2024 ASEE Annual Conference & Exposition

Using Cohort-Based Analytics to Better Understand Student Progress

Presented at DSA Technical Session 6

Engineering programs are typically among the most tightly prescribed programs within the academic landscape on any university campus. The strict nature of these programs often results in students taking more credits than stipulated, thereby leaving them struggling to graduate in a timely manner. The ability to identify potential blockers or challenges in an engineering program’s curriculum is vital to student success and the promotion of on-time graduation. This paper provides a comprehensive examination of patterns and trends observed by a newly developed cohort tracking analytics platform. This platform provides analyses over a cohort of students which uncovers insights that are not easily identified when only looking at data at the individual student level. The analysis pinpoints courses that many students within the cohort have taken that are not applicable to the degree, along with the reasons why these courses are not applicable. It also identifies trends in courses that must be repeated by a significant portion of the cohort. It examines the courses constituting a program’s degree requirements that have yielded both the best and worst grade value outcomes. In addition, an exploration of a cohort’s efficiency of credit hour production is provided for both the home institution units and transfer units, which shows where credits are not aligning with degree requirements and therefore not counting towards degree completion. Finally, a comparative analysis of programs within the engineering field is performed as well as a comparison of engineering programs to non-engineering programs. This type of analysis demonstrates the differences in how students in engineering programs make progress towards their degree completion. The statistical analyses furnished by this platform provide administrators with an evidence-based foundation to support programmatic modifications and enhancements. This allows administrators to depart from the past practices of having to rely on anecdotal evidence and individual experiences. The empirical information from this platform assists advisors in aiding students in creating academic plans that provide students with the best chance for success while maximizing their credit hour efficiency. In this paper, the architecture and the visual display of the cohort tracking analytics platform are briefly discussed. Then we pivot to focus on the results of the analyses, comparing and contrasting three groups that consist of engineering disciplines within a department, departments within engineering colleges, and engineering colleges to other colleges at the institution. We conclude with a discussion of the potential actionable changes dictated by these results.

Authors

Kristina A Manasil The University of Arizona [biography]

Kristi Manasil is a first-year doctoral student within the School of Information at the University of Arizona. Having obtained her bachelor's degree in Computer Science from the University of Arizona, she subsequently garnered valuable industry experience as a Data Quality Specialist and Developer, contributing to the implementation of the student CatCloud platform for the institution. Her scholarly pursuits are centered around the interdisciplinary domains of data visualization, machine learning, learning analytics, and educational data mining.
Prof. Gregory L. Heileman http://orcid.org/0000-0002-5221-5682 The University of Arizona [biography]

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.
Bhavya Sharma The University of Arizona
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.
Mr. Aryan Ajay Pathare http://orcid.org/0000-0001-8010-312X The University of Arizona [biography]

A Master's student in Computer Science at the University of Arizona. His interests lie in software development, cloud computing, and machine learning.
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.
Roxana Sharifi The University of Arizona [biography]

Roxana Sharifi is a second-year master’s student in Electrical and Computer Engineering at the University of Arizona, where she also serves as a Graduate Research Assistant in the Curricular Analytics Lab. She holds a bachelor's degree in Software Engineering from the University of Science and Culture in Tehran, Iran. Her research interests include software engineering, cloud computing, data visualization, and Machine learning.
Mr. Rohit Hemaraja The University of Arizona [biography]

Rohit Hemaraja is a Master's student in Data Science at the School of Information at the University of Arizona. He is a Graduate Research Assistant with the Analysis of Higher Education Research Group. He earned his Bachelor of Engineering degree in Computer Science. His research focuses on machine learning, large language models and data management. His academic and professional interests lie at the intersection of these disciplines, reflecting his commitment to advancing the capabilities and applications of Data Science technologies.
Melika Akbarsharifi The University of Arizona [biography]

Melika Akbarsharifi is a Master's student in Electrical and Computer Engineering at the University of Arizona, studying under Professor Gregory L. Heileman. Her research at the Curricular Analytics Lab focuses on using machine learning and data analysis to enhance educational outcomes. Key contributions include developing a cohort-tracking analytics platform that assists in improving graduation rates by addressing curricular barriers.

Melika has co-authored papers presented at conferences such as the ASEE Annual Conference and Exposition, exploring the intersection of curriculum complexity and student performance. Her technical proficiency spans multiple programming languages and cloud computing, furthering her research into innovative educational technologies

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