An Examination of Cognitive and Non-Cognitive Factors and Academic Success in the Pre-Engineering Curriculum at a Four-Year Southeastern University
Type of DegreeDissertation
Leadership and Technology
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A large amount of empirical research has been conducted on academic achievement with college students. The empirical studies have revealed the significance of high school preparation, more specifically mathematical preparation, for academic success in post-secondary institutions; however, limited research exists for predicting academic success using cognitive and non-cognitive factors (i.e., self-concept, study habits, and inquisitiveness). The nature of engineering college courses tends to be quantitatively oriented, and calculus tends to serve as the gateway course for academic success within these majors. Conversely, non-cognitive factors significantly contribute to college mathematics achievement beyond standardized test scores or high school ranks. The purpose of this study was to determine if cognitive factors mediate the effect of non-cognitive factors on quantitative grade point average and to determine if these cognitive and non-cognitive factors can predict admission status in engineering education. With College Freshman Survey results from a sample of 2,276 college freshman students who intended to major in engineering, the following statistical analyses were used: exploratory factor analysis, confirmatory factor analysis, structural equation modeling, and discriminant function analysis. The structural model analysis revealed that cognitive factors (ACT math scores, high school math grades, and high school ranks) mediated the effects of non-cognitive factors (lack of confidence in academic ability, mathematical ability, difficulty with problem solving, and self-appraised abilities) on the quantitative GPA for the pre-engineering curriculum. The results of the discriminant function analysis suggested that participants who were admitted to engineering and those participants who left unsuccessful were classified correctly based on the cognitive and non-cognitive factors. The overall percentage of correctly classified cases was 51.6% with this analysis. Moreover, the model accounted for 29% of the variance in the quantitative GPA. As a concluding part of this study, a secondary mathematics curriculum was developed to improve mathematical skills and problem-solving abilities. Within the Mathematics Curriculum for Advanced Mathematical Proficiency, the mathematical concepts are taught within real-world contexts. Each unit has an engineering connection to familiarize the students with the various fields of engineering.