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Board 201: A New Public Dataset for Exploring Engineering Longitudinal Development by Leveraging Curricular Analytics

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

11

DOI

10.18260/1-2--42606

Permanent URL

https://peer.asee.org/42606

Download Count

137

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Paper Authors

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David Reeping University of Cincinnati Orcid 16x16 orcid.org/0000-0002-0803-7532

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Dr. David Reeping is an Assistant Professor in the Department of Engineering and Computing Education at the University of Cincinnati. He earned his Ph.D. in Engineering Education from Virginia Tech and was a National Science Foundation Graduate Research Fellow. He received his B.S. in Engineering Education with a Mathematics minor from Ohio Northern University. His main research interests include transfer student information asymmetries, threshold concepts, curricular complexity, and advancing quantitative and fully integrated mixed methods.

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Matthew W. Ohland Purdue University, West Lafayette 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. He was the 2002–2006 President of Tau Beta Pi and is a Fellow of the ASEE, IEEE, and AAAS.

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Kenneth Reid University of Indianapolis Orcid 16x16 orcid.org/0000-0003-2337-7495

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Kenneth Reid is the Associate Dean and Director of Engineering at the R. B. Annis School of Engineering at the University of Indianapolis. He and his coauthors were awarded the Wickenden award (Journal of Engineering Education, 2014) and Best Paper award, Educational Research and Methods Division (ASEE, 2014). He was awarded an IEEE-USA Professional Achievement Award (2013) for designing the B.S. degree in Engineering Education. He is a co-PI on the “Engineering for Us All” (e4usa) project to develop a high school engineering course “for all”. He is active in engineering within K-12, (Technology Student Association Board of Directors) and has written multiple texts in Engineering, Mathematics and Digital Electronics. He earned a PhD in Engineering Education from Purdue University, is a Senior Member of IEEE, on the Board of Governors of the IEEE Education Society, and a Member of Tau Beta Pi.

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Hossein EbrahimiNejad Drexel University

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Hossein EbrahimiNejad is a data scientist currently working with the office of Enrollment Analytics at Drexel University. He received his PhD in Engineering Education from Purdue University, where he gained a strong knowledge of higher education and strategic enrollment management. Hossein's skills in data management, data visualization, and predictive modeling allow him to empower stakeholders to make strategic decisions using advanced analytic information. With Drexel's commitment to promoting and supporting student success, Hossein's work with Enrollment Analytics allows the University to make data-driven strategic decisions regarding enrollment and financial projections that are as effective and efficient as possible.

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Nahal Rashedi

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Abstract

Considering the increasing demand for engineering graduates, understanding what is limiting students from completing their degrees has been a consistent question posed in the literature. The nontrivial variance in pathways students take in obtaining an engineering degree, especially in cases where students abandon their studies, suggests that longitudinal datasets can hold a wealth of information to uncover factors contributing to attrition. Accordingly, this project uses existing data to explore curricular factors that create barriers for different students by leveraging a new framework for quantifying the impact of such factors, Curricular Analytics. Curricular Analytics uses network analysis to measure sequencing and interconnectedness in a plan of study. There are two intrinsic measurements associated with each course in Curricular Analytics: (1) the blocking factor, which is a count of how many courses are inaccessible to a student upon failing and (2) the delay factor, the longest prerequisite chain through the course. The sum of these factors for all courses in a plan of study, called structural complexity, characterizes a measure of the curriculum’s complexity. This project combines Curricular Analytics with course-taking data from the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD).

Curricular Analytics is a relatively new framework with the potential to address existing research questions and generate new ones. To date, curricular complexity has been used to correlate program quality with a curriculum’s structural complexity and predict four, five, and six-year graduation rates for first-time-in-college and transfer students. This paper/poster reports on the year one activities for this project, which addresses the potential of the framework to bring a renewed perspective to MIDFIELD dataset by creating a new public dataset of curricular information for Civil Engineering, Electrical Engineering, Mechanical Engineering, Chemical Engineering, and Industrial Engineering at 13 MIDFIELD institutions with data current up to 2015. For each institution, we are collecting the previous 10 years of curricular data since the institution’s last record in the appropriate format for network analysis. This results in an upper bound of 650 networks; considering some institutions do not offer all five disciplines of interest, our dataset contains 535 networks. We are exploring these networks longitudinally within and between disciplines and institutions.

The next steps of this project involve creating course-taking trajectories from the course table in MIDFIELD using association analysis. The characteristics for these trajectories, including structural complexity, retaking behavior, and major switching, will be clustered and disaggregated across strata, such as first-time-in-college versus transfer, race, gender, and first-generation status. We plan to correlate structural complexity with ecosystem metrics like discipline stickiness and migration yield. By disseminating these results directly to institutional stakeholders and the broader engineering education community, we anticipate that this project can impact curricular design for all engineering students and help us understand what academic policies are inhibiting degree attainment for diverse groups.

Reeping, D., & Ohland, M. W., & Reid, K., & EbrahimiNejad, H., & Rashedi, N. (2023, June), Board 201: A New Public Dataset for Exploring Engineering Longitudinal Development by Leveraging Curricular Analytics Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42606

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2023 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015