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Modeling Trajectories of Latent Classes to Understand the Academic Performance of Engineering Students

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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

Page Count

18

Permanent URL

https://strategy.asee.org/37514

Download Count

25

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Paper Authors

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Heather Lee Perkins North Carolina State University Orcid 16x16 orcid.org/0000-0002-8757-0545

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Heather graduated from the Applied Social and Community Psychology program in the spring of 2021, after completing her Bachelor of Science in Psychology from the University of Cincinnati. She has participated in various research projects examining the interaction between stereotypes and science interest and confidence, their influence upon womens’ performance in school and the workplace, and their presence in the media and consequences for viewers. Her primary research interest is science identity, STEM education, and participation in online communities.

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Justin Charles Major Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-3111-8509

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Justin C. Major is a fifth-year Ph.D Candidate and National Science Foundation Graduate Research Fellow in the Purdue University Engineering Education Program. As an undergraduate student at the University of Nevada, Reno (UNR), Justin completed Bachelor's degrees in both Mechanical Engineering and Secondary Mathematics Education with an informal emphasis in engineering education. Through his involvement in the UNR PRiDE Research Lab and engagement with the UNR and Northern Nevada STEM Education communities, he studied student motivation, active learning, and diversity; developed K-12 engineering education curriculum; and advocated for socioeconomically just access to STEM education. As a Ph.D. Candidate with the STRiDE Research Lab at Purdue University, Justin's dissertation research focuses on the study of Intersectionality Theory and the intersectionality of socioeconomic inequality in engineering education, use of critical quantitative methodology and narrative inquiry to understand the complex stories of engineering students from traditionally minoritized backgrounds, and the pursuit of a socioeconomically just engineering education.

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Julianna S. Ge Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-0084-951X

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Julianna Ge is a Ph.D. student in the School of Engineering Education at Purdue University. At Purdue, she created and currently teaches a novel course for undergraduate engineering students to explore the intersections of wellbeing, leadership, diversity and inclusion. As an NSF Graduate Research Fellow, her research interests intersect the fields of engineering education, positive psychology, and human development to understand diversity, inclusion, and success for undergraduate engineering students. Prior to Purdue, she received dual bachelor’s degrees in Industrial Engineering and Human Development and Family Studies from the University of Illinois at Urbana-Champaign. Her prior work experiences include product management, consulting, tutoring, marketing, and information technology.

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Matthew Scheidt Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0001-6779-1992

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Matthew Scheidt is a Ph.D. candidate in Engineering Education at Purdue University. He graduated from Purdue University with a B.S. in Mechanical Engineering, The Ohio State University with a M.S. in Mechanical Engineering with a focus in Ultrasonic Additive Manufacturing. Matt is currently part of Dr. Allison Godwin’s STRIDE (Shaping Transformative Research on Identity and Diversity in Engineering) research group at Purdue. Matt’s research interests include engineering student success, both quantitatively and qualitatively. He is also interested in military veterans' success in engineering.

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Allison Godwin Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-0741-3356

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Allison Godwin, Ph.D. is an Associate Professor of Engineering Education and Chemical Engineering at Purdue University. Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students’ identity development. She has won several awards for her research including the 2016 American Society of Engineering Education Educational Research and Methods Division Best Paper Award and the 2018 Benjamin J. Dasher Best Paper Award for the IEEE Frontiers in Education Conference. She has also been recognized for the synergy of research and teaching as an invited participant of the 2016 National Academy of Engineering Frontiers of Engineering Education Symposium and the Purdue University 2018 recipient of School of Engineering Education Award for Excellence in Undergraduate Teaching and the 2018 College of Engineering Exceptional Early Career Teaching Award.

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Abstract

This research paper investigates the grade point average (GPAs) trajectories of engineering students and provides a step-by-step guide for those interested in adapting or using the technique. The use of GPA as a measure of academic achievement is sometimes controversial, but it remains a consistently utilized measure of student success (Scheidt, M., Senkpeil, R., Chen, J., Godwin, A., & Berger, E. IEEE Frontiers in Education Conference (FIE) (pp. 1-5). 2018). A student’s GPA is often used by universities to monitor their eligibility for financial aid, filter admission to colleges and departments of engineering, and determine satisfactory progress in degree attainment. In engineering in particular, a low GPA is often seen as a signal that one is not “cutting it” in the highly competitive, rigorous culture of engineering. (Godfrey, E., & Parker, L. Journal of Engineering Education, 99(1), 5-22, 2010). For many students, that signal can suggest that they should leave the major.

Thus, understanding how engineering students’ GPA functions over time can provide insight into students’ academic outcomes. In conjunction with additional behavioral data and psychological measures, temporal changes in GPA can also help researchers identify particular supports and barriers for student success. Several techniques can be used to examine longitudinal outcomes we also have a robust collection of methodologies for identifying latent classes or clusters within larger groups. The intersection of longitudinal and latent class approaches can add further value to research about GPA trajectories and their interpretation. For instance, growth mixture modeling is used with increasing frequency to identify and model group-based changes over time (Frankfurt, S., Frazier, P., Syed, M., & Jung, K. R. The Counseling Psychologist, 44(5), 622-660, 2016). This approach allows for the identification of multiple subpopulations, description of longitudinal change within each subpopulation, and examination of differential changes among unobserved sub-populations. This approach can provide insight about patterns of academic success, as measured by GPA changes over time among and within groups of students.

Along with investigating GPA changes over time, this paper is simultaneously designed to act as a resource for engineering education researchers interested in performing group-based trajectory analyses. Specifically, this paper provides a step-by-step description of exploratory latent class trajectory modeling (LCTM). The data for this example are GPAs from 489 engineering undergraduate students, as collected from institutional records, across five time points (Fall 2018 - Fall 2020). The results of the LCTM indicate a four-group solution over time, with one group starting with high GPA and decreasing slightly, a second group starting around average and decreasing slightly, a third group that started around average and decreased sharply, and a fourth group that started below average and increased sharply. Potential issues with this solution include the possible effects of missingness and attrition, as well as issues identifying the best random effect structure using the current program (e.g., whether residual variances are allowed to vary by class, allowing for random intercepts or slopes). In addition to reviewing the analysis and providing a guide and resources for other researchers, this paper will also discuss future plans for analysis with a larger sample who also provided information about a variety of non-cognitive and affective (NCA) factors in order to identify significant predictors of engineering student success.

Perkins, H. L., & Major, J. C., & Ge, J. S., & Scheidt, M., & Godwin, A. (2021, July), Modeling Trajectories of Latent Classes to Understand the Academic Performance of Engineering Students Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://strategy.asee.org/37514

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