Board 438: Year Two of Developing a New Dataset for Analyzing Engineering Curricula

David Reeping; Kenneth Reid; Matthew W. Ohland; NAHAL RASHEDI

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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: June 26, 2024
Conference Session: NSF Grantees Poster Session
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 11
DOI: 10.18260/1-2--47029
Permanent URL: https://peer.asee.org/47029
Download Count: 119

Paper Authors

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David Reeping University of Cincinnati 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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Kenneth Reid University of Indianapolis 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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Matthew W. Ohland Purdue University, West Lafayette 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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NAHAL RASHEDI University of Cincinnati

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PhD Student of Engineering Education

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Abstract

This paper discusses the developments during Year 2 for a project concerned with analyzing the curricula of engineering programs in the United States to understand the structural barriers embedded in degree requirements that could push out diverse groups of students. We are using an emerging method for quantifying the complexity of these programs called Curricular Analytics. This method involves treating the prerequisite relationships between courses as a network and applying graph theoretic measures to calculate a curriculum’s structure complexity. In Year 1, we collected 497 plans of study representing five engineering disciplines (i.e., Mechanical, Civil, Electrical, Chemical, and Industrial) across 13 institutions - spanning a decade. To ensure the dataset is as useful as possible to engineering education researchers, we have intentionally aligned our data collection with institutions available in the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD).

One of the outputs of this project is an R package that will enable researchers and practitioners to explore and leverage the dataset in their work by enabling the calculations to be completed at scale. With the efforts in Year 1, the package has the required functionality to compute the necessary metrics for Curricular Analytics. During Year 2, we have been building functions to manipulate course-taking trajectories of actual student data such that they can be compared to one another using association analysis. Association analysis will enable us to mine common course-taking patterns disaggregated by strata like institution, discipline, first-generation-status, and transfer-status and reconstruct them as networks to complement the plan of study data. Moreover, after sharing this work in preliminary forms with faculty, there was a desire for more customized functions. Thus, we are currently conducting a systematic literature review of how Curricular Analytics has been applied and extended to search for usable metrics to add to our package.

Much of Year 2 has been spent verifying the data and correcting errors that would impact the results of any analysis, whether quantitative or qualitative, by exploring the dataset using a combination of descriptive statistics and visualizations like histograms, boxplots, and longitudinal plots. As the data currently exists, the mean structural complexity of all engineering programs we considered (n = 497) is 313, and the median is 294. Chemical engineering has the highest mean structural complexity of 430, followed by mechanical engineering with a structural complexity of 369. The remaining disciplines were more tightly clustered together: electrical with 287, industrial with 248, and civil with 232. Although we are finalizing corrections to these data, it is not expected that the results will change significantly. We are currently sampling cases at the distribution's tails in the box plots of structural complexity to explore the extreme cases in our dataset and jumpstart analyses regarding curricular design patterns.

This paper will provide details on the preliminary analyses we have conducted using Curricular Analytics, an introduction to the R package, and updates from our systematic literature review.

Citation
Format

Reeping, D., & Reid, K., & Ohland, M. W., & RASHEDI, N. (2024, June), Board 438: Year Two of Developing a New Dataset for Analyzing Engineering Curricula Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47029

TY - CPAPER
AB - This paper discusses the developments during Year 2 for a project concerned with analyzing the curricula of engineering programs in the United States to understand the structural barriers embedded in degree requirements that could push out diverse groups of students. We are using an emerging method for quantifying the complexity of these programs called Curricular Analytics. This method involves treating the prerequisite relationships between courses as a network and applying graph theoretic measures to calculate a curriculum’s structure complexity. In Year 1, we collected 497 plans of study representing five engineering disciplines (i.e., Mechanical, Civil, Electrical, Chemical, and Industrial) across 13 institutions - spanning a decade. To ensure the dataset is as useful as possible to engineering education researchers, we have intentionally aligned our data collection with institutions available in the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD).

One of the outputs of this project is an R package that will enable researchers and practitioners to explore and leverage the dataset in their work by enabling the calculations to be completed at scale. With the efforts in Year 1, the package has the required functionality to compute the necessary metrics for Curricular Analytics. During Year 2, we have been building functions to manipulate course-taking trajectories of actual student data such that they can be compared to one another using association analysis. Association analysis will enable us to mine common course-taking patterns disaggregated by strata like institution, discipline, first-generation-status, and transfer-status and reconstruct them as networks to complement the plan of study data. Moreover, after sharing this work in preliminary forms with faculty, there was a desire for more customized functions. Thus, we are currently conducting a systematic literature review of how Curricular Analytics has been applied and extended to search for usable metrics to add to our package.

Much of Year 2 has been spent verifying the data and correcting errors that would impact the results of any analysis, whether quantitative or qualitative, by exploring the dataset using a combination of descriptive statistics and visualizations like histograms, boxplots, and longitudinal plots. As the data currently exists, the mean structural complexity of all engineering programs we considered (n = 497) is 313, and the median is 294. Chemical engineering has the highest mean structural complexity of 430, followed by mechanical engineering with a structural complexity of 369. The remaining disciplines were more tightly clustered together: electrical with 287, industrial with 248, and civil with 232. Although we are finalizing corrections to these data, it is not expected that the results will change significantly. We are currently sampling cases at the distribution&#39;s tails in the box plots of structural complexity to explore the extreme cases in our dataset and jumpstart analyses regarding curricular design patterns.

This paper will provide details on the preliminary analyses we have conducted using Curricular Analytics, an introduction to the R package, and updates from our systematic literature review.
AU - David Reeping
AU - Kenneth Reid
AU - Matthew W. Ohland
AU - NAHAL RASHEDI
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Board 438: Year Two of Developing a New Dataset for Analyzing Engineering Curricula
UR - https://peer.asee.org/47029
DO - 10.18260/1-2--47029
ER -