Date of Award

5-2018

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Engineering Education

Committee Chair

Edward Berger

Committee Member 1

Allison Godwin

Committee Member 2

Michael Loui

Committee Member 3

Matthew Ohland

Abstract

To maintain America’s status as a global technological leader, there has been a longstanding effort to increase the quality and quantity of engineers in the workforce. Previous research and government reports have called on the education system at all levels to increase enrollment and persistence in college engineering programs. Additionally, engineering employers continue to be dissatisfied with the skills obtained by those students who do persist to graduation, in part because those students that may become the best engineers are leaving engineering. In order to effectively recruit and retain the best engineering students, the factors that affect engineering student success need to be better understood. In addition, we need to understand how to improve these factors in students to guide them to becoming successful in engineering However, in engineering undergraduate programs, where many students are matriculating with exceptional academic credentials, "cognitive" factors such as high school GPA and standardized test scores are poor predictors of student success. Therefore, in order to gain a better understanding of the drivers of academic success for first-year engineering students, additional non-cognitive and demographic factors must be considered. The initial goal of this research was to determine the cognitive, non-cognitive, and demographic factors that affect the academic performance of first-year engineering undergraduates. To accomplish this, a 41-item non-cognitive and demographic survey was administered to 375 first-year engineering students to measure a collection of non-cognitive, and demographic factors. An exploratory factor analysis was performed on the non-cognitive survey items to determine the underlying factors present in the data, and these factors were included alongside traditional cognitive and demographic factors in a step-wise linear regression model. Finally, a structural equation model was created to better understand the direct and indirect effects of the cognitive, non-cognitive, and demographic variables on first year engineers’ academic performance. The subsequent goal was to recruit students for an intervention intended to improve a subset of those non-cognitive factors. Initially, students were recruited for an academic coaching intervention with the intention of improving their study habits, time management, and test anxiety. An additional set of students was then recruited for a second round of interventions, where academic coaches were given the quantitative results so they could better prepare for and target their sessions to individual students. The results of this research show that the inclusion of non-cognitive and demographic factors creates a much better model for predicting engineering students’ first year performance. For instance, with this sample of students, test anxiety had a significant negative relationship with cumulative first year GPA, while high school GPA was a non-significant predictor. In addition, academic coaching interventions were found, both quantitatively and qualitative, to improve students’ study skills, time management, and test anxiety. All students mentioned that they thought academic coaching would improve their academic performance, and on average students’ test anxiety and study skill survey results improved. This research shows how engineering students’ academic success can be better modeled with a more holistic collection of factors, and that a subset of these factors can be improved with the goal of improving academic performance. These results can be used by faculty and academic advisors to better understand why students may be struggling and can be used to more effectively recruit students for interventions. The academic coaching system can also use these results to create more effective and personalized interventions. Ultimately, this research can be used in numerous ways to better understand students and guide them towards academic success.

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