Minimizing Curricular Complexity through Backwards Design
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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June 26, 2024
- Conference Session
- Tagged Topic
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Data Science & Analytics Constituent Committee (DSA)
- Page Count
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11
- DOI
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10.18260/1-2--47779
- Permanent URL
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https://peer.asee.org/47779
- Download Count
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63
Paper Authors
Gregory L. Heileman
The University of Arizona
orcid.org/0000-0002-5221-5682
Gregory (Greg) L. Heileman currently serves as the Vice Provost for Undergraduate Education and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating collaboration across campus to strategically enhance quality and institutional capacity related to undergraduate programs and academic administration. He has served in various administrative capacities in higher education since 2004.
Professor Heileman currently serves on the Executive Committee of AZTransfer, an organization that works across the system of higher education in the State of Arizona to ensure students have access to efficient, seamless, and simple ways to transfer from a community college to a university in Arizona. He serves on the board of the Association for Undergraduate Education at Research Universities, a consortium that brings together research university leaders with expertise in the theory and practice of undergraduate education and student success. In addition, he is a fellow at the John N. Gardner Institute for Excellence in Undergraduate Education.
Professor Heileman’s work on analytics related to student success has led to the development of a theory of curricular analytics that is now being used broadly across higher education in order to inform improvement efforts related to curricular efficiency, curricular equity, and student progression.
Yiming Zhang The University of Arizona
Yiming Zhang completed his doctoral degree in Electrical and Computer Engineering from the University of Arizona in 2023. His research focuses on machine learning, data analytics, and optimization in the application of higher education.
Abstract
In this paper, we first describe the Optimal Learning Outcomes Assignment (OLOA) problem, which involves assigning learning outcomes to courses during the backwards curriculum design process in ways that minimize the complexity of the resulting curriculum. An approximation algorithm for the OLOA problem is then described that yields novel solutions to important engineering curricular design challenges. Reducing curricular complexity, while maintaining effective learning outcomes attainment, increases the likelihood students will complete a curriculum and earn a degree. The rationale for the approach taken here follows from the fact that by rearranging the learning outcomes among the courses in a curriculum, the overall structure of a curriculum can be changed. Thus, the OLOA problem provides a criterion for finding curricular structures that enhance student success. The OLOA problem is shown to be strongly NP-complete; however, an integer quadratic programming approximation algorithm is described that effectively produces practical, efficient, and novel solutions for attaining the most important leaning outcomes in an undergraduate engineering curriculum.
Heileman, G. L., & Zhang, Y. (2024, June), Minimizing Curricular Complexity through Backwards Design Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47779
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