ASEE Zone 2 Conference 2017

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Using Predictive Variable Analysis to Investigate Non-Academic Factors that Contribute to the Persistence of Undergraduate Engineering Students

Final Paper
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This work in progress describes the study of how dispositional variables such as gender and ethnicity affect the persistence of undergraduate Electrical and Computer Engineering (ECE) students at two different institutions. ECE students from an Historically Black College or University (HBCU), along with ECE students from a Predominately White Institution (PWI) who matriculate at the same engineering school and take the same engineering classes, are included as the targeted populations for this study. Previous research on the non-academic factors that contribute to the persistence of undergraduate electrical and computer engineering (ECE) students suggested that, of the ten variables studied, academic integration and institutional commitment were the two primary factors that moderately correlate with persistence in the ECE major. Bivariate correlations of predictor variables indicated that for academic integration (AI), financial stress and institutional commitment were the strongest indicators of AI. For institutional commitment (IC), financial strain had the strongest impact. The other predictive variables in this study included Degree Commitment, Social Integration, Scholastic Conscientiousness, Academic Efficacy, Academic Motivation, Collegiate Stress, and Academic Advising. Though correlation analyses revealed strong correlations among predictive variables, there may also be strong correlations among dispositional factors, such as gender and ethnicity, which were not investigated in the sample population in previous research. Also, incorporating cross-validation methods of reliability will provide deeper insight to the non-cognitive persistence factors impacting engineering student success. In this paper, the researchers describe an investigation study on dispositional variables that may influence the persistence of ECE students in similar academic environments. In addition, cross-validation studies will be presented which may provide meaningful reliability measures. The results of this study will contribute to the advanced knowledge of the impact of psychosocial factors on engineering student success. Results may also contribute to the design of effective prediction models for persistence of undergraduate engineering students.


Shonda Bernadin    
Electrical and Computer Engineering
Florida A&M University-Florida State University College of Engineering
United States

Wachelle McKendrick    
Department of Social Work
Florida A&M University
United States

Charmane Caldwell    
Dean's Office
Florida A&M University-Florida State University College of Engineering
United States


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