David Reeping presents Curricular Analytics, a data-driven approach to analyzing engineering curricula. Learn how network analysis can identify bottlenecks, compare programs, and improve student pathways.
March 27, 2pm CT
Kiewit Hall A253
Title: Measuring the Complexity of Your Curriculum for First-Time-In-College and Transfer Students
Abstract: Engineering curricula are highly sequential and commonly include an intricate set of prerequisites, often leading to chains of courses where students can get stuck - especially in courses with low pass rates called gateway or weed-out courses. When developing new curricula or tweaking existing programs, few data-driven tools exist to help identify potential bottlenecks, perform comparisons among peer programs, and simulate how different students could flow through various scenarios. In this seminar, we will explore an analytical framework for curriculum development and change called Curricular Analytics, which is growing in popularity. In essence, Curricular Analytics measures how “complex” a curriculum is by drawing ideas from network analysis. Thus, this framework allows us to quantify the interconnectivity of requirements in a plan of study and correlate the results with other variables like student outcomes, including retention and graduation. We will discuss two ongoing projects to highlight the framework’s applications. The first project involves creating and analyzing a new publicly available plan of study dataset to quantitatively compare engineering programs in five disciplines across 13 institutions in the United States over a decade (n ~ 500 plans of study). Additionally, we explore how student course-taking patterns within these programs differ using a high-dimensional clustering technique called n-TARP. The second project concerns the refinement of Curricular Analytics to be sensitive to issues faced by students entering a four-year program from a community college. To accomplish this revision, transfer professionals (n = 38) from across the U.S. were engaged using focus groups to discuss unique curricular challenges transfer students face. The transcripts were analyzed using constructivist grounded theory to elicit a theory of curricular complexity for transfer students. Practical guidance and resources will be provided for those interested in applying Curricular Analytics in their own contexts.
Bio: Dr. David Reeping is an Assistant Professor in the Department of Engineering and Computing Education at the University of Cincinnati and a Fellow of the Institute for Learning Research within the Office of the Provost. 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.