2024 ASEE Annual Conference & Exposition

Enhancing Academic Pathways: A Data-Driven Approach to Reducing Curriculum Complexity and Improving Graduation Rates in Higher Education

Presented at DSA Technical Session 1

Curriculum structure and prerequisite complexity significantly influence student progression and graduation rates. Thus, efforts to find suitable measures to reduce curriculum complexity have recently been employed to the utmost. Most of these efforts use the services of domain experts, such as faculty and student affairs staff. However, it is tedious for a domain expert to study and analyze a full curriculum in an attempt to reform its structure, given all the complexities associated with its prerequisite dependencies and learning outcomes. Things can become even more complicated when a set of curricula is examined. Therefore, efforts to automate the process of restructuring curricula are beneficial to helping the university community find the best available practices to reduce the complexity of their institutional curricula. This study introduces an innovative framework for automating curriculum restructuring, employing a combination of graphical models and machine learning techniques. In particular, we use latent tree graphical models and collaborative filtering to induce curriculum reforms without needing a domain expert. The approach used in this paper is data-driven, where actual student data and actual university curricula are utilized. Five thousand seventy-three student records from the University of New Mexico (UNM) are used for this purpose. Results demonstrate the restructuring impact on an engineering curriculum, particularly the computer engineering program at UNM. The effect is an improvement in the graduation rates of the students attending the revised engineering programs. These results are validated using a Markov Decision Processes~(MDP) model. Furthermore, the findings of this paper showcase the practical benefits of our approach and offer valuable insight for future advancements in curriculum restructuring methodologies.

Authors

Dr. Ahmad Slim The University of Arizona [biography]

Dr. Ahmad Slim is a PostDoc researcher at the University of Arizona, where he specializes in educational data mining and machine learning. With a Ph.D. in Computer Engineering from the University of New Mexico, he leads initiatives to develop analytics solutions that support strategic decision-making in academic and administrative domains. His work includes the creation of predictive models and data visualization tools that aim to improve student recruitment, retention, and success metrics. Dr. Slim's scholarly contributions include numerous articles on the application of data science in enhancing educational practices.
Prof. Gregory L. Heileman http://orcid.org/0000-0002-5221-5682 The University of Arizona [biography]

Gregory (Greg) L. Heileman currently serves as the Associate Vice Provost for Academic Administration and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating collaboration across campus t
Husain Al Yusuf http://orcid.org/0000-0002-4769-2089 The University of Arizona [biography]

Husain Al Yusuf is a third-year PhD candidate in the Electrical and Computer Engineering Department at the University of Arizona. He is currently pursuing his PhD with a research focus on applying machine learning and data analytics to higher education, aiming to enhance student outcomes and optimize educational processes.

Husain Al Yusuf holds an M.Sc in Computer Engineering from the University of New Mexico and brings over fifteen years of professional experience as a technology engineer, including significant roles in cloud computing and infrastructure development at a big technologies company and financial services industry.
Dr. Yiming Zhang The University of Arizona [biography]

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.
Asma Wasfi
Mohammad Hayajneh
Bisni Fahad Mon United Arab Emirates University
Ameer Slim University of New Mexico

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