Teaching & Learning in Undergraduate Academic Programs

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:

1. Compare pre and post knowledge levels, perceived self-efficacy, and subject matter interest of participants on the topic of precision measurement.
2. 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-efficacya	.89	.94		.91	.97
Interesta	.92	.96		.90	.94
Knowledgeb	.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.

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Whitney L. Figland, Louisiana State University, wfigla2@lsu.edu

J. Joey Blackburn, St. Charles Community College, jblackburn@stchas.edu

Kristin S. Stair, Louisiana State University, kstair@lsu.edu

Michael F. Burnett, Louisiana State University, vocbur@lsu.edu

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Abstract

One of the greatest challenges that classroom teachers face has been fostering a learning environment that caters to the needs of diverse learners. Teachers have various teaching methodologies at their disposal, ranging from passive, teacher-centered to active, student-centered strategies. The flipped classroom approach allows for teachers to become the facilitator of learning activities and students to become actively engaged in the learning experience. This transition allows for more student-centered activities to occur in class that enhance students’ critical thinking and problem-solving skills. Team-based learning (TBL) is a modified version of flipped classroom that allows students to work collaboratively to solve complex problems. Content knowledge has long been considered an important prerequisite of higher cognitive functions such as critical thinking, problem solving, and reflective thinking. The purpose of this exploratory study was to explain the effect of cognitive style on the small gasoline engines content knowledge of undergraduate students enrolled in a flipped introductory agricultural mechanics course at Louisiana State University. To test the hypotheses, this study utilized descriptive statistics, including the mean and standard deviation, and independent t-tests. A Mann-Whitney U test was employed to determine the influence of cognitive style on content knowledge. Overall, no differences in content knowledge were found. It is recommended to replicate this study longitudinally to increase statistical power. For practice, educators should employ learning strategies that meet the needs of students with diverse cognitive styles.

Introduction and Literature Review

One of the greatest challenges classroom teachers face has been fostering a learning environment that caters to the needs of diverse learners. To achieve this, teachers have a variety of teaching methodologies at their disposal, ranging from passive, teacher-centered methods to active, student-centered strategies (Schunk, 2012). One relatively new means of active engagement has been through the utilization of flipped classrooms. Some of the first flipped classroom models can be seen emerging into secondary and post=secondary education in the late 1990s and early 2000s after the inception of No Child Left Behind (NCLB) (Frederickson et al., 2005; Strayer, 2007; U.S. Department of Education, 2001). Baker (2000) presented his early version of the “classroom flip” as a new method of teaching that was made possible by an increase in the need for new educational methodologies that better engage learners and the increase in instructional technology availability (p. 4). Similarly, Lage et al. (2000) developed the “inverted classroom” model to invert the classroom structure and better engage students during class (p. 32). In both models, it was suggested to move instructional lecture material out of the classroom and make it available online, thus using class time for the professor to serve as a guide to assist students while providing increased time for application and practice (Baker, 2000; Lage et al., 2000). Over the past two decades, the flipped classroom approach has gained increased attention in secondary and post-secondary education for its student-centered approach and increased emphasis on engagement (Barkley, 2015; McCubbins et al., 2018).

The flipped classroom model allows teachers to become the facilitator of learning activities and the students to become actively engaged in the learning process while still focusing on delivering course content (Connor et al., 2014). This transition can allow for more student-centered activities during class to enhance students’ critical thinking and problem-solving skills (Allen et al., 2011; Hanson, 2006). Additionally, active learning strategies promote a student-centered learning environment by creating opportunities for students to solve problems in a real-world context (Michealsen & Sweet, 2008; Sibley & Ostafichuk, 2015).

In recent years, a new type of flipped classroom has emerged as a version of a traditionally flipped classroom; team-based learning (TBL). TBL has emerged as a flipped classroom technique that allows students to work collaboratively to solve complex problems during class time (Michealsen & Sweet, 2008; Wallace et al., 2014). Similar to traditional flipped classroom models, TBL is a student-centered approach that shifts instruction away from a traditional lecture format to create a student-centered learning environment (Artz et al., 2016; Nieder et al., 2005). In a TBL-formatted course, students take on the responsibility of learning conceptual knowledge outside of class and spend more time applying that knowledge in class as a part of a team (Michaelsen et al., 2004). Essentially, TBL is formatted to provide students with opportunities to learn declarative and procedural knowledge to enhance critical thinking and problem-solving skills (Michaelsen & Sweet, 2008). One aspect of TBL that sets it apart from the traditional flipped classroom is its increased emphasis on accountability (Michaelson et al., 2004). An essential element of TBL is the administration of Individual Readiness Assurance Tests (IRATS) and Team Readiness Assurance Tests (TRATS) that serve as formative assessments after each module to ensure students have engaged with the material.

Despite the many possible applications of TBL to agricultural education, research supporting its use in agricultural education has been limited. McCubbins et al. (2016) conducted a study to examine student perceptions of TBL in an agricultural education capstone course. The findings suggested that students had a positive view of TBL and were highly satisfied with the student-centered learning environment (McCubbins et al., 2016). This study also indicated that working in teams positively impacted student motivation to learn in a collaborative setting (McCubbins et al., 2016). A similar study conducted by McCubbins et al. (2018) found that TBL in agricultural education courses supported the development of critical thinking, motivation to learn, and ability to effectively apply course concepts by undergraduate students. Focusing specifically on agricultural mechanics, a course typically heavily focused on problem solving, Figland et al. (2020a) reported that undergraduate students perceived that TBL supported the development of problem-solving skills and promoted positive collaboration between group members while increasing student self-efficacy in the content area.

The ability to increase critical thinking and problem-solving skills cannot be developed exclusively by integrating specific teaching methods. Instead, the education literature has supported the notion that the cognitive styles of students in classes and educational teams can influence the ability of students to problem solve effectively (Myers & Dyer, 2006; Parr & Edwards, 2004; Thomas, 1992; Torres & Cano, 1994; Torres & Cano, 1995; Witkin et al.,1977). Cognitive styles have typically been defined as an individual’s preferred way of organizing and retaining information to solve problems (Keefe, 1979; Kirton, 2003). The awareness of a student’s cognitive style can be an important factor in the success of their ability to solve problems (Jonassen, 2000; Witkin et al., 1977). In agricultural education, Blackburn et al. (2014) and Lamm et al. (2011) concluded that before educators can understand how to tailor lessons to teach critical thinking and problem-solving skills effectively, they must be aware of varying cognitive styles and understand how to relate those cognitive styles to successful problem solving and critical thinking development. To better understand how problem solving can be developed within agricultural education coursework, cognitive style, and innovative teaching methods can be utilized to develop students’ critical thinking ability (Figland et al., 2020b).

Theoretical Framework

Kirton’s (2003) adaptation-innovation theory (A-I theory) served as the theoretical foundation of this study to aid in furthering the understanding of how critical thinking ability can be tied to TBL teaching methodologies. A-I theory is grounded on the premise that all people are creative and can solve problems, regardless of their preferred cognitive style (Kirton, 2003). Per the theory, cognitive style is a person’s preferred way to think, learn, and solve problems (Kirton, 2003). An individual’s cognitive style is measured through Kirton’s adaption-innovation inventory (KAI). KAI scores that fall below the mean are considered more adaptive, while scores above the mean are more innovative. However, it is important to note that the scale is a continuum, and individuals are never purely adaptive or purely innovative (Kirton, 2003). In other words, two people can have scores below the mean, indicating they are more adaptive compared to the normal distribution of scores, but the individual with the higher score is considered more innovative than the other.

When comparing the more adaptive and innovative, several key distinctions exist in how these individuals prefer to learn and solve problems. More adaptive individuals prefer well-established problems and favor working within the current problem structure (Kirton et al., 1991). These individuals collaborate well with group members and generate ideas that favor consensus (Kirton, 2003). On the contrary, the more innovative prefer less structure to solve the problem and often challenge boundaries (Kirton, 2003; Lamm et al., 2012). More innovative individuals tend to stretch the boundaries of problems and generate ideas outside the current group structure (Kirton, 2003). Often, individuals falling more on the innovative side of the continuum tend to be novel and find different ways to solve problems. Whereas the more adaptive ones tend to be safer, more predictable, conforming, and less ambiguous when solving problems (Kirton, 1999, 2003).

Cognitive style is one’s preferred way of learning and engaging in problem solving tasks (Kirton, 2003). However, learners are often presented with situations in which they must learn or perform outside their preferred style. In these instances, individuals utilize coping behaviors to navigate the environment (Kirton, 2003). Often, this occurs in a setting where the person must work with individuals of diverse cognitive styles. Kirton (2003) described this as the Problem A and Problem B situations. For example, consider students assembled into a team to complete a group project. Problem A is the group assignment, while Problem B is how well the group can navigate their diverse cognitive styles to perform the task.

Little research has existed in agricultural education that investigates the effects of cognitive style on student learning outcomes in a flipped learning environment. A-I theory postulates that cognitive style is unrelated to cognitive capacity; however, little literature has been advanced in agricultural education examining this notion. Further, no literature was found that tested this hypothesis in a flipped classroom setting. As a result, the principal question that arose after reviewing the literature was: How does cognitive style effect the small gasoline engine content knowledge of undergraduate students enrolled in a flipped introductory agricultural mechanics course at Louisiana State University?

Purpose of the Study

The purpose of this exploratory study was to explain the effect of cognitive style on small gasoline engine content knowledge of undergraduate students enrolled in a flipped introductory agricultural mechanics course at Louisiana State University.

The following null hypotheses guided this study:

H₀1: There were no statistically significant differences in small gasoline engine content knowledge of undergraduate students in an introductory agricultural mechanics course based on cognitive style.

Methodology

Data associated with this study were collected as a part of a larger research project that investigated students’ abilities to solve small gasoline engine-related problems. Specifically, a one-group pretest-posttest pre-experimental design was employed to collect data for this research (Campbell & Stanley, 1963; Salkind, 2010). This design is used widely in educational research when all individuals are assigned to the experimental group and observed at two points (Campbell & Stanley, 1963; Salkind, 2010). The changes from the pre-test to the post-test determine the results from the intervention; however, in this design, there is no comparison group, making it almost impossible to determine if the change would have occurred only from the intervention and not from extraneous variables (Salkind, 2010). Extraneous variables must be considered and dismissed to make any generalizations between the interventions and change (Salkind, 2010).

Population/Sample

The population of this study was all students who enrolled in an introductory agricultural mechanics course at Louisiana State University during the spring semester of 2018 (n = 17) and spring semester of 2019 (n = 15). Overall, one student in the spring semester of 2018 did not complete enough course material to be included in the study; therefore, the participating sample totaled n = 31. Institutional Review Board (IRB) approval was sought and granted. Per IRB, students were notified of this research on the first day of class and were given the opportunity to opt out without penalty. All students were over 18 and elected to provide signed consent to participate in this research.

To test for homogeneity between semesters, independent sample t-tests were conducted on individual cognitive score, age, and students’ pre-course interest survey to determine if the groups were homologous. The t-test analysis found that there were not statistically significant differences between the 2018 and 2019 semesters and cognitive style (p = .109), age (p = .596), and pre-CIS (p = .062), respectively. To test for homogeneity, Levene’s test for equality of error variances was calculated and was not statistically significant; therefore, it was assumed that the variances were almost equal and the groups were similar.

Further, a Chi-Square test was employed to determine if differences existed between the two semesters based on gender (X² = .313, df = 1, p = .576). Therefore, from the analysis, it is concluded that our population from both semesters was homologous, and subsequently, the data were merged for further data analysis.

While the course was offered through the Department of Agricultural and Extension Education and Evaluation at Louisiana State University, it was advertised throughout the college and university. Table one provides the personal and educational characteristics of students (n = 31) who enrolled in this course during the spring of 2018 or 2019. Overall, these students’ ages ranged from 18 to 24, with 19 (29.0%) and 21(29.0%) being the most reported ages. The majority (n = 17; 54.8%) of students were female, and sophomore (41.9%) was the most frequently reported academic classification.  In all, nine majors were represented in this course, with Agricultural and Extension Education being the most common (41.9%).

Instrumentation

Kirton’s adaptation-innovation inventory (KAI) was used to determine students’ cognitive styles (Kirton, 2003). This instrument consisted of 32 items that asked questions about the individuals’ preferred way to learn. The KAI scores range from 32 to 160 on a continuum from more adaptive to more innovative, with a theoretical mean of 96 (Kirton, 2003). However, the practical mean of the KAI is 95 (Kirton, 2003). Therefore, individuals who score 95 or below are considered more adaptive, while those who score 96 or above are considered more innovative. The instrument has been successfully utilized to determine the cognitive style of a wide variety of individuals from varying backgrounds (Kirton, 2003). Internal reliability of this instrument has been measured through multiple studies. Kirton (2003) reported that after analyzing data from six different population samples with over 2,500 respondents that internal reliability coefficients ranged from .84 − .89. Also, 25 other studies that utilized the KAI showed reliabilities between .83 and .91 (Kirton, 2003).

Due to the nature of this pre-experimental study, it was important to determine the students’ knowledge in small gasoline engine content before and after the intervention. The researcher developed a 30-item criterion-referenced test to test the individual’s knowledge. It should be noted that half of the questions on this test were developed by Blackburn (2013) and further modified to meet the needs of this study. The other 15 questions were developed by the researcher based on the Small Engine Care & Repair textbook written by London (2003), a Small Engines Equipment and Maintenance textbook written by Radcliff (2016), and the Briggs and Stratton PowerPortal website. The criterion-referenced test was formatted using a four-option multiple-choice template, including one correct answer and three distractors. Guidelines offered by Wiersma and Jurs (1990) were followed to ensure the reliability of the criterion-referenced test. Table two provides the factors considered as well as how each was addressed.

Course Structure and Procedures

On the first day of the small gasoline engines unit, the KAI and the 30-item pretest were administered to the students. Due to using TBL as the primary teaching strategy, the students were grouped purposively by cognitive style into teams in which they would remain for the duration of the unit. Teams were developed as heterogeneous, homogeneous adaptive, or homogenous innovative. The course layout was formatted based on Michealsen and Sweet’s (2008) recommendations.

In the small gasoline foci, five individual modules were constructed, including (a) small engine tool and part ID, (b) 4-cycle theory and fuel, (c) ignition and governor systems, (d) cooling/lubrication system, and (f) troubleshooting. After each module, students completed an IRAT to determine their content knowledge retained. After completing the IRAT, the students would join their assigned team and complete the TRAT. During the TRATs, students were allowed to collaborate with other members to come to an agreement on items they may have gotten incorrect. The goal of completing the IRAT before the TRAT was to ensure that all group members of the team contributed equally. At the end of the small gasoline engine unit, the 30-item criterion-referenced test was administered.

Data Analysis

Descriptive statistics were utilized to test this study’s hypotheses, including means and standard deviations and independent sample t-tests. Independent sample t-tests are utilized to compare the means of two independent groups and determine if they are statistically significant. In this study, the t-tests were utilized to determine if the groups from the 2018 and 2019 semesters were homologous and could be merged for further data analysis. Further, Mann-Whitney U tests were employed to determine if there was a statistically significant difference between content knowledge and cognitive style.

Findings

The overall mean of the pretest was 15.58 (51.9%).  The mean of the more adaptive students pretest was 15.48 (51.6%), while the more innovative averaged 15.88 (52.9%). Regarding the post-test, the overall mean was 23.39 (77.9%). The more adaptive students’ average score was 22.96 (76.5%), and the mean post-test score of the more innovative students was 24.63 (82.1%), as presented in Table 5.

A Mann-Whitney U test was employed to determine if a statistically significant difference in content knowledge existed based on cognitive style. This test (see Table 6)determined no statistically significant differences in content knowledge by cognitive style (p = .292) at the .05 level.

Conclusion and Limitations

Overall, the statistical analysis revealed that cognitive style did not affect the small gasoline engine content knowledge of students enrolled in an introductory agricultural mechanics course at Louisiana State University. Therefore, the researchers failed to reject the null hypothesis. This conclusion aligns with the A-I theory in that cognitive style does not relate to cognitive capacity. In other words, one’s preferred style or manner of learning and problem solving does not influence the ability to learn or performance. Similarly, this research aligns with the findings of prior research that investigated factors influencing content knowledge achievement (Blackburn, 2013, 2014; Pate et al., 2004). However, these prior studies did not include a pretest measure of small gasoline engine content knowledge; therefore, they failed to account for pretreatment differences in content knowledge. Further, research should be conducted to compare the TBL method of teaching small gasoline engine content with direct instruction. Due to the lack of a comparison group, it is not known whether students in these semesters would have performed better or worse than similar students taught in a more traditional format. This type of research could allow practitioners greater confidence that, at a minimum, they are not impeding students learning by employing TBL in their classrooms.

This study was conducted during two spring semesters to increase the sample size to enhance statistical power. However, due to enrollment sizes and data attrition, the overall sample was only 31 students. Small sample sizes are a detriment to most parametric statistical tools; however, these data were tested for normality in SPSS. However, due to the low sample size, the statistical power of this research was inherently low, which increased the chance of committing Type-II errors.

An additional limitation of this study was the lack of random selection of participants. Due to the nature of using student enrollment in a particular class, caution must be given when interpreting the findings, and it cannot be generalized past the sample reported in this research. The introductory agricultural mechanics course was required for students majoring in agricultural and extension education and has become an increasingly popular elective for other majors across the university. Students not required to complete this course may have a higher mechanical aptitude or prior knowledge and/or experiences in the content areas, which may influence their performance in the course.

Recommendations

To increase statistical power, it is recommended that this research be extended for a minimum of three more semesters. Depending on enrollments, this would increase the sample size to more than 75 students. A sample size of 75 to 100 would sufficiently increase power. Further, additional variables such as mechanical aptitude should be assessed to determine the impact on content knowledge. Additionally, content knowledge should be utilized as an independent variable to determine its role in students’ problem-solving ability in authentic learning environments. Additional research should determine the effect of these diverse cognitive teams on the ability to generate hypotheses and solve authentic problems. Content knowledge could also be employed in a multiple regression model to determine its impact when hypothesizing and solving contextual problems.

Practitioners should be informed that cognitive styles influence how students prefer to learn and solve problems (Kirton, 2003) but are not related to how well a student learns. Teachers should strive to create learning environments conducive to diverse learners to ensure all students have an opportunity to learn (Roberts et al., 2020). As teachers provide opportunities for diverse learning styles – auditory, kinesthetic, and visual – they should provide opportunities geared toward the more adaptive and innovative problem-solving styles. This would ensure one style preference is not constantly required to employ coping behaviors to succeed. Post-secondary educators should consider TBL if they are interested in flipping an agricultural mechanics course. Results from this study indicated that, based on cognitive style, all students can learn successfully. Further, the use of frequent IRATs and TRATs ensures a level of accountability not normally found in traditional flipped classes.

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