Semester of Graduation

Spring 2019

Degree

Master of Science (MS)

Department

Agricultural and Extension Education and Evaluation

Document Type

Thesis

Abstract

Perhaps the most challenging factor in teaching is being able to foster a learning environment that meets the needs of all learners, which is often achieved by utilizing a plethora of instructional methods that develop critical thinking and problem solving skills. These 21st century skills have been recurrently identified as a critical component of today’s workplace and employers are hiring individuals who can solve complex problems, especially within agriculture. However, students in todays educational classrooms are often not receiving the hands-on instruction that is needed in order to foster the development of critical thinking and problem solving skills. Further, problem solving skills have been identified has one of the most crucial cognitive activities that we encounter every day in our personal and professional lives. In order to combat this problem, educators have moved to more active learning environments to help develop critical thinking and problem solving skills that are needed for employment in the workforce. Previous research supports the idea that active learning classrooms provide students with the necessary hands-on activities that develops their critical thinking and problem solving skills. Previous research also has been conducted to understand how cognitive style, learning style, and critical thinking style affect an individual’s problem solving ability. However, little research has been conducted to understand how cognitive diversity amongst a group affects the problem solving process. Therefore, this study sought to understand how cognitive diversity affects the problem solving ability of students in an agricultural mechanics class. This study compared students’ problem solving ability by measuring time to solution and hypothesis generation ability when troubleshooting a small gasoline engine. A one-group pretest-posttest design was utilized for this study. In all, 31 participants elected to participate in this study and completed a criterion-referenced test, course motivation survey instrument, and a troubleshooting exercise. Data were analyzed using nonparametric statistics, specifically, Mann-Whitney U tests, Kruskal-Wallis test, and the Pearson’s Chi-Square test. The analysis rendered no statistically significant differences between cognitive style and content or course motivation. However, further analysis revealed a statistically significant difference was found between cognitive diversity groups and time to solution and hypothesis generation.

Committee Chair

Joey Blackburn

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