2026 ASEE Annual Conference & Exposition

The Ranked Analysis of Major Programs System (RAMPS)

Presented at DSAI-Session 10: Curriculum Analytics, Degree Planning, and Student Pathways

Higher education presents students with increasingly structured curricula, complex prerequisite chains, and limited flexibility to explore different disciplines. These challenges often hinder academic mobility and timely degree completion, which are particularly critical in engineering education where course sequencing and prerequisite dependencies determine student progress. To address these challenges, we developed the Ranked Analysis of Major Programs System (RAMPS), a data-driven advising and degree-planning platform that transforms institutional and student data into transparent, interactive insights. Consider a second-year computer science student who has completed 45 credits and is exploring a switch to computer engineering. Using RAMPS, this student can instantly visualize how completed coursework would apply to the new major and how the change would affect their time to graduation. The system provides a side-by-side comparison between current and prospective majors, displaying which degree requirements are satisfied, partially satisfied, or unmet. Leveraging this data, RAMPS automatically generates a comprehensive, term-by-term completion plan that integrates prerequisite structures, sequencing logic, and historical course availability, which are especially crucial in engineering programs. For advisors and faculty, RAMPS reveals the data behind student decision-making, enabling proactive guidance and improved forecasting of enrollment demand. By applying advanced data integration and optimization methods, the system converts complex academic structures into clear, actionable pathways. Early testing suggests that this transparency helps students make informed choices, supports academic recovery for those changing majors, and enhances persistence in engineering and related STEM fields. Beyond its immediate utility for advising, RAMPS provides a foundation for longitudinal analytics and institutional planning,
allowing stakeholders to examine curriculum design, identify high-impact bottlenecks, and optimize resource allocation. As RAMPS advances toward broader institutional adoption, it represents a scalable, data-driven framework for improving academic efficiency, reducing barriers to completion, and strengthening student success in engineering education. In this paper, we first describe the design and architecture of the RAMPS platform, including the underlying data structures, algorithms, and integration methods. We then present the results of pilot testing and user feedback from students and advisors. Finally, we discuss how progress analytics derived from RAMPS can inform institutional decision-making and contribute to broader efforts to improve persistence and success in engineering education.

Authors

Melika Akbarsharifi http://orcid.org/https://0009-0004-9163-3707 The University of Arizona [biography]

Melika Akbarsharifi is a PhD student in Software Engineering at the University of Arizona, studying under Professor Gregory L. Heileman. Her research at the Curricular Analytics Lab focuses on applying machine learning and data analysis to improve educational outcomes. Key contributions include developing a cohort-tracking analytics platform that supports efforts to increase graduation rates by identifying and 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 expertise spans multiple programming languages and cloud computing, supporting her work on innovative educational technologies.
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 Academic Affairs and Professor of Electrical and Computer Engineering at the University of Arizona.
Roxana Akbarsharifi http://orcid.org/0009-0008-2985-4516 The University of Arizona [biography]

Roxana Akbarsharifi is a PhD student in Software Engineering at the University of Arizona. Her research focuses on educational analytics and developing tools to improve student outcomes and support academic success. Her research interests include software engineering, data analytics, and data visualization, with an emphasis on applying these technologies to solve educational challenges and enable data-driven decision making in higher education.
Kian G. Alavy University of Arizona [biography]

Kian Alavy is Director of Strategic Planning and Initiatives for the Division of Undergraduate Education and a doctoral student in Higher Education at the University of Arizona. Kian is interested in the history and evolution of undergraduate education offices at research universities in the United States, particularly their current nationwide organization around high-impact practices (HIPs). He earned his MA in Middle Eastern and North African Studies at the University of Arizona and his BA in History from the University of Minnesota.
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.
Kristina A Manasil The University of Arizona [biography]

Kristi Manasil is a second-year PhD student in the College of Information Science at the University of Arizona. She received her bachelor's degree in Computer Science from the University of Arizona. Her areas of focus are data visualization, machine learning, learning analytics, and educational data mining.
Mahshad Akbarsharifi The University of Arizona [biography]

Mahshad Akbarsharifi is an M.S. student in Electrical and Computer Engineering at the University of Arizona. Her research interests include artificial intelligence, machine learning, predictive modeling, and data analytics, with an emphasis on developing computational approaches for complex, data-rich problems. She has contributed to projects involving machine learning applications in educational data, student success and retention modeling, curriculum analytics, and the analysis of academic pathways. Her broader interests include the design of intelligent systems, interdisciplinary applications of AI, and the use of data-driven methods to support evidence-based decision-making and innovation.

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