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Investigating Factors that Predict Academic Success in Engineering and Computer Science

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Academic Success and Retention

Tagged Division

Educational Research and Methods

Tagged Topic

Diversity

Page Count

10

Permanent URL

https://peer.asee.org/37393

Download Count

18

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Paper Authors

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Olusola Adesope Washington State University

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Dr. Olusola O. Adesope is a Professor of Educational Psychology and a Boeing Distinguished Professor of STEM Education at Washington State University, Pullman. His research is at the intersection of educational psychology, learning sciences, and instructional design and technology. His recent research focuses on the cognitive and pedagogical underpinnings of learning with computer-based multimedia resources; knowledge representation through interactive concept maps; meta-analysis of empirical research, and investigation of instructional principles and assessments in STEM. He is currently a Senior Associate Editor of the Journal of Engineering Education.

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Oluwafemi J. Sunday Washington State University

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OLUWAFEMI J. SUNDAY is currently a doctoral student studying Educational Psychology in the College of Education. His research focuses on multimedia instructional design, learning strategies, and STEM education. His interests cut across the use of multimedia in advancing early childhood, different instructional strategies, and how technology impacts mental health. Femi leverages quantitative techniques such as psychometrics, CFA, measurement invariance, SEM, and Monte Carlo approaches in his studies. He loves to provide an inclusive learning environment that promotes cognitive learning and students’ well-being.

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Ebenezer Rotimi Ewumi Washington State University

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Ebenezer Ewumi is a Computer Engineering master student at Washington State University. His research is in engineering education and software engineering techniques. His recent research focuses on the effect of high impact practices on engineering and computer science undergraduate student outcomes around academic success and persistence.

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Angela Minichiello P.E. Utah State University Orcid 16x16 orcid.org/0000-0002-4545-9355

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Angela Minichiello is an assistant professor in the Department of Engineering Education at Utah State University (USU) and a registered professional mechanical engineer. Her research examines issues of access, diversity, and inclusivity in engineering education. In particular, she is interested in engineering professional formation, problem-solving, and the intersections of online learning and alternative pathways for adult, nontraditional, and veteran undergraduates in engineering.

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Muhammad Asghar P.E. Utah State University

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Muhammad Asghar is a graduate research assistant and a PHD student at Engineering Education Department, Utah State University. He has a master's degree in educational psychology and a bachelor's in computer information systems engineering. His research interests consist of using different technical and non-technical methods to enhance learning processes of engineering students.

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Candis S. Claiborn Washington State University

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Dr. Candis Claiborn has been at Washington State University since 1991. In 2016, she returned to faculty after serving for 10 years as Dean of the Voiland College of Engineering and Architecture at WSU. Prior to that, she served as interim dean and as associate dean for research and graduate programs. Dr. Claiborn received her PhD in chemical engineering from North Carolina State University in 1991. Her research interests are in atmospheric aerosols, air pollution, and atmosphere-biosphere interactions.

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Abstract

Over the years, researchers have found that student engagement facilitates desired academic success outcomes for college undergraduate students. Much research on student engagement has focused on academic tasks and classroom context. High impact engagement practices (HIEP) have been shown to be effective for undergraduate student academic success. However, less is known about the effects of HIEP specifically on engineering and computer science (E/CS) student outcomes. Given the high attrition rates for E/CS students, student involvement in HIEP could be effective in improving student outcomes for E/CS students, including those from various underrepresented groups.

More generally, student participation in specific HIEP activities has been shown to shape their everyday experiences in school, both academically and socially. Hence, the primary goal of this study is to examine the factors that predict academic success in E/CS using multiple regression analysis. Specifically, this study seeks to understand the effects of high impact engagement practices (HIEP), coursework enjoyability, confidence at completing a degree on academic success of the underrepresented and nontraditional E/CS students. We used exploratory factor analyses to derive “academic success” variable from five items that sought to measure how students persevere to attain academic goals.

A secondary goal of the present study is to address the gap in research literature concerning how participation in HIEP affects student persistence and success in E/CS degree programs. Our research team developed and administered an online survey to investigate and identify factors that affect participation in HIEP among underrepresented and nontraditional E/CS students. Respondents (N = 531) were students enrolled in two land grant universities in the Western U.S. Multiple regression analyses were conducted to examine the proportion of the variation in the dependent variable (academic success) explained by the independent variables (i.e., high impact engagement practice (HIEP), coursework enjoyability, and confidence at completing a degree). We hypothesized that (1) high impact engagement practices will predict academic success; (2) coursework enjoyability will predict academic success; and (3) confidence at completing a degree will predict academic success. Results showed that the multiple regression model statistically predicted academic success , F(3, 270) = 33.064, p = .001, adjusted R2 = .27. This results indicate that there is a linear relationship in the population and the multiple regression model is a good fit for the data.

Further, findings show that confidence at completing a degree is significantly predictive of academic success. In addition, coursework enjoyability is a strong predictor of academic success. Specifically, the result shows that an increase in high impact engagement activity is associated with an increase in students’ academic success. In sum, these findings suggest that student participation in High Impact Engagement Practices might improve academic success and course retention. Theoretical and practical implications are discussed.

Adesope, O., & Sunday, O. J., & Ewumi, E. R., & Minichiello, A., & Asghar, M., & Claiborn, C. S. (2021, July), Investigating Factors that Predict Academic Success in Engineering and Computer Science Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37393

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