Vol. 75

Jacob Englin In Quantitative, Teaching & Learning in Undergraduate Academic Programs, Vol. 75

Determining Instructional Design Effects on Self-Efficacy, Interest, and Knowledge in a Small Engines Course

Authors

Will Doss, University of Arkansas, wd009@uark.edu

Christopher M. Estepp, University of Arkansas, estepp@uark.edu

Donald M. Johnson, University of Arkansas, dmjohnso@uark.edu

Kobina Fanyinkah, University of Arkansas, kobinaf@uark.edu

PDF Available

Abstract

Small engine maintenance and repair is a topic in which school-based agricultural education teachers lack technical skills and need instructional planning professional development. At the college level, this topic is taught to preservice SBAE teachers and serves as example that can be used at the secondary school level. To maximize the quality of instruction in this area, a critical evaluation of procedures used to teach small engine concepts at the post-secondary level was conducted. This study examined three factors regarding instruction in a small engines course: student knowledge, interest, and self-efficacy. A quasi-experimental design was used to compare pre and post knowledge levels, perceived self-efficacy, and subject matter interest of participants on the topics of precision measurement and carburetor part identification and function. Participants received instructional treatments designed based on situated learning theory related to each specific topic. Having two different topics allowed for an internal replication of the study. We found the precision measurement instructional treatment resulted in a significant increase in knowledge scores and slight decreases in self-efficacy and interest. For the carburetor topic, there were increases in knowledge, though not significant, and decreases in measured perceived self-efficacy and interest. For the internal replication, the direction of changes in self-efficacy, interest, and knowledge were the same across both designs and with both topics, however, significance was not always the same. Instructors at the post-secondary level and secondary SBAE teachers could use methods described in this study to teach precision measurement and carburetor part identification and function if their goal is to increase knowledge of these topics. We recommend conducting further research to identify ways to maintain or increase self-efficacy and interest while gaining knowledge. Additional testing of instructional designs using situated learning theory in agricultural mechanics and other agricultural topics is also encouraged.

Introduction

Small engine maintenance and repair is a subject many SBAE teachers have been expected to teach; however, it is the least taught agricultural mechanics subject at the post-secondary level (Clark et al., 2021). Despite not being taught often, those who took a college course in small engine agricultural mechanics were associated with being more competent in teaching small engine repair, highlighting a benefit of the course (LaVergne et al., 2018). However, due to the lack of instruction on small engine maintenance and repair, it is an area where current SBAE teachers lack technical skills (Wells & Hainline, 2021) and was identified as a topic where SBAE teachers need professional development in instructional planning and evaluation (Hainline & Wells, 2019; Peake et al., 2007). SBAE teachers lack depth in their instruction on small engines and believe competencies related to small engines are only somewhat important (Rasty & Anderson, 2025). Additionally, prior research found preservice teachers had poor content knowledge related to small engines, affecting their ability to troubleshoot engine problems and likely the quality of instruction they will provide in their future classrooms on the topic (Blackburn et al., 2014).

Quality of instruction is an important element of student learning and ultimately career-readiness (National Research Council [NRC], 2000), particularly for preservice school-based agricultural education (SBAE) teachers (Shoulders et al., 2013). Courses on small engines have been used to assess the quality of instruction from several different perspectives. At both the secondary and post-secondary levels, students taking a course in small engines had higher success rates with small engine troubleshooting when using think-aloud pair problem solving compared to those who did not use the learning technique (Pate & Miller, 2011; Pate et al., 2004). The effects of cognitive learning styles on the ability to solve problems related to engine troubleshooting has also been tested (Blackburn & Robinson, 2017; Blackburn et al., 2014). These studies indicated students with a more innovative cognitive style are less likely to hypothesize correctly when troubleshooting small engine problems compared to students with a more adaptive learning style. Other researchers have examined the positive impact on perceived importance and ability to teach small engine repair skills from offering a two-day professional development event for teachers (Anderson et al., 2022) and how using a small engine diagnostics mobile app can improve competency and knowledge in small engine troubleshooting (Valdetero et al., 2015).

Because of expectations placed on SBAE teachers to teach small engines and the minimal instruction received, the need existed to maximize the quality of instruction in this area at the University of Arkansas. A critical evaluation of procedures used to teach small engine concepts at the post-secondary level was needed to identify best practices for increasing the quality of instruction preservice SBAE teachers receive in small engine courses.

Literature Review/Theoretical Framework

This study examined three important factors regarding instruction in a small engines course: student knowledge, interest, and self-efficacy. According to the literature, using diverse instructional methods impacts the effectiveness of teachers, quality of instruction, and students’ knowledge gains (NRC, 2000; Rosenshine & Furst, 1971). While instruction in agricultural mechanics has historically relied upon varying hands-on, student-centered methods taught in the classroom or laboratory (Newcomb et al., 2004; Talbert et al., 2022), few studies have examined student learning in agricultural mechanics using experimental designs. More studies have explored students’ perceptions, and when tasks are perceived as difficult, student knowledge gains, student interest, and self-efficacy can be negatively affected (Niemivirta & Tapola, 2008). Conversely, knowledge gains and increased student performance have been related to higher self-efficacy (Bailey et al., 2017). Interest, however, was not always related to task performance or knowledge gain (Hackett & Campbell, 1987; Nuutila et al., 2021). Related studies have shown that knowledge gains are not substantially impacted by either student interest or self-efficacy, it was likely the instructor who played a larger role through quality instruction (Guo et al., 2020).

Findings from the literature on the effects of instruction on knowledge, interest, and self-efficacy were mixed. To better understand how these variables relate, we can consult the expectancy-value theory (EVT). Rooted in Atkinson’s (1957) seminal work, modern EVT posits a student’s level of success or achievement of a task can be influenced by their beliefs about how well they will do on the task and the extent to which they value the activity (Wigfield, 1994; Wigfield & Eccles, 2000). Expectancy, or belief about how well students will do on an upcoming task, is often linked to Bandura’s self-efficacy construct in which the more positive one’s self-efficacy beliefs are toward a particular task, the greater the influence on one’s expectation for success (Wigfield et al., 2021). Although Wigfield and Eccles (2000) argued self-efficacy and expectancy are not exactly equal because self-efficacy evaluates the present and expectancy refers to future performance, self-efficacy measures are very similar and are often used, especially for task-specific situations.

In addition to expectancy, values contribute to achievement and as such have been categorized into four types: attainment value, intrinsic value, utility value, and cost (Eccles, 2005). According to Eccles, attainment value is the personal importance of doing well on a task because it is linked to an individual’s personal and social identities. Intrinsic value is the enjoyment one gets from performing the activity or their subjective interest in the activity. Utility value refers to how individuals believe a task relates to current and future goals. Finally, cost is what one has to give up or negatively experience in order to engage in the activity. Overall values related to doing an activity are collectively shaped by all four areas previously described and provide for the reason one does an activity (Mathew et al., 2022).

An individual’s expectancy and values both interact and can be used to predict academic achievement (Mathew et al., 2022). This study measured achievement through knowledge scores on a quiz, intrinsic values were evaluated with an interest scale, and expectancy was estimated with a self-efficacy scale. Our measures were topic specific on two different lessons taught in a college level small engines course. Due to the limited size of our accessible population, we did not attempt to predict achievement, rather we were interested in observing changes in expectancy, value, and achievement after an instructional intervention.

Situated learning theory (SLT) served as the theoretical model for instructional design for the lessons taught in this study (Bell et al., 2013; Green et al., 2018). According to SLT, the learning environment can be composed of three different areas: the use of a constructivist learning approach, teaching and evaluation in an authentic context, and the use of social interaction to enhance learning (Bell et al., 2013; Green et al., 2018). SLT highlights the need for all these areas to be present to increase long-term learning (Bell et al., 2013; Green et al., 2018). To achieve the purpose of this study, we used a constructivist approach by designing lessons where students’ pre-lesson knowledge was assessed and then built upon using varying instructional methods. Laboratory activities served as an authentic context for learning and evaluation to occur. As part of evaluation in an authentic context, students used a reflection video to self-evaluate their knowledge on lesson topics. These videos allowed students to evaluate their own performance and recall what they did during a task by providing real-time, vivid, and physical evidence of their performance (Arikan & Bakla, 2011; Borg & Al-Busaidi, 2012). Social interaction was incorporated in the learning environment by pairing students to work together on various activities and videos. The use of cooperative learning has the potential to deepen understanding of course materials, and hence was chosen as a key component in instructional design (Gregg & Bowling, 2023).

Purpose and Objectives

The purpose of this study was to assess the effectiveness of an instructional treatment using video reflection on student learning. The objectives that guided the study were:

Compare pre and post knowledge levels, perceived self-efficacy, and subject matter interest of participants on the topic of precision measurement.
Compare pre and post knowledge levels, perceived self-efficacy, and subject matter interest of participants on the topic of carburetor part identification and function.

Methods

The population of this study was undergraduate students in the Small Power Units/Turf Equipment course at the University of Arkansas (N = 32) in the spring 2023 semester. The course was designed for junior level students and was focused on concepts related to small engine theory, operation, and maintenance in the context of small turf equipment. After IRB approval was granted, two course topics were selected for assessment: precision measurement and carburetor part identification and function. These two topics were chosen because the course instructors identified them as topics in which students historically have had difficulty understanding. The evaluation of the effectiveness of a new design for teaching these topics was desired.

This quasi-experimental study utilized two Campbell and Stanley (1963) designs (Figure 1) including a one-group pretest-posttest (design 2) and a separate-sample pretest-posttest (design 12). These designs were chosen over an experimental design because participation in the treatment was a course requirement for all students to ensure fairness, preventing the use of random assignment required for a true experimental design. Design 2 compared pretest (O₁) and posttest (O₂) scores for control group participants only. Design 12 compared pretest scores (O₁) of the control group to posttest scores (O₂) of the treatment group. According to Campbell and Stanley (1963), design 12 controls for all threats to external validity and all threats to internal validity except for history, maturation, and the interaction of the two. However, we did not consider history or maturation a threat due to the short duration of this study. Design 2 served as internal replication where the entire study was replicated by completing the process with the topic of precision measurement and then with carburetor part identification and function.

Figure 1

Research Design with Statistical Comparisons for Designs 2 and 12 (Campbell & Stanley, 1963)

The 20-item instrument used for pretest and posttest measures for both instructional topics included three sections: perceived subject matter self-efficacy (nine items), subject matter interest (11 items), and subject matter knowledge (5 items). We chose to use the self-efficacy scale from the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich & De Groot, 1990) because it was designed to measure subject-specific self-efficacy and had a reported reliability of α = .89. Sample items included “I expect to do very well on this topic” and “Compared with other students in class I think I know a great deal about this topic.” Items were rated on a Likert-type scale (1 = not at all true of me to 7 = very true of me). Subject matter interest was measured with the general interest scale from the Gable-Roberts Attitude Toward School Subjects (GRASS) instrument and had a reported reliability of α = .94 (Gable & Roberts, 1983). Sample items included “The subject fascinates me” and “I look forward to my class in the subject.” Items for the general interest scale were rated from 1 = strongly disagree to 5 = strongly agree.

The measure for subject matter knowledge was a five-question, multiple choice quiz developed by the research team with possible scores ranging from zero to 100. While a five-question quiz was chosen to remain consistent with other quizzes given throughout the semester in the course. Questions were created to assess instructional objectives for each topic. For the instructional topic of precision measurement, sample questions included: “Which tool would be most appropriate for measuring piston ring gap?” and “Determine the micrometer reading for the pictured 2”-3” micrometer.” For the instructional topic of carburetor part identification and function, sample questions included “Which of the following carburetor parts is the arrow pointing to in the illustration below?” and “What is the part of the carburetor that carries the fuel from the bowl to the venturi called?” Each question included four possible answer choices. Pre-test and post-test instruments were identical for both stages of measurement. Pretests were administered one class day before the instructional treatment, followed by two periods of classroom/laboratory instruction, and then a posttest the class day after treatment was completed, resulting in two weeks between the pretest and posttest.

The instructional treatment for precision measurement included a short demonstration and discussion of precision measurement tools, a laboratory hands-on guided practice activity measuring engine components, a homework sheet to practice reading micrometers, and a video reflection where students worked in pairs to explain the purpose, identify parts, and demonstrate how to use micrometers, telescoping gauges, and feeler gauges. The instructional treatment for carburetor part identification and function consisted of a one-hour class lecture on fuels and combustion chemistry, a one-hour class lecture on carburetor components, functions, and theory, one lab activity with a complete carburetor tear down, inspection, and reassembly, and a video reflection where students worked in pairs to explain the overall function of a carburetor, identify carburetor parts and their specific functions, and explain how fuel and air flow in the carburetor.

To establish validity for the knowledge scales, two members of the research team familiar with course content and assessment design created questions aligned with content learning objectives. Multiple choice questions were modified from those used in the course textbook and existing test materials already developed for the course. The team worked together to ensure all questions were written at the “remember” level of Bloom’s Taxonomy (Anderson et al., 2001). To determine reliability of both the precision measurement and carburetor instruments, post hoc reliability coefficients were calculated for pretest (n = 16) and posttest (n = 32) scores (Table 1). Alpha coefficients for the self-efficacy and interest constructs were acceptable (Taber, 2018). The reliability of the knowledge construct was low but acceptable, except for the posttest reliability for precision measurement. According to Paek (2015) low reliability coefficients on knowledge scales can be associated with guessing, a possibility with students in the course. Additionally, the low number of items on the quiz likely contributed to reduced reliability.

Table 1

Construct Scale Reliabilities by Topic

	Precision Measurement		Carburetors
Variable	Pretest	Posttest	Pretest	Posttest
Self-efficacy^a	.89	.94	.91	.97
Interest^a	.92	.96	.90	.94
Knowledge^b	.59	.41	.77	.50
^acoefficient alpha, ^bKR-20.

Data from this study were collected through paper copies of the pretests and posttests administered in class. A member of the research team scored knowledge sections and entered all data for each student into a spreadsheet. Frequencies and percentages were used to describe participant demographics while means and standard deviations were used to describe self-efficacy, interest, and knowledge. Paired-samples t-tests were used to compare pretest and posttest scores for Design 2. To compare pretest and posttest constructs for Design 12, a MANOVA was conducted with post hoc comparisons in SPSS v.28. Significance was established a priori at p ≤ 0.05.

Results

Participants in this study identified as mostly male (f = 30, 93.75%). Three participants (9.38%) indicated they were freshman, nine (28.12%) sophomores, 10 (31.25%) juniors, and 10 (31.25%) seniors. For objective one, design 2: participants indicated positive perceptions of their self-efficacy and agreed they were interested in precision measurement on the pretest; mean scores on their knowledge pretest were 43.75 (SD = 30.3) (see Table 2). As required by design 2, paired-samples t-tests were conducted to detect significant differences between the control group’s pretest and posttest scores. A Bonferroni correction was used to adjust for Type I error with significance of 0.0125 established a priori. Results from t-tests indicated there were no significant differences in perceived self-efficacy [t(15) = 1.40, p = .182] or interest [t(15) = 2.19, p = .045]. There was a significant increase in knowledge scores [t(15) = -3.58, p = .003, d = -.89].

Table 2

Precision Measurement Pretest and Posttest Construct Scores for Design 2

	Pretest (n = 16)		Posttest (n = 16)
Variable	M	SD	M	SD
Self-Efficacy	5.38	0.96	5.03	0.85
Interest	4.22	0.51	3.90	0.63
Knowledge	43.75	30.30	77.50	21.76

For design 12, pretest scores from the control group were compared to posttest scores from the treatment group for self-efficacy, interest, and knowledge pertaining to precision measurement. As summarized in Table 3, posttest scores indicated treatment participants had positive perceptions of their self-efficacy and somewhat agreed they were interested in the topic. Mean posttest knowledge scores were 81.25 (SD = 19.96). To test for significance in differences between control pretest and treatment posttest scores, a one-way MANOVA was conducted. The omnibus test indicated a significant difference between groups for one or more dependent variables [Wilkes’ Λ = 0.27, p <.001, n² = .733]. Univariate ANOVAs indicated significantly lower scores for the posttest group on interest [F(1,30) = 11.31, p = .002, n² = .274] and significantly higher knowledge scores for the posttest group [F(1,30) = 17.09, p <.001, n² = .363]. No significant differences were found for self-efficacy [F(1,30) = 0.22, p = .641, n² = .007].

Table 3

Precision Measurement Pretest and Posttest Construct Scores for Design 12

	Pretest (n = 16)		Posttest (n = 16)
Variable	M	SD	M	SD
Self-Efficacy	5.38	0.96	5.22	0.96
Interest	4.22	0.51	3.41	0.81
Knowledge	43.75	30.30	81.25	19.96

For objective two, pretest measures for design 2 of the carburetor topic indicated participants had positive perceptions of their self-efficacy and agreed they were interested in the topic. Mean pretest knowledge scores were 66.26 (SD = 34.03) (Table 4). Results from paired-sample t-tests indicated no significant differences in perceived self-efficacy [t(15) = 0.83, p = .42], interest [t(15) = 2.53, p = .02], or knowledge [t(15) = -1.62, p = .13] between pretest and posttest scores.

Table 4

Carburetor Pretest and Posttest Construct Scores for Design 2

	Pretest (n = 16)		Posttest (n = 16)
Variable	M	SD	M	SD
Self-Efficacy	5.44	0.90	5.21	0.97
Interest	4.34	0.51	4.07	0.48
Knowledge	66.25	20.66	80.00	20.66

For design 12, pretest scores from the control group were compared to posttest scores from the treatment group for self-efficacy, interest, and knowledge pertaining to carburetors. Posttest scores indicated participants had positive perceptions of their self-efficacy and somewhat agreed they were interested in the topic. Mean posttest knowledge scores were 80.00 (SD = 20.66) (see Table 5). The one-way MANOVA resulted in an omnibus test indicating a significant difference between one or more dependent variables [Wilkes’ Λ = 0.689, p = .014, n² = .311]. Subsequent univariate ANOVAs indicated significantly lower scores for the treatment group on self-efficacy [F(1,30) = 4.52, p = .042, n² = .131] and interest [F(1,30) = 7.27, p = .011, n² = .195] while knowledge scores were not significantly higher [F(1,30) = 1.91, p = .177, n² = .060].

Table 5

Carburetor Pretest and Posttest Construct Scores for Design 12

	Pretest (n = 16)		Posttest (n = 16)
Variable	M	SD	M	SD
Self-Efficacy	5.44	0.90	4.69	1.08
Interest	4.34	0.51	3.77	0.17
Knowledge	66.25	20.66	80.00	20.66

Conclusions/Discussion/Implications/Recommendations

Based on the results of this study, we found the precision measurement instructional treatment resulted in a significant increase in knowledge scores for both groups and slight decreases in self-efficacy and interest for both groups. The only significant decrease in interest was observed with Design 12. Possible reasons for the increase in knowledge can be the use of a laboratory experience, having high levels of perceived self-efficacy prior to instruction, and incorporation of all three components of the SLT (Bailey et al., 2017; Bell et al., 2013; Green et al., 2018; Pate et al., 2004). Although it was beyond the scope of this study to determine why there was a decrease in self-efficacy and interest, a decrease is possible according to the literature and is not always associated with knowledge gain (Guo et al., 2020; Hackett & Campbell, 1987; Nuutila et al., 2021). If the topic of precision measurement was more difficult than students expected or they encountered failure with activities and evaluation, decreases in self-efficacy and interest could result (Niemivirta & Tapola, 2008). Based on pretest scores, students had little knowledge of precision measurement prior to instruction, indicating they may have overestimated their ability in the subject.

Similar results emerged for the carburetor topic. For students in both groups there were increases in knowledge, though not significant, and decreases in measured perceived self-efficacy and interest. The decreases in self-efficacy and interest were significant for students in Design 12. Possible causes for decreased self-efficacy and interest discussed for precision measurement could explain decreases for the carburetor topic as well. In the case of knowledge, pretest knowledge was higher than that of precision measurement and may explain why a significant increase was not found. With only 16 students participating in each design group, this study also may not have had the statistical power to detect a difference. The instructional treatment for carburetors included two one-hour class lectures related to the topic, while precision measurement did not. The extra time spent on the topic may have caused the decrease in self-efficacy and interest, because with more time, students can decide they are not interested in the topic and discover their self-efficacy was not as high as previously thought, although this was not a variable measured in this study.

With the internal replication of Designs 2 and 12, one might expect similar results if the study were valid and reliable. While the direction of changes in self-efficacy, interest, and knowledge were the same across both designs and with both topics, significance was not always the same across both designs. A limitation of this study was the small number of students in each group and could contribute to differences among the groups. The research team also acknowledges the low reliability of the knowledge scale; thus, readers should use caution when interpreting results.

Both instructors at the post-secondary level and secondary SBAE teachers could use methods described in this study to teach precision measurement and carburetor part identification and function if their goal is to increase knowledge of these topics. We recommend instructional designers keep in mind the three components of SLT when developing lessons. We also recommend the use of self-recorded videos to evaluate student performance as it provided the benefits previously described (Arikan & Bakla, 2011; Borg & Al-Busaidi, 2012). Relatedly, further research should be conducted to measure the impact videos have on perceived self-efficacy, interest, and knowledge gain. Identifying ways to maintain or increase both self-efficacy and interest while gaining knowledge would also be helpful for those wanting to teach topics related to small engines. To better understand how much time should be spent on specific instructional topics, future studies could measure interest over time so declines in interest can be detected. Another important question to address may be how much interest is enough for learning to occur? Additional testing of instructional designs using SLT in agricultural mechanics and other agriculture topics is also encouraged.

References

Anderson, K., Anderson, R., & Swafford, M. (2022). Effects of a professional development workshop on career and technical education teachers’ perceptions of teaching two-stroke engine repair skills: A preliminary study. Journal of Agricultural Systems, Technology, and Management, 33(1), 1-13. https://jastm.org/index.php/jastm/article/view/10217

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

Arikan, A., & Bakla, A. (2011). Learner autonomy online: Stories from a blogging experience. In D. Gardner (Ed.), Fostering autonomy in language learning (pp. 240-251). Zirve University. http://ilac2010.zirve.edu.tr/Fostering_Autonomy.pdf

Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64(6), 359–372. https://doi.org/10.1037/h0043445

Bailey, J. M., Lombardi, D., Cordova, J. R., & Sinatra, G. M. (2017). Meeting students halfway: Increasing self-efficacy and promoting knowledge change in astronomy. Physical Review Physics Education Research, 13(2), 020140. https://doi.org/10.1103/PhysRevPhysEducRes.13.020140

Bell, R. L., Maeng, J. L., & Binns, I. C. (2013). Learning in context: Technology integration in a teacher preparation program informed by situated learning theory. Journal of Research in Science Teaching, 50(3), 348-379. https://doi.org/10.1002/tea.21075

Blackburn, J. J., & Robinson, J. S. (2017). An investigation of factors that influence the hypothesis generation ability of students in school-based agricultural education programs when troubleshooting small gasoline engines. Journal of Agricultural Education, 58(2), 50-66. https://doi.org/10.5032/jae.2017.02050

Blackburn, J. J., Robinson, J. S., & Lamm, A. J. (2014). How cognitive style and problem complexity affect preservice agricultural education teachers’ abilities to solve problems in agricultural mechanics. Journal of Agricultural Education, 55(4), 133-147. https://doi.org/10.5032/jae.2014.04133

Borg, S., & Al-Busaidi, S. (2012). Learner autonomy: English language teachers’ beliefs and practices. London: British Council. https://www.teachingenglish.org.uk/sites/teacheng /files/b459%20ELTRP%20Report%20Busaidi_final.pdf

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.

Clark, T. K., Anderson, R., & Paulsen, T. H. (2021). Agricultural mechanics preparation: How much do school based agricultural education teachers receive? Journal of Agricultural Education, 62(1), 17-28. https://doi.org/10.5032/jae.2021.01017

Eccles, J. S. (2005). Subjective Task Value and the Eccles et al. Model of Achievement-Related Choices. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). Guilford Publications.

Gable, R. K., & Roberts, A. D. (1983). An instrument to measure attitude toward school subjects. Educational and Psychological Measurement, 43(1), 289-293. https://doi.org/10.1177/001316448304300139

Green, C., Eady, M. J., & Andersen, P. J. (2018). Preparing quality teachers: Bridging the gap between tertiary experiences and classroom realities. Teaching & Learning Inquiry, 6(1), 104-125. https://doi.org/10.20343/teachlearninqu.6.1.10

Gregg, C., & Bowling, A. (2023). Undergraduate student perceptions of cooperative discussion groups as a classroom engagement tool. NACTA Journal, 67(1), 139-145. https://doi.org/10.56103/nactaj.v67i1.92

Guo, Y. M., Klein, B. D., & Ro, Y. K. (2020). On the effects of student interest, self-efficacy, and perceptions of the instructor on flow, satisfaction, and learning outcomes. Studies in Higher Education, 45(7), 1413-1430. https://doi.org/10.1080/03075079.2019.1593348

Hackett, G., & Campbell, N. K. (1987). Task self-efficacy and task interest as a function of performance on a gender-neutral task. Journal of Vocational Behavior, 30(2), 203-215. https://doi.org/10.1016/0001-8791(87)90019-4

Hainline, M. S., & Wells, T. (2019). Identifying the agricultural mechanics knowledge and skills needed by Iowa school-based agricultural education teachers. Journal of Agricultural Education, 60(1), 59-79. https://doi.org/10.5032/jae.2019.01059

LaVergne, D. D., Bakhtavoryan, R., & Williams, R. L. (2018). Using a logistic regression approach to estimate the influence of demographical factors on small engine and welding competencies of secondary agricultural education teachers. The Texas Journal of Agriculture and Natural Resources, 31(1), 1-11. https://txjanr.agintexas.org/index.php/txjanr/article/view/355

Mathew, S., Bright, K., Barrero-Molina, L. B., & Hawkins, J. (2022, April). Expectancy-value theory. OSU motivation in classrooms lab-motivation minute. https://education.okstate.edu/site-files/documents/motivation-classrooms/motivation-minute-expectancy-value-theory.pdf

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

Newcomb, L. H., McCracken, J. D., Warmbrod, J. R., & Whittington, M. S. (2004). Methods of teaching agriculture (3rd ed.). Pearson Prentice Hall.

Niemivirta, M., & Tapola, A. (2008). Self-efficacy, interest, and task performance: Within-task changes, mutual relationships, and predictive effects. Zeitschrift für Pädagogische Psychologie, 21(3/4), 241-250. https://doi.org/10.1024/1010-0652.21.3.241

Nuutila, K., Tapola, A., Tuominen, H., Molnár, G., & Niemivirta, M. (2021). Mutual relationships between the levels of and changes in interest, self-efficacy, and perceived difficulty during task engagement. Learning and Individual Differences, 92, 102090. https://doi.org/10.1016/j.lindif.2021.102090

Paek, I. (2015). An investigation of the impact of guessing on coefficient α and reliability. Applied Psychological Measurement, 39(4), 264-277. https://doi.org/10.1177/0146621614559516

Pate, M. L., & Miller, G. (2011). A descriptive interpretive analysis of students’ oral verbalization during the use of think-aloud pair problem solving while troubleshooting. Journal of Agricultural Education, 52(1), 107-119. https://doi.org/10.5032/jae.2011.01107

Pate, M. L., Wardlow, G. W., & Johnson, D. M. (2004). Effects of thinking aloud pair problem solving on the troubleshooting performance of undergraduate agriculture students in a power technology course. Journal of Agricultural Education, 45(4), 1-11. https://doi.org/10.5032/jae.2004.04001

Peake, J. B., Duncan, D. W., & Ricketts, J. C. (2007). Identifying technical content training needs of Georgia agriculture teachers. Journal of Career and Technical Education, 23(1), 44-54. https://journalcte.org/articles/10.21061/jcte.v23i1.442

Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33

Rasty, J., & Anderson, R. G. (2005). The importance of agricultural mechanics skills training: Implications for agricultural education. Journal of Agricultural Systems, Technology & Management, 36(1), 1-13. https://jastm.org/index.php/jastm/article/view/11453

Rosenshine, B., & Furst, N. (1971). Research on teacher performance criteria. In B.O. Smith (Ed.), Research in teacher education: A symposium (pp. 37-72). Prentice-Hall.

Shoulders, C.W., Stripling, C.T., & Estepp, C.M. (2013). Preservice agriculture teachers’ perceptions of teaching assistants: Implications for teacher education. Journal of Agricultural Education, 54(2), 85-98. https://doi.org/10.5032/jae.2013.02085

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273-1296. https://doi.org/10.1007/s11165-016-9602-2

Talbert, B. A., Croom, B., LaRose, S. E., Vaughn, R., & Lee, J. S. (2022). Foundations of agricultural education (4th ed.). Purdue University Press.

Valdetero, H. E., Kirk, K. R., Dobbins, T. R., & Fravel, P. M. (2015, July 26-29). Teaching small engine diagnostics using mobile app technology. [Paper presentation]. ASABE Annual International Meeting, New Orleans, LA, United States. https://elibrary.asabe.org/pdfviewer.asp?param1=s:/8y9u8/q8qu/tq9q/5tv/L/3471IGHL/HLIHPGGKG.5tv&param2=J/IK/IGIL&param3=HJG.HOK.ILI.K&param4=46279

Wells, T., & Hainline, M. S. (2021). Examining teachers’ agricultural mechanics professional development needs: A national study. Journal of Agricultural Education, 62(2), 217-238. https://doi.org/10.5032/jae.2021.02217

Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49-78. https://www.jstor.org/stable/23359359

Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015 Wigfield, A., Muenks, K., Eccles, J. S., (2021). Achievement motivation: What we know and where we are going. Annual Review of Developmental Psychology, 3(1), 87-111. https://doi.org/10.1146/annurev-devpsych-050720-103500

Jacob Englin In Ag. Communications, Philosophical, Vol. 75

Challenging the Linear Approach: Effective and Efficient Communication of Agricultural Innovations

Authors

Barbara Worley, North Carolina State University, barbara_worley@ncsu.edu

Jason Peake, University of Georgia, jpeake@uga.edu

Nick Fuhrman, University of Georgia, fuhrman@uga.edu

PDF Available

Abstract

Agricultural innovation information has been traditionally communicated by Extension professionals, with dissemination practices informed by linear theories and models. However, effectively and efficiently communicating agriculture and natural resource (ANR) innovations, as well as those specific to the turfgrass industry, has not been approached in a non-linear fashion. Understanding the role of county-based and university-level Extension professionals is necessary to achieve clarity in communication efforts due to an often-seen overlap in the creation and dissemination of ANR and turfgrass information. Factors associated with the role of communicating agricultural and turfgrass innovations were analyzed using secondary data. Through the integration of multi- and interdisciplinary theories, the creation of a new communications framework – one specific for effectively and efficiently disseminating and communicating turfgrass information – allows for a non-linear, holistic demarcation in, and understanding of, university-level and county-based Extension roles. Findings revealed that the role of the Ag Communicator must be considered for crafting communications to promote clarity and concise information transfer. However, additional research is needed to better understand how the Ag Communicator serves as a bridge between the information disseminator and the information creator.

Introduction

Anyone has the ability to create change (Eversole, 2012). Yet the humanistic desire for change that is embedded in Western culture has resulted in our world reaching the moment where climate crisis is upon us, affecting our access to natural resources (Fuchs, 2016; National Aeronautics and Space Administration [NASA], 2022). In just the last decade, our world has experienced the two warmest years since modern recordkeeping began in 1880, and the irreversible effects of human-caused climate change are forecasted to continually worsen (NASA, 2017; NASA, 2022). Now, civilization has a crisis; our planet is a proverbial greenhouse, and not the type where new, innovative turfgrass cultivars are developed through research initiatives of land-grant universities.

Civilization is also facing another crisis – one where the creation and dissemination of information through ever-advancing communication channels has become limited in peer-review. Ferguson (2004) stated that knowledge produced by scientists, and additionally scientific knowledge, cannot be removed, separated, or disengaged from associated and situated social, political, cultural, and economic contexts. Moreover, the ways in which humans interact with scientific information have been impacted by the advent of social media (Dobbins et al., 2021).

We live in a world with readily available access to information. However, with this increase in information, communication challenges intensify with issues surrounding source credibility (Lazer et al., 2018; Ruth et al., 2018). Messages of misinformation and skewed data can be propagated since “anyone can be a communicator if they have a social media account and smart phone” (Kurtzo, 2016, p. 24). In addition to being open sources of misinformation, Fuchs (2016) argued that it is a guise that social media is a collaborative means by which to share information, detached from hierarchical communication structures wielding centralized power.

The difference between broadcasting and social media is that in the first kind of medium there are centers that control the dissemination of information. In social media, every consumer of information can be a producer who creates and disseminates information. It is, however, mere semblance and ideological appearance that the emergence of presumption democratizes the media because the ownership of Facebook, Twitter, and YouTube is not collective and there are hierarchies of reputation, visibility and voice on these media. (Fuchs, 2016, p. 136)

Rethinking Change: A Turfgrass Industry Example

Agricultural innovations are at the forefront of land-grant university research development (Wright, 2012). New turfgrass cultivars to mitigate environmental issues such as water scarcity are being developed in the form of drought tolerant and salinity resistant varieties. Additionally, these new cultivars are being developed globally by university researchers to withstand varying recreational and professional uses (Chawla et al., 2018; Santos & Castilho, 2018). Communicating the benefits effectively and efficiently is therefore necessary to highlight the attributes of the innovative developments coming from university research as well as serving to avert any further detriment to our world.

Second-order change can be described as disruptive or dramatic shifts in thinking or behavior; it is predicated on the need for transformation due to a “crisis” that has unraveled (Bartunek & Moch, 1987, p. 495). This has a direct link with innovations in the turfgrass industry. From the basis of disseminating information related to innovative drought tolerant and salinity resistant turfgrass cultivars, second-order change is needed to challenge the organizational structures in place that dictate the traditional communications processes used for disseminating research-based agriculture and natural resources (ANR) and turfgrass information (Knickel et al., 2009). In doing so, the authors present a case for understanding the pretext for efficient and effective communications in introducing a framework to disrupt the processes rooted in traditional linear models for disseminating information by university-level and county-based Extension professionals.

Theoretical perspectives including the diffusion of innovations (Rogers, 2003), the decision-making model for agriculture and natural resources (Ruth et al., 2018), the Forrester’s social technographics ladder (Li & Bernoff, 2007), Mintzberg’s (1990) management model, and knowledge management and agricultural knowledge management systems (Rivera et al., 2005), are congruently and critically examined in this paper. We must understand the overlap in these five theories that offer promise in being effective and efficient in communicating turfgrass innovations. The authors also made recommendations to become more effective and efficient communicators of turfgrass and broader agricultural innovations moving forward.

Theoretical Foundations

Evans (2006) stated a need existed to draw on various theories not typically utilized in agricultural communications to enhance scholarship. Additionally, Rhoades and Booth (1982) contest that interdisciplinary approaches are necessary for advancing agricultural technologies and solving complex research problems. For the purposes of this study, an integration of interdisciplinary theories, from marketing, management, science communications, and rural sociology, serve as the foundation for the creation of a new communications framework; one specific for effectively and efficiently disseminating and communicating turfgrass information, allowing for a non-linear, holistic demarcation in, and understanding of, university-level and county-based Extension roles.

Diffusion of Innovations

Rogers’ (2003) theory of diffusion of innovations is one of the most utilized yet contested frameworks in understanding and analyzing the dissemination of information and adoption process of new innovations in agricultural communications (Lamm et al., 2019). However, despite the contestation, the multifaceted and complex nature of the framework provides elements that support understanding factors affecting innovativeness, down to an individual’s sense of autonomy within an organization. Therefore, internal characteristics of organizational structures must be considered as they can have a myriad of effects on the “innovativeness of organizations” in relation to communications (Rogers, 2003, p. 411).

In examining organizational factors contributing to the creation and dissemination of turfgrass communications, formalization and interconnectedness are considered. Rogers (2003) defined formalization as “the degree to which an organization emphasizes its members’ following rules and procedures” (p. 412). Million et al. (2018) interpreted formalization in organizational structure as having uniformity in practices to include job position responsibilities. Interconnectedness is described as the exchange and flow of ideas within interpersonal networks (Rogers, 2003). Interconnectedness allows for increased visibility of an innovation through collaborative and coordinated communication efforts. Later detailed as a key component of knowledge management, this flow of information through collaborative non-linear processes results in innovation (Knickel et al., 2009). In the context of the turfgrass industry, this is important because as new innovations are developed at land-grant universities through the efforts of turfgrass Specialists, the effective sharing of information through collaborative, consistent means leads to more efficient and clear messages to be disseminated to end-users. In the broader arena of agricultural innovations such as artificial intelligence, an understanding would allow for the bridging of knowledge for creating enhanced systems. Those systems include those used for measuring water absorption, allowing for efficiency rather than duplication and replication (and potentially competing technologies).

Decision-Making Model for Agriculture and Natural Resources (DMM for ANR)

The decision-making model for ANR (Ruth et al., 2018) was developed to “break the cycle of decisions made with incomplete information and equip practitioners with the foundation needed to efficiently and effectively disseminate information through educational practice and informed communication efforts” (p. 224). The DMM for ANR models how formative theories of communication combined, including Rogers’ (2003) theory of diffusion of innovations, “guide the dissemination of information about complex ANR issues” (Ruth et al., 2018, p. 225). The DMM for ANR has helped researchers to better understand efficient and effective ways for disseminating information about ANR issues. Additionally, the model has been used as the theoretical framework for studying communications applicable to the turfgrass industry by Worley et al. (2022, 2023). Data from these previous studies reveal the role of those creating knowledge, and subsequently crafting and disseminating communications, is often not clear to anyone engaged in the diffusion process due to organizational formality characteristics including administrative expectations. In essence, knowledge creators and disseminators (the communications professionals) are often misaligned in their roles.

Creator or Disseminator: Forrester’s Social Technographics Ladder and the Mintzberg Management Model

The turfgrass industry is a $40 billion industry across the United States (National Turfgrass Federation, 2017). Moreover, as a global ANR commodity, turfgrass industry professionals including Extension, golf course superintendents, landscapers, turfgrass producers, and landscapers have been studied in relation to their use of communications channels. Due to the dual nature and positionality that the turfgrass industry holds in educational research and business contexts, this research is focused on understanding communications related to ANR and turfgrass innovations, and as such, these innovations are developed, and subsequently marketed, for commercial use.

The use of technology in ANR and the turfgrass industry is changing the way engagement occurs with consumers (Li et al., 2007). Forrester, a company devoted to technology and market research, refers to this technological change as social computing, “a social structure in which technology puts power in communities, not institutions” (Charron et al., 2006, p. 6). Li et al. (2007) further examined social computing engagement behavior, outlining how consumers approach technology. Consumers were grouped according to participation behaviors to develop the social technographics ladder.

Each of the six rungs of Forrester’s social technographics ladder is a representation of the level in which someone is invested in their personal online presence. The top rung of the ladder is occupied by the creator, defined by Li et al. (2007) as “online consumers who publish blogs, maintain Web pages, or upload videos to sites like YouTube at least once per month” (p. 3). While these online consumers or participants are described as young and evenly split among males and females, engagement in activity varies, and few are engaged in the three categories outlined. Thus, Li et al. (2007) did not explicitly operationalize the manner in which information is created and communications are developed by this group. Although the ladder figuratively shows an increase in the level of one’s participation in the online ecosystem, participation at one level does not prevent participation anywhere else on the ladder.

The Mintzberg management model (1973) was developed to convey the theory that managerial roles within organizations can be contextualized as 10 roles within three domains: interpersonal, informational, and decisional (Chareanpunsirikula & Wood, 2002). The role of disseminator is defined as an informational role for transferring information to other individuals. While another informational role of spokesperson exists in this model, the disseminator is different due to the context of the information and the relationship with those to whom the information is being passed. Due to it being a managerial framework, the disseminator shares vital information from within and outside the organization, thus implying a true messenger relationship associated with the transfer of information rather than the hierarchical and linear role of the spokesperson (Altamony et al., 2007).

Congruent to these models, previous work by Worley et al. (2023) examined how county-based Extension professionals identified their role as a creator or disseminator, in relation to the behavioral intent to use social media communication channels for disseminating turfgrass innovation information. Thus, within the turfgrass industry and broader agricultural innovations arena, the Forrester and Mintzberg models offer promise due to their explicit framing and operationalization of organizational roles that can be applied to agriculture and Extension communications.

Knowledge Management and Agricultural Knowledge Systems

Agricultural knowledge systems, first constructed in the 1990s, and presented in agricultural knowledge information systems (AKIS) models such as that presented by Rivera et al. (2005), illustrate the bidirectional relationship between research, extension, support, and education. Despite these modeled systems, the construction of agricultural knowledge by universities and research institutions through top-down, linear-based approaches can lead to a disconnect in communication; the dissemination of information, adoption of innovations, and, moreover, the development of knowledge are challenged when multiple perspectives are not considered (Eversole, 2012; Knickel et al., 2009; Masambuka-Kanchewa et al., 2020).

Knowledge management refers to “identifying and leveraging the collective knowledge in an organization,” while knowledge management systems (KMS) provide organizations with the ability to store, access, and transfer knowledge (Alavi & Leidner, 2001, p. 113). Doerfert et al. (2007) noted that the sharing of knowledge is vital to sustainable development and thus proposed a framework using knowledge management as a basis for outlining a strategy that could be applied in agricultural communication contexts. Four priority areas and 18 research questions addressing information dissemination (and exchange) of knowledge were presented. The framework proposed by Doerfert et al. (2007) offered a logical approach through the prioritized areas and corresponding questions for understanding that the sharing of agricultural information should be approached holistically. However, gaps still exist in implementing a collaborative agricultural communications approach to understanding the basis of creating (and subsequently crafting) information, knowledge, and communications, as well as the formative paths for dissemination.

While such agricultural knowledge systems aim for collaborative communication thereby mitigating the dissemination of misinformation, an inherent hegemonic structure exists in application. This top-down approach is nonexistent in the traditional knowledge management paradigm offered by Nonaka et al. (1994). Nonaka et al. (1994) noted that hierarchical sequences for providing solutions and creating change must be altered to dynamic processes through a “shift in our thinking about the nature and functioning of organizations as knowledge-creating systems” (p. 337). In describing how knowledge must be managed, Doerfert et al. (2007) conveyed a dialectical tone that opposes the foundations of knowledge management (Nonaka et al., 1994; Tietze & Dick, 2013).

We do not know what the final picture of our future will look like. However, we must move into this new information era and begin to manage knowledge like never before. This can become a golden age in our shared field of interest if we both embrace and lead this change. The agricultural well-being of society may depend on our ability to communicate effectively in a dynamic knowledge era. (Doerfert et al., 2007, p. 19)

From both object and access to information perspectives as outlined by Alavi and Leidner (2001), for knowledge to be managed it must be objectified – it must be tangible (p. 112). Therefore, empirical analysis of the four key elements that Evans (2006) and Doerfert et al. (2007) acknowledge as central to knowledge management systems – information, knowledge, agents, and tools – is needed. Within the turfgrass and broader agricultural innovations arena, such a pursuit will provide an understanding of how knowledge is created and information is subsequently disseminated, by whom, and through what messages and channels for effective and efficient communications.

Purpose and Objectives

In previous work by Worley et al. (2021, 2022, 2023), the authors identified key individuals and specified media channels for the diffusion of emerging turfgrass innovations, factors influencing communications for sharing information about turfgrass innovations, and the behavioral intent to use social media communication channels for disseminating turfgrass innovations in relation to one’s identified role as a creator or disseminator. Results of these studies revealed that the communication roles of ANR and turfgrass university-level and county-based Extension professionals are not clearly defined, and therefore, a duplication in communications and job duties often occurs. Moreover, organizational structure indicators of formalization and interconnectedness were determined to affect the dissemination of turfgrass information due to administrative expectations (Rogers, 2003). For example, specific communication channels were used over others because of annual evaluation incentives for sharing information through one channel over another (Worley et al., 2021).

A need existed for a more succinct model of communicating ANR and turfgrass information that considers an interconnectedness of ideas and synthesis of knowledge while simultaneously establishing a framework for understanding how the creation, crafting, and dissemination of communications is operationalized. The objectives for a establishing such a communications framework are as follows:

Perform an analysis of secondary data of turfgrass research revealing internal and external variables contributing to an overlap in the creation of communications materials, and the felt or perceived need to subsequently communicate that information.
Perform an analysis of secondary data of turfgrass research revealing the expectations placed by administrators on both university-level and county-based Extension professionals to create communications.

Additionally, the following research questions were developed to guide the formation and application of the framework:

Who is responsible for the creation of information? (i.e., Who are the knowledge producers?)
Who is responsible for crafting the communications?
Who is responsible for disseminating communications?

Conceptual Framework

Results of previous research and future recommendations by Worley et al. (2022, 2023) pointed to the development of a conceptual framework for information to efficiently and effectively be communicated, with university-level and county-based Extension professionals having a clear understanding of their role in the development and dissemination of communications. Defining communication roles as it relates to the creation, crafting, and dissemination of turfgrass innovation information also mitigates the diffusion of misinformation (Ruth et al., 2018). Additionally, expectations of the responsibility for the creation and dissemination of communications placed on university-level and county-based Extension professionals by university administrators must be revealed to outline why dualisms (leading to inefficient and ineffective communication of turfgrass information) occur in roles.

While the authors present a conceptual framework to present the importance of effective and efficient communication of turfgrass innovations through an understanding of Extension roles supported by theoretical frameworks, Ravitch and Riggan (2017) noted that the visual representation of a framework is not necessary when narrative, text-based presentations can suffice. Visual depictions can lead researchers to become more focused on a modeled product rather than the inquiry process. Frameworks in narrative form can be offered in the absence of concept maps (Ravitch & Riggan, 2017). Therefore, a visual model of the framework is not offered as such; instead, the framework is outlined narratively and figuratively through the operationalization of terms, accompanied by a figure and table as they relate to theoretical bases and findings from analyses of secondary data.

Application of the Framework

The authors performed an analysis of secondary data from previous studies by Worley et al. (2022, 2023) to determine internal and external variables contributing to an overlap in the crafting of turfgrass communications materials, and the felt or perceived need of university-level and county-based Extension professionals to communicate the related information. These internal and external variables included how information is communicated (the message and channel), knowledge (including content or explicit), who is communicating (university-level or county-based Extension professionals), resources (time, money, media access and prowess), and organizational culture (expectations).

Factors contributing to the need expressed by university-level Specialists working with the turfgrass industry to maintain a presence across roles defined as creator or disseminator in the creation of information (and the development of communications materials) and the dissemination of the information were analyzed. In an analysis of responses from county-based Extension professionals, the authors examined factors contributing to a similar need to act in the role of both creator and disseminator, particularly in which the creator included crafting communications materials rather than solely disseminating information based on materials already developed by university-level Specialists. Expectations of university-level and county-based Extension professionals to create, craft, and disseminate turfgrass communications were also analyzed.

Central to agricultural communications were four key concepts of knowledge management: information flow and function (e.g., creation, retention, transfer, and use); types of knowledge (explicit, implicit, tacit); agents (e.g., whose role is it? – individuals, organizations); and tools (knowledge/content repositories, communications) (Evans, 2006; Doerfert, 2007). Considering these four knowledge management concepts coupled with terms defined by Li et al. (2007), Rogers (2003), Mintzberg (1990), and Eversole (2012), the authors operationalize the role of Ag Communicator as being one who drafts and/or crafts communications from information that is provided by the creator (see Table 1).

Table 1

Operationalization of the Role of Ag Communicator in relation to other Communications Roles

Creator	Ag Communicator	Disseminator
Opinion Leader (Rogers, 2003)	One who is charged with drafting/crafting communications	Change Agent (Rogers, 2003) Translation Agent (Eversole, 2012)

Note. Li et al. (2007) introduced the term creator in the socialtechnographics ladder. The authors operationalize Ag Communicator in this manuscript. The use of the term disseminator is in accordance with the Mintzberg management model.

In a traditional linear-approach, the one constructing, drafting, and/or crafting communications is often undefined and unclear. The “waters become muddied” when roles of creating and disseminating information overlap, including the crafting of communications materials such as social media posts and factsheets. In a holistic approach to turfgrass communication, the role of the Ag Communicator can be defined and designated through the understanding of structural factors and expectations. This is not to say that one individual cannot occupy all three roles, as shown in the social technographic ladder. Instead, the designation of roles is presented to provide for effective and efficient communication of turfgrass information.

Is this the Role or are these (Un)Realistic Expectations?

In the analysis of the secondary turfgrass data, the authors determined several factors as important to communicating agricultural information. As shown in Figure 1, factors revealed from the data are illustrated as related to knowledge management concepts (and knowledge taxonomies), internal characteristics of organizational structures, and the roles of creator and disseminator as outlined by Worley et al. (2023). These factors, internal characteristics, and designated roles of creator and disseminator are presented relative to the diffusion of innovations (Rogers, 2003), the decision-making model for agriculture and natural resources (Ruth et al., 2018), the Forrester’s social technographics ladder (Li & Bernoff, 2007), Mintzberg’s (1990) management model, and knowledge management and agricultural knowledge management systems (Rivera et al., 2005).

The appropriate message and subsequent channel for communicating that information was noted to be vitally important to both university-level and county-based Extension professionals. Content knowledge as it relates to the individual, whether the university-level Specialist or county-based Extension professional, was also regarded as being relevant to an Extension professional’s perceived need to act in the role as a creator and/or disseminator. Expressed barriers to creating and disseminating information included access to fiscal, schedule, and multimedia resources. Further, the culture of Extension and its organizational expectations was influential to both university-level and county-based Extension professionals for creating and disseminating information. In the analysis of the secondary data, several of the factors revealed overlaps, showing an interconnectivity in the aspects presented in the framework.

Figure 1

Factors relevant to Communicating Turfgrass Innovations, as related to Theoretical Bases

Note. Each of the theoretical models and/or theories depicted inform the conceptual framework of introducing and operationalizing the role of Ag Communicator.

Knowledge

Whereas some university-level Specialists felt they possessed explicit knowledge to an extent beyond county-based Extension professionals due to academic specialization or advancement in degree field, Extension professionals directly engaged with the community possess inherent and experienced tacit and implicit knowledge due to relationships built with community members, as well as access to explicit university-level knowledge (Knickel et al., 2009). County-based Extension professionals often participate in a “paradox of dual embeddedness” that includes one’s formal work organization coupled with the community within which they live and work (Eversole, 2012, p. 38). This combination of professional and local or indigenous knowledge allows for agricultural communicators to “connect such knowledge with knowledge generated through scientific agricultural research (Evans, 2006, pp. 22–23). This implies two things: (1) university-level and county-based Extension professionals must be trained to better understand each other’s role, and (2) community level knowledge must be considered, acknowledged, and applied in the communications processes regarding ANR and turfgrass innovations.

How Information is Communicated

The participatory relationship that a county-based Extension professional has with their community members provides them with access to not only local knowledge but also the advantage of using those relationships to garner a better understanding of the types of communications needed and desired by their clientele (Evans, 2006; Eversole, 2012). County-based Extension professionals noted the importance of effective and efficient information transfer to clientele and how diverse forms of communication must be utilized to increase impact. However, this flow of information is only effective and efficient if clientele value their own local knowledge, have an ability to express their local knowledge, and feel that the information that is being communicated is accurate and meets their needs (Masambuka-Kanchewa et al., 2020). This also implies that university-level Specialists must provide county-based Extension professionals with information in a way that is transferable to local clientele. It is imperative that agricultural knowledge and innovations become more effective in the convergence of public and private interests (Knickel et al., 2009).

By Whom?

The perception that one’s role within Extension is to both create and disseminate information was prevalent among university-level and county-based Extension professionals. Data show that university-level Specialists felt it necessary to create and disseminate information, even when culture and resource barriers were expressed, due to perceptions of their personal content (explicit) knowledge being more implicit in nature. County-based Extension professionals expressed dualism in both roles due to a need for content (communications materials) not available or provided for by their respective universities; thus, these materials often were crafted from information created by county-based Extension professionals. Without university-level Specialist oversight, what does this mean about information accuracy and consistency in messaging?

Resources

Organizations use knowledge management systems to organize information in databases and other infrastructures (physically and digitally) so that communications can be readily accessed. For example, this is demonstrated by North Carolina State University which has a repository of turfgrass information available online. County-based Extension professionals expressed this need for interconnectedness in the sharing of resources to connect university and community members to enhance communication.Apart from tools for knowledge storage, access, and transfer, lack of time, money, and the knowledge of using technology related to social media and multimedia for communication were revealed as being barriers to creating and disseminating turfgrass information (Worley et al., 2022, 2023).

Cultural Expectations

Extension’s culture was influential in the messages and communication channels used by both university-level and county-based Extension professionals for creating and disseminating information. Data reveal that expectations by administration for university-level and county-based Extension professionals to craft certain types of communications over others, or use certain communication channels, limited the scope for disseminating information. Incentives for using a particular channel influenced the likelihood of using that channel if a university-level Extension Specialist was being evaluated against it. Due to these formalization and interconnectedness factors, the messages and channels used to disseminate information in the turfgrass industry differed among university-level and county-based Extension professionals.

Conclusion

Innovations are created as solutions to existing and emerging challenges to bring about impactful and lasting outcomes (Militello et al., 2016). Technological innovations in agricultural sectors, coupled with ever rapid advancements in communications, have created a global transition in the way ANR information is shared and knowledge is produced (Doerfert et al., 2007). Communicating agricultural innovations have often been done through reactionary measures due to a variety of confounding factors including funding challenges. The environmental and social crises that challenge our planet justify the need for a framework that aligns with agricultural needs and provides (as opposed to responds) research-based information at such a critical global juncture. Communication of turfgrass innovations is vital, particularly as these innovations are those that are being developed to help mitigate and resolve the very issues that contributed to the crisis that has assailed our planet. Diffusing information regarding innovations, and subsequently the adoption of those innovations, can no longer be approached through traditional linear processes for facilitating change (Knickel et al., 2009). More targeted and coordinated research and communications delivery can ensure that stakeholders receive research-based information and solutions (Doerfert et al., 2007). Therefore, the relevancy that agricultural communications as a field, and the space agricultural communication plays in culture and all things ag, must be acknowledged.

It may seem that the authors are presenting a framework based on a dialectic by stating that communications should be holistic yet targeted and coordinated, but not necessarily through all aboard the social media bandwagon just because use of such channels are innovative. The basic concept of the framework is for turfgrass professionals and the entirety of the agricultural industry to consider the importance of the role of the Ag Communicator in Extension. Thus, communicating innovations efficiently and effectively to proactively address critical and emerging issues must be done so through this considered, non-hegemonic approach; one that allows for an understanding of whose role it is to create information, craft communications, and disseminate and communicate research-based information when we live in an era where most Extension professionals seem to be inundated with multiple other duties as assigned.

Implications and Future Research

While the Smith-Lever Act of 1914 established the Cooperative Extension service, more than 100 years later, the phrase best kept secret is often associated with the organization due to a public lack of awareness. A review of data by Worley et al. (2022, 2023) showed that turfgrass information needs to be effectively and efficiently communicated, but often knowing whose role it is to do so is unclear. Therefore, implications of the framework include a need for an increase in agricultural science communications professionals to work with creators and disseminators as Ag Communicators to effectively and efficiently craft and design turfgrass innovation information.

Whereas the framework and theoretical foundations provide for (yet neither suggest nor exclude) university-level and county-based Extension professionals to create, craft, and disseminate communications, future research points to determining if the responsibility for creation, crafting, and dissemination of communications materials and information transferred solely through new technologies (i.e., social media) lie within both roles. Considerations for this research could further examine the effects of organizational formalization on communications and potentially account for variables that measure the level of engagement with social media technologies, as well as demographics (i.e., age) of university-level and county-based Extension professionals.

Recommendations for future research also include a review of annual Extension performance expectations via a meta-evaluation of university-level and county-based Extension professionals (Stufflebeam, 2001). This would determine if the current evaluation models are congruent with practices and techniques relevant to agricultural communications. Promotion and tenure criteria (and Extension performance appraisal guidelines) within different academic units across universities should be reviewed to examine explicitly outlined metrics as expectations for communications creation and dissemination. Thus, analysis of how and by what metrics are those communications performance measures (if in existence and based on an individual’s role) being evaluated and rewarded is warranted.

References

Alavi, M. & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25, 107–136. http://dx.doi.org/10.2307/3250961

Bartunek, J. M., & Moch, M. K. (1987). First-order, second-order, and third-order change and organization development interventions: A cognitive approach. The Journal of Applied Behavioral Science, 23(4), 483–500. https://doi.org/10.1177/002188638702300404

Charron, C., Favier, J. & Li, C. (2006). Social computing: How networks erode institutional power, and what to do about it. Forrester Research.

Chawla, S., Roshni, A., Patel, M., Patil, S., & Shah, H. (2018). Turfgrass: A billion-dollar industry. Proceedings of the National Conference on Floriculture for Rural and Urban Prosperity in the Scenario of Climate Change, 30–35.

Dobbins, C. E., Masambuka-Kanchewa, F., & Lamm, A. J. (2021). A systematic literature review of the intersection between social media and cultural identity: Implications for agricultural and environmental communication. Journal of Applied Communications, 105(2). https://doi.org/10.4148/1051-0834.2372

Doerfert, D., Evans, J., Cartmell, D., & Irani, T. (2007). Developing an international framework and agenda for agricultural communications research. Journal of Applied Communications, 91(3), 7–2. https://doi.org/10.4148/1051-0834.1245

Evans, J. F. (2006). Roaming the changing theoretical landscape of agricultural communications. Journal of Applied Communications, 90(1), 15–32. https://doi.org/10.4148/1051-0834.1288

Ferguson, A. E. (1994). Gendered science: A critique of agricultural development. American Anthropologist, 96(3), 540–552. https://www.jstor.org/stable/682299

Knickel, K., Brunori, G., Rand, S. & Proost, J. (2009). Towards a better conceptual framework for innovation processes in agriculture and rural development: From linear models to systemic approaches. The Journal of Agricultural Education and Extension, 15(2), 131–146. https://doi.org/10.1080/13892240902909064

Kurtzo, F., Hansen, M. J., Rucker, K. J., & Edgar, L. D. (2016). Agricultural communications: Perspectives from the experts. Journal of Applied Communications, 100(1), 17–28. https://doi.org/10.4148/1051-0834.1019

Lamm, K. W., Borron, A., & Holt, J. (2019). Communication channel preferences: A descriptive audience segmentation evaluation. Journal of Applied Communications, 103(3), 1–18. https://doi.org/10.4148/1051-0834.2238

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Duncan J. Watts, D. J., & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Li, C., & Bernoff, J. (2007). Social technographics: Mapping participation in activities forms the foundation of a social strategy. A Forrester Report.

Masambuka-Kanchewa, F., Lamm, K., Lamm, A., (2020). Beyond diffusion of improved technologies to promoting innovation creation and information sharing for increased agricultural productivity: A case study of Malawi and Kenya. Journal of International Agricultural and Extension Education, 27(1), 79–92. https://doi.org.10.5191/jiaee.2020.27106

Militello, M., Janson, C., & Tonissen, D. (2016). InQuiry: A participatory approach for understanding stakeholder perceptions. The Foundation Review, 8(1), 88–107. https://doi.org/10.9707/1944-5660.1286

Million, A. J., Hudson‐Vitale, C., & Moulaison Sandy, H. (2018). Restructuring and formalizing: Scholarly communication as a sustainable growth opportunity in information agencies. Proceedings of the Association for Information Science and Technology, 55(1), 377–386. https://doi.org.10.1002/pra2.2018.14505501041

National Turfgrass Federation. (2017). The turfgrass industry – Present and future.

The National Turfgrass Research Initiative. https://www.turfresearch.org/pdf/Industry%20Turf%20Initiative.pdf

Nonaka, I., Borucki, C., Byosiere, P., & Konno, N. (1994).Organizational knowledge creation theory: A first comprehensive test. International Business Review, 3(4), 337–351. https://doi.org/10.1016/0969-5931(94)90027-2

Reuben, J. A. (1996). The making of the modern university: Intellectual transformation and the marginalization of morality. University of Chicago Press.

Rivera, W.M., Qamar, M.K. & Mwandemere, H.K., 2005. Enhancing coordination among AKIS/RD actors: An analytical and comparative review of country studies on agricultural knowledge and information systems for rural development (AKIS/RD). FAO. http://www.fao.org/sd/dim_kn3/kn3_050901_en.htm

Santos, P. L. F. D., & Castilho, R. M. M. D. (2018). Substrates in the development of a sports turfgrass “Tifton 419.” Ornamental Horticulture, 24, 138–144.

Stufflebeam, D. L. (2001). The meta-evaluation imperative. American Journal of Evaluation, 22(2), 183–209. https://doi.org/10.1177/109821400102200204

Tietze, S. & Dick, P. (2013). The victorious English language: Hegemonic practice in the management academy. Journal of Management Inquiry, 22(1), 122–134. https://doi.org/10.1177/105649261244316

Wright, B. D. (2012). Grand missions of agricultural innovation. Research Policy, 41(10), 1716-1728. https://doi.org/10.1016/j.respol.2012.04.021

Worley, B., Peake, J., & Fuhrman, N. (2021). A quantitative approach to identifying turfgrass key players. Advancements in Agricultural Development, 2(1), 83–95. https://doi.org/10.37433/aad.v2i1.85

Worley, B., Peake, J., & Fuhrman, N. (2022). Perceptions of agricultural extension and communication professionals regarding current, preferred, and emerging communication channels: A qualitative study. Journal of Agricultural Education, 63(4), 22–38. https://doi.org/10.5032/jae.2022.04022

Worley, B., Peake, J. & Fuhrman, N. (2023). Identifying the behavioral intent to use social media through the application of UTAUT in ANR and turfgrass Extension. Journal of Applied Communications, 107(3), 1–16. https://doi.org/10.4148/1051-0834.2469

Jacob Englin In Teacher Education & School-based Ag. Education, Vol. 75

Early-Career Georgia Agriculture Teachers’ Agricultural Mechanics Professional Development Needs

Authors

Christopher C. Crump, Banks County High School, christopher.crump@banks.k12.ga.us

Trent Wells, Murray State University, kwells23@murraystate.edu

PDF Available

Abstract

Agricultural mechanics is a prominent agricultural subject matter area in many agricultural education programs throughout Georgia. Hainline and Wells (2024) indicated that early-career agriculture teachers often have different agricultural mechanics professional development (PD) needs than their more-experienced colleagues. Hence, our study focused on early-career agriculture teachers. We used human capital theory (HCT) to theoretically underpin our study. To conduct our study, we used a valid and reliable research instrument that contained eight demographics items and 65 agricultural mechanics items. Wells and Hainline (2021) previously used this instrument to conduct their national-level study of agriculture teachers’ agricultural mechanics PD needs. We distributed this instrument via e-mail to 253 early-career agriculture teachers throughout Georgia; however, only 243 emails delivered successfully. Seventy-six teachers provided usable data, yielding a 31.3% response rate. Using mean weighted discrepancy scores (MWDS), we found that the greatest PD needs among early-career Georgia agriculture teachers were: (1) American Welding Society (AWS) standards for welding procedures, (2) Procedures for structural welding, and (3) Principles of metallurgy (ex. identifying metals, proper use of metals, etc.). We recommend that Georgia agricultural education stakeholders use our findings to structure PD sessions that address early-career Georgia agriculture teachers’ greatest agricultural mechanics PD needs. We advise that scholars should engage with mid- and late-career Georgia agriculture teachers to examine their agricultural mechanics PD needs as well.

Introduction and Theoretical Framework

Undeniably, effective teachers are vital components of the agricultural education programs found within public schools across the United States. Considering the concept of effectiveness as professional educators, agriculture teachers must be knowledgeable and skilled in a wide range of agricultural subject matter to appropriately serve their students and their local communities (Eck et al., 2019). This is certainly applicable to agricultural mechanics as well (Granberry et al., 2023). Agricultural mechanics is broad in its nature and scope (Wells et al., 2021) and is popular with many students (Valdez & Johnson, 2020), including students in Georgia public schools (Georgia Agricultural Education, 2023). The teaching of agricultural mechanics subject matter in agricultural education programs presents a combination of learning opportunities for students, such as applying engineering concepts throughout a trailer fabrication project, and liability concerns for agriculture teachers, such as adequately supervising students during project activities (Wells & Hainline, 2021). Consequently, it is imperative that agriculture teachers be well-prepared to appropriately and professionally tackle the opportunities and challenges associated with this technical agriculture subject matter area (Granberry et al., 2023).

To help overcome potential deficits in agriculture teachers’ current knowledge and skills, offering teacher-oriented learning opportunities via professional development (PD) sessions or workshops is a frequent approach. Conceptually, PD can be structured to fit a range of time frames and can be leveraged to meet a variety of targeted needs, such as improvements in agriculture teachers’ pedagogical skills, strengthening their technical agriculture subject matter knowledge, and so forth. Ultimately, PD should be operationalized to help better prepare agriculture teachers to serve their students over both the short- and long-term (Grieman, 2010). Not surprisingly, agriculture teachers frequently need PD in agricultural mechanics subject matter to help them improve their capacities to serve students (Granberry et al., 2023; Wells & Hainline, 2021). However, as indicated by Hainline and Wells (2024) in their national-level study, agriculture teachers’ agricultural mechanics PD needs vary based on their career phases. Specifically, early-career agriculture teachers tend to have greater agricultural mechanics PD needs in comparison to their mid- and late-career colleagues (Hainline & Wells, 2024). Further, Solomonson et al. (2021) noted that taking steps to improve agriculture teachers’ (especially early-career teachers’) confidence to teach technical agriculture curricula can help increase their likelihood to remain in the profession. Considering the abovementioned factors, we found it prudent to examine early-career Georgia agriculture teachers’ agricultural mechanics PD needs.

We employed human capital theory (HCT) to undergird our study. Regarding HCT, Becker (1993) noted that investing in individuals’ knowledge and skills yields improvements in their abilities to provide adequate returns. In the context of our study, we operationalized agricultural mechanics PD for early-career agriculture teachers as an investment. We characterized returns as improvements in early-career agriculture teachers’ capacity to teach technical agriculture subject matter to their students to help better prepare them for opportunities in the agricultural industry. By working with students, agriculture teachers directly facilitate the development of human capital for the agricultural industry (Stripling & Ricketts, 2016). As such, we anticipate that defining early-career Georgia agriculture teachers’ agricultural mechanics PD needs will be helpful for the state’s agricultural industry stakeholders.

Purpose of the Study

The purpose of our study was to determine the agricultural mechanics PD needs of early-career Georgia agriculture teachers. Our approach helps facilitate the strategic development of PD sessions that directly address early-career Georgia agriculture teachers’ actual needs.

Methods

Our study, which we framed via Borich’s (1980) needs assessment model, used a census design and was a direct replication of Wells and Hainline’s (2021) study, Examining Teachers’ Agricultural Mechanics Professional Development Needs: A National Study. We used their valid and reliable instrument to collect our data. Their instrument contained several teacher demographics items and 65 agricultural mechanics items. The 65 agricultural mechanics items addressed diverse topics related to woodworking and structures construction, welding and metal fabrication, electricity, land surveying, plumbing, safety, project planning, construction, and tool and equipment usage. Their instrument included two five-point, Likert-type scales to collect data regarding the 65 agricultural mechanics items. One scale addressed agriculture teachers’ perceived importance for each item to be taught in agricultural education programs (i.e., the Importance scale). The other scale addressed agriculture teachers’ perceived competence to teach each item (i.e., the Competence scale). The Importance scale used the following anchors: (1) Not important (NI), (2) Of little importance (LI), (3) Somewhat important (SI), (4) Important (I), and (5) Very important (VI). The Competence scale used the following anchors: (1) Not competent (NC), (2) Little competence (LC), (3) Somewhat competent (SC), (4) Competent (C), and (5) Very competent (VC). In contrast to Wells and Hainline (2021), our study’s focus was solely on early-career agriculture teachers in Georgia during the 2023-2024 academic year (i.e., in years one through five as described by Solomonson and Retallick [2018]).

Upon Murray State University (MSU) Institutional Review Board (IRB) approval, we partnered with Georgia agricultural education state staff to obtain the school e-mail addresses for all 253 early-career agriculture teachers in Georgia. Once we obtained all 253 agriculture teachers’ school e-mail addresses, we followed Dillman et al.’s (2014) advice and used five points of contact (i.e., e-mails) to conduct the data collection process via Qualtrics. These five e-mails included: (1) the initial e-mail that detailed the purpose of the study and contained an electronic link to the research instrument sent on Tuesday, October 3, 2023, (2) the first reminder e-mail sent on Tuesday, October 10, 2023, (3) the second reminder e-mail sent on Tuesday, October 17, 2023, (4) the third reminder e-mail sent on Tuesday, October 24, 2023, and (5), the fourth and final reminder e-mail sent on Tuesday, October 31, 2023. Of the five different contact e-mails sent, e-mails to 10 agriculture teachers bounced, yielding a failure rate of approximately 3.9%, thus reducing our potential respondents to 243. To help foster responses, we offered respondents a chance to win one of five $20.00 gift cards that we randomly drew after we concluded the data collection process. Because the data collection overlapped with the 2023 Georgia National Fair and the 2023 National FFA Convention, we elected to conclude our data collection on Friday, November 17, 2023 to help maximize responses.

Ninety-four respondents participated in our study. We elected, however, to analyze and report only the data from those 76 respondents who completed at least 75% of the research instrument, yielding a usable response rate of 31.3%. Both Sherman and Sorensen (2020) and Wells and Hainline (2021) reported similar response rates (26.8% and 27.5%, respectively). To identify the presence of non-response error, we compared early respondents (n = 29) to late respondents (n = 47) as recommended by Linder et al. (2001). We defined early respondents as those who responded prior to the first reminder e-mail that we distributed on Tuesday, October 10, 2023. We defined late respondents as those who responded on or after Tuesday, October 10, 2023. We used an independent samples t-test to compare the means of the two groups on the Competence scale of the research instrument. We determined that there were no statistically significant differences between early respondents and late respondents (t(74) = .24, p =.81).

We used Microsoft Excel to analyze our data. We employed a variety of descriptive statistics to analyze our respondents’ demographics data and their responses on the Importance and Competence scales. To identify and rank our respondents’ agricultural mechanics PD needs, we used McKim and Saucier’s (2011) Excel-Based MWDS [mean weighted discrepancy score] Calculator. We acknowledge that because we employed a census design within our study, we cannot generalize our results beyond the 76 early-career Georgia agriculture teachers who participated in our study.

Results

Teacher Demographics

We reported the agriculture teacher demographics data in Table 1 (below). Fifty-four respondents (71.1%) identified as female while 22 respondents (28.9%) identified as male. Also, 55% of respondents (f = 42) stated that they had taught agricultural mechanics courses in the past three years and 47% (f = 36) had worked in the agricultural industry prior to their current teaching position. Respondents had been teaching agriculture for an average of 2.87 years (SD = 1.70). Also, the majority of respondents (f = 48; 63.2%) reported that they had initially gained their teacher certification via an undergraduate-level teacher preparation program (see Table 1).

Table 1

Agriculture Teacher Demographics

Item	f	%
What is your gender? (n = 76)
Male	22	28.9
Female	54	71.1
Including this academic year, have you taught agricultural mechanics coursework in an agricultural education program during any of the past three academic years? (n = 76)
Yes	42	55.3
No	34	44.7
Prior to your current agricultural education teaching position, did you previously work in the agricultural industry? (n = 76)
Yes	36	47.4
No	40	52.6
Including this academic year, how many years have you have been teaching agricultural education? (n = 76)
0	11	14.5
1	3	3.9
2	20	26.3
3	12	15.8
4	11	14.4
5	19	25
Which of the following best describes how you obtained your agricultural education teacher certification? (n = 76)
Undergraduate-level teacher preparation program	48	63.2
Began teaching agricultural education after working in industry	17	22.4
Graduate-level teacher preparation program	7	9.2
Other (Alternative Certification, Provisional Certificate)	4	5.3
Note. Some percentages may not add to 100% due to rounding.

Perceived Importance to Teach

We reported the responses from the 65 agricultural mechanics items within the Importance scale in Table 2 (below). We bolded the highest mode for each item across the five categories (i.e., Not important, Of little importance, Somewhat important, Important, and Very important). Twenty items had a mode of five (Very important) and 44 items had a mode of four (Important). The item with the highest percentage of Very important rankings was Safety procedures for agricultural mechanics activities (VI: 90.5%, f = 74, Md = 5, Mdn = 5). While all 65 items had a mode of four (I) or five (VI), Procedures for using legal land descriptions (VI: 15.5%, f = 64, Md = 4, Mdn = 4) had the lowest percentage of Very important responses (see Table 2).

Table 2

Early-career Georgia Agriculture Teachers’ Perceived Importance to Teach Agricultural Mechanics

		%
Item	n	NI	LI	SI	I	VI	Mdn	Md
Safety procedures for agricultural mechanics activities	74	0.0	0.0	0.0	9.5	90.5	5	5
Use of personal protective equipment (PPE)	74	0.0	0.0	0.0	12.2	87.8	5	5
Use of measuring tools (ex. tape measure, framing square, etc.)	72	0.0	0.0	0.0	12.5	87.5	5	5
Use of laboratory safety equipment (ex. fire extinguishers, eye wash stations, etc.)	74	0.0	0.0	1.4	10.8	87.8	5	5
Use of hand tools (ex. screwdriver, hammer, etc.)	69	0.0	0.0	1.5	20.3	78.3	5	5
Use of handheld power tools (ex. cordless drill, jig saw, etc.)	70	0.0	0.0	1.4	21.4	77.1	5	5
Use of fasteners (ex. screws, nails, glue, etc.)	74	0.0	0.0	2.7	41.9	55.4	5	5
Estimating materials for projects	74	0.0	0.0	2.7	50.0	47.3	4	4
Use of stationary power equipment (ex. band saw, table saw, etc.)	71	0.0	0.0	2.8	40.9	56.3	5	5
Principles of electrical theory (ex. conductors, insulators, alternating current [AC], direct current [DC], etc.)	65	0.0	1.5	1.5	46.2	50.8	5	5
Use of electrical measurement units (ex. amperes, volts, Ohms, etc.)	65	0.0	1.5	1.5	40.0	56.9	5	5
Procedures for wiring outlets	65	0.0	1.5	3.1	38.5	56.9	5	5
Use of electrical systems tools (ex. digital multi-meter, wire strippers, etc.)	65	0.0	1.5	3.1	38.5	56.9	5	5
Procedures for laying out projects	74	0.0	0.0	5.4	51.4	43.2	4	4
Procedures for building wood projects	72	0.0	0.0	5.6	54.2	40.3	4	4
Creating a bill of materials for projects	74	0.0	1.4	5.4	39.2	54.0	5	5
Use of marking tools (ex. chalk line, paint marker, etc.)	70	0.0	1.4	5.7	42.9	50.0	4.5	5
Procedures for wiring single-pole switch circuits	65	0.0	1.5	6.2	36.9	55.4	5	5
Procedures for wiring double-pole switch circuits	65	0.0	1.5	7.7	46.2	44.6	4	4
Procedures for reassembling small engines	64	0.0	4.7	4.7	48.4	42.2	4	4
Procedures for troubleshooting small engines	63	0.0	3.2	6.4	46.0	44.4	4	4
Principles of welding theory (ex. joint types, positions, etc.)	70	1.4	2.9	5.7	48.6	41.4	4	4
Procedures for disassembling small engines	65	0.0	4.6	6.2	49.2	40.0	4	4
Interpreting project blueprints	74	0.0	0.0	10.8	51.4	37.8	4	4
Procedures for wiring three-way switch circuits	65	0.0	1.5	10.8	41.5	46.2	4	5
Procedures for SMAW (Arc welding)	70	1.4	2.9	8.6	45.7	41.4	4	4
Procedures for GMAW (MIG welding)	70	1.4	2.9	8.6	47.1	40.0	4	4
Use of precision tools (ex. micrometer, dial caliper, etc.)	70	1.4	1.4	10	47.1	40.0	4	4
Procedures for agricultural equipment operation	65	1.5	1.5	10.8	40.0	46.2	4	5
Procedures for using PVC pipe	72	0.0	1.4	12.5	51.4	34.7	4	4
Principles of four-stroke engine operational theory	64	1.6	3.1	9.4	46.9	39.1	4	4
Drawing project plans to scale	73	0.0	1.4	13.7	54.8	30.1	4	4
Procedures for wiring trailer electrical systems	65	0.0	3.1	12.3	41.5	43.1	4	5
Principles of two-stroke engine operational theory	65	1.5	3.1	10.8	47.7	36.9	4	4
Procedures for structural welding	70	1.4	2.9	11.4	47.1	37.1	4	4
Principles of metallurgy (ex. identifying metals, proper use of metals, etc.)	69	1.5	1.5	13.0	46.4	37.7	4	4
American Welding Society (AWS) standards for welding procedures	69	1.5	1.5	13	39.1	44.9	4	5
Procedures for wiring four-way switch circuits	65	0.0	3.1	13.9	43.1	40.0	4	4
Principles of diesel engine operational theory	65	1.5	4.6	10.8	47.7	35.4	4	4
Procedures for building metal projects (ex. trailers, barbecue pits, etc.)	70	1.4	1.4	14.3	50.0	32.9	4	4
Procedures for oxy-fuel cutting	70	1.4	2.9	14.3	42.9	38.6	4	4
Use of handheld pneumatic (air) tools (ex. impact wrench, paint spray gun, etc.)	71	0.0	4.2	16.9	43.7	35.2	4	4
Procedures for plasma arc cutting	70	1.4	10.0	11.4	41.4	35.7	4	4
Principles of vehicle powertrain operational theory	64	1.2	4.7	17.2	42.2	34.4	4	4
Procedures for GTAW (TIG welding)	71	1.4	4.2	18.3	43.7	32.4	4	4
Procedures for building masonry projects	72	1.4	5.6	18.0	48.6	26.4	4	4
Use of hydraulic equipment (ex. shears, iron worker, etc.)	70	0.0	2.9	22.9	35.7	38.6	4	5
Procedures for cold metalworking bending	70	1.4	2.9	21.4	48.6	25.7	4	4
Procedures for oxy-fuel welding	70	2.9	5.7	17.1	41.1	32.9	4	4
Procedures for FCAW (Flux-core arc welding)	71	2.8	5.6	18.3	45.0	28.1	4	4
Procedures for painting projects	74	0.0	2.7	24.3	47.3	25.7	4	4
Procedures for hot metalworking cutting	70	1.4	5.7	20.0	45.7	27.1	4	4
Procedures for cold metalworking cutting	70	1.4	4.3	21.4	47.1	25.7	4	4
Procedures for building fence projects	72	0.0	4.1	23.6	41.7	30.6	4	4
Procedures for using PEX pipe	72	0.0	6.9	20.8	44.4	27.8	4	4
Procedures for hot metalworking bending	70	2.9	2.9	22.9	41.4	30.0	4	4
Procedures for using copper pipe	72	1.4	9.7	20.8	43.0	25.0	4	4
Procedures for hot metalworking shaping	70	1.4	7.1	24.3	40.0	27.1	4	4
Procedures for cold metalworking shaping	70	1.4	4.3	27.1	41.4	25.7	4	4
Procedures for oxy-fuel brazing	69	2.9	7.3	23.2	43.5	23.2	4	4
Use of computer numerical control (CNC) systems	71	0.0	15.5	23.9	32.4	28.2	4	4
Procedures for using legal land descriptions	64	3.1	18.8	18.8	43.8	15.6	4	4
Procedures for using land surveying equipment	64	1.6	14.1	25.0	35.9	23.4	4	4
Procedures for conducting land surveys	64	3.1	12.5	26.6	35.9	21.9	4	4
Note. Importance scale: 1 = Not important (NI), 2 = Of little importance (LI), 3 = Somewhat important (SI), 4 = Important (I), 5 = Very important (VI); Mdn = Median; Md = Mode.

Perceived Competence to Teach

We reported the responses from the 65 agricultural mechanics items within the Competence scale in Table 3 (below). We bolded the highest mode for each item across the five categories (i.e., Not competent, Little competence, Somewhat competent, Competent, and Very competent). Two items had a mode of five, 24 items had a mode of four, four items had a mode of three, two items had a mode of two, and 24 items had a mode of one. Nine items had two modes. The item that had the highest reported combined Very competent and Competent ratings was Use of laboratory safety equipment (ex. fire extinguishers, eye wash stations, etc.) (VC: 39.2%, C: 56.8%, f = 74, Md = 4, Mdn = 4). The area that had the highest percentage of Not competent ratings (Md = 1) was Procedures for using unmanned aerial vehicles in land surveying (NC: 43.8%, f = 64) (see Table 3).

Table 3

Early-career Georgia Agriculture Teachers’ Perceived Competence to Teach Agricultural Mechanics

		%
Item	n	NC	LC	SC	C	VC	Mdn	Md
Use of laboratory safety equipment (ex. fire extinguishers, eye wash stations, etc.)	74	0.0	0.0	4.1	56.8	39.2	4	4
Use of personal protective equipment (PPE)	74	0.0	1.4	5.4	35.1	58.1	5	5
Use of hand tools (ex. screwdriver, hammer, etc.)	69	0.0	0.0	13.0	34.8	52.2	5	5
Use of measuring tools (ex. tape measure, framing square, etc.)	72	1.4	0.0	19.4	41.7	37.5	4	4
Use of fasteners (ex. screws, nails, glue, etc.)	74	1.4	6.8	16.2	41.9	33.8	4	4
Use of handheld power tools (ex. cordless drill, jig saw, etc.)	70	5.7	2.9	17.1	40	34.3	4	4
Procedures for SMAW (Arc welding)	70	1.4	4.3	21.4	47.1	25.7	4	4
Safety procedures for agricultural mechanics activities	74	1.4	5.4	21.6	36.5	35.1	4	4
Use of marking tools (ex. chalk line, paint marker, etc.)	70	1.4	2.9	25.7	41.4	28.6	4	4
Procedures for painting projects	74	2.7	6.8	25.7	44.6	20.3	4	4
Procedures for wiring single-pole switch circuits	65	12.3	9.2	15.4	40.0	23.1	4	4
Procedures for wiring outlets	65	9.2	10.8	18.5	40.0	21.4	4	4
Procedures for building wood projects	72	4.2	12.5	23.6	44.4	15.3	4	4
Use of electrical systems tools (ex. digital multi-meter, wire strippers, etc.)	65	10.8	6.2	26.2	38.5	18.5	4	4
Creating a bill of materials for projects	74	5.4	6.8	32.4	24.3	31.1	4	3
Use of stationary power equipment (ex. band saw, table saw, etc.)	71	5.6	14.1	25.4	28.2	26.8	4	4
Estimating materials for projects	74	5.4	13.5	27.0	36.5	17.6	4	4
Use of electrical measurement units (ex. amperes, volts, Ohms, etc.)	65	9.2	7.7	29.2	38.5	15.4	4	4
Principles of electrical theory (ex. conductors, insulators, alternating current [AC], direct current [DC], etc.)	65	9.2	10.8	26.2	36.9	16.9	4	4
Use of handheld pneumatic (air) tools (ex. impact wrench, paint spray gun, etc.)	71	4.2	19.7	22.5	39.4	14.1	4	4
Procedures for wiring double-pole switch circuits	65	13.9	10.8	23.1	35.4	16.9	4	4
Procedures for wiring three-way switch circuits	65	15.4	13.9	21.5	30.8	18.5	3	4
Procedures for laying out projects	74	9.5	10.8	31.1	35.1	13.5	3	4
Procedures for using PVC pipe	72	8.3	13.9	31.9	31.9	13.9	3	3 / 4
Procedures for building fence projects	72	12.5	15.3	29.2	30.6	12.5	3	4
Drawing project plans to scale	73	9.6	20.6	27.4	31.5	11.0	3	4
Procedures for GMAW (MIG welding)	70	22.9	18.6	17.1	28.6	12.9	3	4
Use of precision tools (ex. micrometer, dial caliper, etc.)	70	8.6	22.9	28.6	24.3	15.7	3	3
Procedures for agricultural equipment operation	65	26.2	13.9	21.5	24.6	13.9	3	1
Procedures for wiring four-way switch circuits	65	20.0	20.0	21.5	24.6	13.9	3	4
Procedures for disassembling small engines	65	26.2	12.3	26.2	23.1	12.3	3	1 / 3
Procedures for wiring trailer electrical systems	65	18.5	26.2	20.0	26.2	9.2	3	2 / 4
Procedures for reassembling small engines	64	25.0	17.2	23.4	21.9	12.5	3	1
Procedures for oxy-fuel cutting	70	27.1	18.6	21.4	17.1	15.7	3	1
Use of hydraulic equipment (ex. shears, iron worker, etc.)	70	21.4	18.6	27.1	17.1	15.7	3	3
Principles of four-stroke engine operational theory	64	25.0	17.2	25.0	21.9	10.9	3	1 / 3
Principles of two-stroke engine operational theory	65	24.6	18.5	24.6	21.5	10.8	3	1 / 3
Principles of welding theory (ex. joint types, positions, etc.)	70	25.7	25.7	17.1	18.6	12.9	2	1 / 2
Procedures for plasma arc cutting	70	28.6	25.7	14.3	21.4	10.0	2	1
Interpreting project blueprints	74	10.8	14.9	46.0	18.9	9.5	3	3
Procedures for troubleshooting small engines	63	27.0	19.1	27.0	14.3	12.7	3	1 / 3
Procedures for using PEX pipe	72	29.2	25.0	19.4	15.3	11.1	2	1
Procedures for structural welding	70	35.7	24.3	15.7	15.7	8.6	2	1
Principles of metallurgy (ex. identifying metals, proper use of metals, etc.)	69	31.9	24.6	20.3	15.9	7.3	2	1
Procedures for building metal projects (ex. trailers, barbecue pits, etc.)	70	37.1	22.9	17.1	14.3	8.6	2	1
American Welding Society (AWS) standards for welding procedures	69	37.7	20.3	20.3	13.0	8.7	2	1
Procedures for FCAW (Flux-core arc welding)	71	42.3	21.1	15.5	14.1	7.0	2	1
Procedures for GTAW (TIG welding)	71	39.4	22.5	16.9	15.5	5.6	2	1
Procedures for building masonry projects	72	26.4	33.3	19.4	15.3	5.6	2	2
Procedures for oxy-fuel welding	70	40.0	17.1	22.9	14.3	5.7	2	1
Procedures for conducting land surveys	64	32.8	28.1	20.3	14.1	4.7	2	1
Procedures for cold metalworking cutting	70	38.6	31.4	11.4	12.9	5.7	2	1
Principles of diesel engine operational theory	65	27.7	29.2	24.6	13.9	4.6	2	2
Use of computer numerical control (CNC) systems	71	36.6	25.4	19.7	11.3	7.0	2	1
Use of computer numerical control (CNC) systems		36.6	25.4	19.7	11.3	7.0	2	1
Procedures for oxy-fuel brazing	69	43.5	24.6	14.5	13.0	4.4	2	1
Procedures for hot metalworking bending	70	38.6	28.6	15.7	10.0	7.0	2	1
Procedures for using copper pipe	72	30.6	30.6	22.2	8.3	8.3	2	1 / 2
Procedures for hot metalworking cutting	70	38.6	28.6	17.1	11.4	4.3	2	1
Principles of vehicle powertrain operational theory	64	34.4	25.0	25.0	10.9	4.7	2	1
Procedures for using land surveying equipment	64	31.3	21.9	31.3	12.5	3.1	2	1 / 3
Procedures for cold metalworking bending	70	40.0	28.6	17.1	10.0	4.3	2	1
Procedures for cold metalworking shaping	70	40.0	30.0	17.1	8.6	4.3	2	1
Procedures for hot metalworking shaping	70	38.6	30.0	20.0	7.1	4.3	2	1
Procedures for using legal land descriptions	64	35.9	32.8	21.9	6.3	3.1	2	1
Procedures for using unmanned aerial vehicles in land surveying	64	43.8	34.4	12.5	7.8	1.6	2	1
Note. Competence scale: 1 = Not competent (NC), 2 = Little competence (LC), 3 = Somewhat competent (SC), 4 = Competent (C), 5 = Very competent (VC); Mdn = Median; Md = Mode.

Agricultural Mechanics PD Needs Ranked by MWDS

We reported early-career Georgia agriculture teachers’ agricultural mechanics PD needs within each of the 65 different agricultural mechanics items in Table 4 (below). As indicated by their positive MWDS, the five greatest agricultural mechanics PD needs for early-career Georgia agriculture teachers were: (1) American Welding Society (AWS) standards for welding procedures (MWDS = 8.06), (2) Procedures for structural welding (MWDS = 7.42), (3) Principles of metallurgy (ex. identifying metals, proper use of metals, etc.) (MWDS = 7.32, (4) Procedures for building metal projects (ex. trailers, barbeque pits, etc.) (MWDS = 7.29), and (5) Procedures for cold metalworking bending (MWDS = 7.27). Conversely, the five lowest agricultural mechanics PD needs for early-career Georgia agriculture teachers were: (1) Use of marking tools (ex. chalk line, paint marker, etc.) (MWDS = 2.14), (2) Use of personal protective equipment (PPE) (MWDS = 1.85), (3) Use of hand tools (ex. screwdriver, hammer, etc.) (MWDS= 1.80), (4) Procedures for SMAW (arc welding) (MWDS = 1.33), and (5) Procedures for painting projects (MWDS = 0.91) (see Table 4).

Table 4

Early-career Georgia Agriculture Teachers’ Agricultural Mechanics Professional Development Needs by MWDS

				Importance		Competence
Item	n	Rank	MWDS	M	SD	M	SD
American Welding Society (AWS) standards for welding procedures	69	1	8.06	4.25	0.85	2.35	1.34
Procedures for structural welding	70	2	7.42	4.16	0.85	2.37	1.34
Principles of metallurgy (ex. identifying metals, proper use of metals, etc.)	69	3	7.32	4.17	0.82	2.42	1.29
Procedures for building metal projects (ex. trailers, barbecue pits, etc.)	70	4	7.29	4.11	0.81	2.34	1.34
Procedures for cold metalworking bending	70	5	7.27	3.94	0.85	2.10	1.17
Procedures for troubleshooting small engines	63	6	7.13	4.32	0.74	2.67	1.36
Principles of vehicle powertrain operational theory	64	7	7.12	4.03	0.93	2.27	1.19
Principles of diesel engine operational theory	65	8	7.08	4.11	0.89	2.38	1.17
Procedures for GTAW (TIG welding)	71	9	7.07	4.01	0.90	2.25	1.28
Procedures for hot metalworking cutting	70	10	6.93	3.91	0.91	2.12	1.18
Procedures for cold metalworking shaping	70	11	6.89	3.86	0.91	2.07	1.15
Procedures for cold metalworking cutting	70	12	6.88	3.91	0.88	2.16	1.24
Procedures for hot metalworking bending	70	13	6.85	3.93	0.95	2.19	1.25
Principles of welding theory (ex. joint types, positions, etc.)	70	14	6.75	4.26	0.81	2.67	1.38
Procedures for hot metalworking shaping	70	15	6.75	3.84	0.96	2.09	1.13
Procedures for oxy-fuel welding	70	16	6.61	3.96	1.00	2.29	1.29
Procedures for FCAW (Flux-core arc welding)	71	17	6.54	3.90	0.97	2.23	1.32
Procedures for reassembling small engines	64	18	6.35	4.28	0.77	2.80	1.37
Procedures for oxy-fuel brazing	69	19	6.28	3.77	0.99	2.10	1.23
Procedures for wiring trailer electrical systems	65	20	6.08	4.25	0.79	2.82	1.27
Procedures for agricultural equipment operation	65	21	6.05	4.28	0.84	2.86	2.00
Procedures for disassembling small engines	65	22	6.01	4.25	0.77	2.83	1.38
Procedures for building masonry projects	72	23	6.01	3.93	0.89	2.40	1.19
Principles of four-stroke engine operational theory	64	24	5.95	4.19	0.85	2.77	1.34
Principles of two-stroke engine operational theory	65	25	5.82	4.15	0.85	2.75	1.33
Procedures for oxy-fuel cutting	70	26	5.74	4.14	0.87	2.76	1.43
Procedures for plasma arc cutting	70	27	5.66	4.00	1.00	2.59	1.37
Procedures for using copper pipe	72	28	5.60	3.81	0.97	2.33	1.23
Procedures for GMAW (MIG welding)	70	29	5.54	4.21	0.83	2.90	1.38
Use of computer numerical control (CNC) systems	71	30	5.47	3.73	1.04	2.27	1.26
Procedures for using PEX pipe	72	31	5.46	3.93	0.88	2.54	1.35
Interpreting project blueprints	74	32	5.37	4.27	0.65	3.01	1.08
Procedures for wiring four-way switch circuits	65	33	5.36	4.20	0.79	3.92	1.35
Procedures for using unmanned aerial vehicles in land surveying	64	34	5.09	3.39	1.11	1.89	1.01
Use of hydraulic equipment (ex. shears, iron worker, etc.)	70	35	5.04	4.10	0.85	2.87	1.36
Procedures for using legal land descriptions	64	36	4.98	3.50	1.07	2.08	1.06
Use of electrical measurement units (ex. amperes, volts, Ohms, etc.)	65	37	4.94	4.52	0.62	3.43	1.13
Procedures for using land surveying equipment	64	38	4.80	3.66	1.04	2.34	1.14
Procedures for conducting land surveys	64	39	4.74	3.61	1.10	2.30	1.20
Procedures for wiring three-way switch circuits	65	40	4.72	4.32	0.73	3.23	1.33
Principles of electrical theory (ex. conductors, insulators, alternating current [AC], direct current [DC], etc.)	65	41	4.67	4.46	0.61	3.42	1.17
Use of electrical systems tools (ex. digital multi-meter, wire strippers, etc.)	65	42	4.65	4.51	0.64	3.48	1.19
Procedures for laying out projects	74	43	4.62	4.38	0.59	3.32	1.14
Use of precision tools (ex. micrometer, dial caliper, etc.)	70	44	4.53	4.23	0.80	3.16	1.20
Safety procedures for agricultural mechanics activities	74	45	4.51	4.91	0.29	3.99	0.96
Procedures for wiring double-pole switch circuits	65	46	4.47	4.34	0.69	3.31	1.27
Use of stationary power equipment (ex. band saw, table saw, etc.)	71	47	4.41	4.54	0.56	3.56	1.19
Procedures for wiring outlets	65	48	4.37	4.51	0.64	3.54	1.21
Estimating materials for projects	74	49	4.33	4.45	0.55	3.47	1.10
Procedures for wiring single-pole switch circuits	65	50	4.19	4.46	0.69	3.52	1.29
Drawing project plans to scale	73	51	4.14	4.14	0.69	3.14	1.16
Use of handheld power tools (ex. cordless drill, jig saw, etc.)	70	52	3.87	4.76	0.46	3.94	1.08
Procedures for using PVC pipe	72	53	3.79	4.20	0.70	3.29	1.13
Use of measuring tools (ex. tape measure, framing square, etc.)	72	54	3.59	4.88	0.33	3.14	0.83
Procedures for building wood projects	72	55	3.50	4.35	0.59	3.54	1.03
Creating a bill of materials for projects	74	56	3.43	4.46	0.67	3.69	1.15
Procedures for building fence projects	72	57	3.32	4.00	0.85	3.15	1.21
Use of handheld pneumatic (air) tools (ex. impact wrench, paint spray gun, etc.)	71	58	2.89	4.10	0.83	3.39	1.09
Use of laboratory safety equipment (ex. fire extinguishers, eye wash stations, etc.)	74	59	2.50	4.86	0.38	4.35	0.56
Use of fasteners (ex. screws, nails, glue, etc.)	74	60	2.39	4.53	0.55	4.00	0.95
Use of marking tools (ex. chalk line, paint marker, etc.)	70	61	2.14	4.40	0.67	3.93	0.89
Use of personal protective equipment (PPE)	74	62	1.85	4.88	0.33	4.50	0.67
Use of hand tools (ex. screwdriver, hammer, etc.)	69	63	1.80	4.77	0.46	4.39	0.71
Procedures for SMAW (Arc welding)	70	64	1.33	4.23	0.84	3.91	0.88
Procedures for painting projects	74	65	0.91	3.96	0.78	3.73	0.96
Note. Importance Scale: 1 = Not important (NI), 2 = Of little importance (LI), 3 = Somewhat important (SI), 4 = Important (I), 5 = Very important (VI); Competence Scale: 1 = Not competent (NC), 2 = Little competence (LC), 3 = Somewhat competent (SC), 4 = Competent (C), 5 = Very competent (VC); MWDS = Mean weighted discrepancy score; M = Mean; SD = Standard deviation.

Conclusions, Discussion, Recommendations, and Limitations

The purpose of our study was to determine the agricultural mechanics PD needs of early-career Georgia agriculture teachers. Based upon our findings, we concluded that early-career Georgia agriculture teachers have PD needs in all 65 agricultural mechanics items included in our instrument. Consequently, therein lie opportunities for Georgia agricultural education stakeholders to strategically plan and implement a wide range of agricultural mechanics PD sessions that would potentially benefit early-career agriculture teachers throughout the state. More specifically, we further concluded that early-career Georgia agriculture teachers’ agricultural mechanics PD needs relate primarily to welding and metal fabrication. When examining Table 4, numerous items within welding and metal fabrication had high MWDS. Consequently, special consideration must be given to welding and metal fabrication-related items when developing PD sessions for early-career agriculture teachers in Georgia. To maximize the potential to address multiple items within this broad area, we recommend that Georgia agricultural education stakeholders consider facilitating long-duration (i.e., one day-long or longer) PD sessions. Examples of potential PD sessions that could likely address multiple items underneath the welding and metal fabrication umbrella may include beginner-level, skill development-oriented workshops along with more advanced, project-focused workshops.

We did note that the majority of our respondents did perceive all 65 agricultural mechanics items to be important to teach within agricultural education programs. Because agricultural mechanics instruction is popular with students in the state (Georgia Agricultural Education, 2023), we found it reassuring that early-career Georgia agriculture teachers indicated that this subject matter area is important to teach. In contrast, however, many respondents did not identify themselves as either Competent or Very competent on a wide range of the 65 agricultural mechanics items on our research instrument. We found this to be particularly evident with the highly-technical agricultural mechanics items (e.g., Use of computer numerical control [CNC] systems and Procedures for GTAW [TIG welding]) in comparison to the fundamental, introductory-level agricultural mechanics items (e.g., Use of hand tools [ex. screwdriver, hammer, etc.] and Procedures for painting projects). This gap was likely produced via a combination of both limited exposure to agricultural mechanics before entering a university (as was often found by Whitehair et al. [2020]) and limited undergraduate-level agricultural teacher education programming addressing agricultural mechanics.

As Granberry et al. (2023) noted, the credit hours within agricultural teacher education programs intended to prepare pre-service teachers to teach agricultural mechanics remain limited. As such, early-career Georgia agriculture teachers may not be prepared to adequately address their students’ learning needs, which may disrupt to the development of human capital for the state’s agricultural industry. Per HCT (Becker, 1993), investing in professionals’ knowledge and skills (i.e., developing and offering agricultural mechanics PD for early-career agriculture teachers) can produce quantifiable, measurable returns on investment. As agriculture teachers are vital developers of human capital for the agricultural industry (Stripling & Ricketts, 2016), they must be prepared to adequately deliver instruction in agricultural mechanics (Wells & Hainline, 2021).

Regarding further research to support agricultural education programming in Georgia, we recommend that scholars replicate our study with mid- and late-career Georgia agriculture teachers. On a national level, Hainline and Wells (2024) found that agriculture teachers have different agricultural mechanics PD needs based on career phase. Consequently, we believe that Georgia agriculture teachers may likewise have differing agricultural mechanics PD needs based on their respective career phases. Such data would be useful for Georgia agricultural education stakeholders as they work to strategically address agriculture teachers’ technical agriculture knowledge and skill shortcomings.

Regarding limitations of our study, we identified that our response rate (31.3%) was a primary limitation. Ideally, we intended to collect data from all 253 early-career Georgia agriculture teachers. While a response rate of 31.3% is usable, it is certainly not the ideal. However, our response rate was similar to recent national-level studies (Sherman & Sorensen, 2020; Wells & Hainline, 2021), indicating that other scholars are encountering challenges with response rates. A higher response rate in our study would have provided a more complete picture of the agricultural mechanics PD needs that early-career Georgia agriculture teachers have. We further acknowledge that because our study used a census design and we obtained a fairly-low response rate, we cannot generalize our results to all early-career Georgia agriculture teachers.

Both the timing of our data collection and the Qualtrics platform itself may have also served as limitations to potential respondents. Due to the 2023 Georgia National Fair and the 2023 National FFA Convention, many early-career Georgia agriculture teachers were likely traveling with (and supervising) their students while we were collecting data. Consequently, some potential respondents may have attempted to complete our research instrument on their mobile devices (i.e., smartphones). Our research instrument used a two-part, Likert-type scale that required scrolling back and forth as well as up and down. This may have led to some respondents either exiting our research instrument prior to answering all the items or electing to not respond at all. We recommend that scholars who elect to replicate our study carefully consider both data collection timing and the formatting of the research instrument when using Qualtrics.

References

Becker, G. S. (1993). Human capital: A theoretical and empirical analysis with special reference to education (3rd ed.). The University of Chicago Press.

Borich, G. (1980). A needs assessment model for conducting follow-up studies. Journal of Teacher Education, 31(3), 39-42. https://doi.org/10.1177/002248718003100310

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., Ramsey, 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. https://doi.org/10.5032/jae.2019.04001

Georgia Agricultural Education. (2023, July). Georgia agricultural education 2022-2023 annual report. https://www.georgiaffa.org/docs/48404_2022-2023%20Georgia%20Ag%20Ed%20Annual%20Report.pdf

Granberry, T., Blackburn, J. J., & Roberts, R. (2023). The state of agricultural mechanics in the preparation of school-based agricultural education teachers. Journal of Agricultural Education, 64(4), 144-158. https://doi.org/10.5032/jae.v64i4.160

Grieman, B. C. (2010). Continuing professional development. In R. M. Torres, T. Kitchel, A. L. Ball (Eds.) Preparing and advancing teachers in agricultural education (pp. 180-201). The Ohio State University.

Hainline, M. S., & Wells, T.(2024). Examining differences in teachers’ agricultural mechanics professional development needs: A national study. Journal of Agricultural Education, 65(1), 245-264. https://doi.org/10.5032/jae.v65i1.2473

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42(4), 43-53. https://doi.org/10.5032/jae.2001.04043

McKim, B. R., & Saucier, P. R. (2011). An Excel-based mean weighted discrepancy score calculator. The Journal of Extension, 49(2). https://www.joe.org/joe/2011april/pdf/JOE_v49_2tt8.pdf

Sherman, A., & Sorensen, T. J. (2020). A national examination of the predictors of volunteer utilization in school-based agricultural education. Journal of Agricultural Education, 61(2), 339-357. https://doi.org/10.5032/jae.2020.02339

Solomonson, J. K., & Retallick, M. S. (2018). Over the edge: Factors nudging mid-career, school-based agriculture teachers out of the profession. Journal of Agricultural Education, 59(4), 1-19. https://doi.org/10.5032/jae.2018.04001

Solomonson, J. K., Still, S. M., Maxwell, L. D. (2021). Factors influencing the decision of Illinois school-based agricultural education teachers to remain in the profession. Journal of Agricultural Education, 62(3), 121-137. https://doi.org/10.5032/jae.2021.03121

Stripling, C. T., & Ricketts, J. C. (2016). Research priority 3: Sufficient scientific and professional workforce that addresses the challenges of the 21^st century. In T. G. Roberts, A. Harder, & M. T. Brashears. (Eds.), American Association for Agricultural Education national research agenda: 2016-2020. University of Florida Department of Agricultural Education and Communication.

Valdez, E. D., & Johnson, S. (2020). Mismatch? Aligning secondary career and technical education with regional workforce demand. Texas Public Policy Foundation. https://files.texaspolicy.com/uploads/2020/05/05112536/Valdez-Johnson-Workforce-Demand.pdf

Wells, T., & Hainline, M. S. (2021). Examining teachers’ agricultural mechanics professional

development needs: A national study. Journal of Agricultural Education, 62(2), 217–238. https://doi.org/10.5032/jae.2021.02217

Wells, T., Hainline, M. S., Rank, B. D., Sanders, K. W., & Chumbley, S. B. (2021). A regional study of the agricultural mechanics knowledge and skills needed by school-based agricultural education teachers. Journal of Agricultural Education, 62(2), 148-166. https://doi.org/10.5032/jae.2021.02148

Whitehair, R. L., Schramm, K. R. S., Wells, T., & Hainline, M. S. (2020). Preservice teachers’ conceptualizations of agricultural mechanics. Journal of Agricultural Education, 61(3), 60-74. https://doi.org/10.5032/jae.2020.03060

Determining Instructional Design Effects on Self-Efficacy, Interest, and Knowledge in a Small Engines Course

Challenging the Linear Approach: Effective and Efficient Communication of Agricultural Innovations

Early-Career Georgia Agriculture Teachers’ Agricultural Mechanics Professional Development Needs

Sort Articles

Helpful Downloads

Pages

Recent Posts

Recent Articles

JSAER Editor

Search

Have an issue?