

Shultz, M. J., Anderson, R. G., Shultz, A. M., & Paulsen, T. H. (2014). Importance and
            capability of teaching agricultural mechanics as perceived by secondary agricultural
            educators. Journal of Agricultural Education, 55(2), 48-65.

Oklahoma Career Tech. (2019). Agricultural education: Contact us.

Talbert, B. A., & Balschweid, M. A. (2006). Career aspirations of selected FFA members.
            Journal of Agricultural Education, 47(2), 67–80.

Terry, R., Jr., & Briers, G. E. (2010). Roles of the secondary agriculture teacher. In R. Torres, T.
            Kitchel, & A. Ball (Eds.), Preparing and advancing teachers in agricultural education
            (pp. 86-98). Curriculum Materials Service, The Ohio State University.

The National Council for Agricultural Education. (2012). Agricultural education.

The National Council for Agricultural Education. (2015). National AFNR content standards.

Tschannen-Moran, M., Woolfolk Hoy, A., & Hoy, W. K. (1998). Teacher efficacy: Its meaning
            and more. Review of Education Research, 68(2), 202–248.

Wardlow, G. W., & Osborne, E. W. (2010). Philosophical underpinnings in agricultural
            education. In R. Torres, T. Kitchel, & A. Ball (Eds.), Preparing and advancing teachers
            in agricultural education
(pp. 16-29). Curriculum Materials Service, The
            Ohio State University.