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A Novel Research Design: Using Multilevel Discrete-Time Survival Analysis to Investigate the Effect of Calculus I on Engineering Student Persistence

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Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

Educational Research and Methods Division (ERM) Technical Session 14

Tagged Division

Educational Research and Methods Division (ERM)

Tagged Topic

Diversity

Permanent URL

https://peer.asee.org/46469

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Paper Authors

biography

Hayaam Osman Purdue University

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Hayaam Osman is a Ph.D. candidate in Engineering Education at Purdue University. She earned her M.S. in Electrical and Computer Engineering Technology from Purdue University and her Bachelor's degree in Electrical Engineering from United Arab Emirates University. Hayaam's research focuses on investigating student success in Engineering programs, employing quantitative methods. She utilizes multi-institutional datasets to explore engineering students' longitudinal development and trajectories.

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biography

Matthew W. Ohland Purdue University Orcid 16x16 orcid.org/0000-0003-4052-1452

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Matthew W. Ohland is the Dale and Suzi Gallagher Professor and Associate Head of Engineering Education at Purdue University. He has degrees from Swarthmore College, Rensselaer Polytechnic Institute, and the University of Florida. His research on the longitudinal study of engineering students and forming and managing teams has been supported by the National Science Foundation and the Sloan Foundation and his team received for the best paper published in the Journal of Engineering Education in 2008, 2011, and 2019 and from the IEEE Transactions on Education in 2011 and 2015. Dr. Ohland is an ABET Program Evaluator for ASEE and represents ASEE on the Engineering Accreditation Commission. He was the 2002–2006 President of Tau Beta Pi and is a Fellow of the ASEE, IEEE, and AAAS. He was inducted into the ASEE Hall of Fame in 2023.

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Abstract

The persistence of engineering students through graduation continues to be a concern in higher education. Previous studies have highlighted a link between students' performance in introductory mathematics courses and graduation rates. Focusing on a crucial foundational course within the engineering curriculum, the purpose of this study is to investigate how students’ performance in Calculus I impact their persistence in the engineering program. Utilizing data from 22 diverse educational institutions using Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD), multilevel discrete-time survival analysis is employed to examine the longitudinal persistence patterns among the nested structure of students within institutions. Discrete-time analysis is an event-based approach that has the advantage of analyzing time in discrete chunks during which the event of interest could occur. The technique is a type of survival analysis, which has been used in other studies in engineering education and other educational studies. This approach addresses various challenges associated with analyzing student persistence data such as dealing with censored observations – observations for whom their entire educational pathway is not yet known because they are still enrolled. Using a multilevel form of this analysis approach also accounts for the hierarchical nature of the data involving students nested within institutions and incorporating variables that change over time. Thus, the study takes into account the variability and complexities inherent in the analysis of different institutions and examines persistence patterns more comprehensively than previous studies. By incorporating a diverse range of institutions, the study captures a broader spectrum of experiences and contexts, which enhances the generalizability of the results.

Osman, H., & Ohland, M. W. (2024, June), A Novel Research Design: Using Multilevel Discrete-Time Survival Analysis to Investigate the Effect of Calculus I on Engineering Student Persistence Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46469

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