2026 ASEE Annual Conference & Exposition

Visualizing Complexity in Curricula through Data-Driven Analytics

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

Institutional factors play an important but often overlooked role in how students move through their degree programs. There has been wide attention given to instructional challenges such as course difficulty, sequencing, and prerequisite design. However, factors like course fill rates, offering frequency, instructor availability, and enrollment capacity also have a strong impact on student progress and degree efficiency. These institutional constraints often operate invisibly, shaping the degrees of freedom that students have when organizing their schedules or recovering from a setback. When combined with instructional challenges, they can create structural bottlenecks that limit students’ ability to progress, thus extending time to degree and exacerbating equity gaps in access to required courses. While instructional and institutional factors have typically been examined separately, their interaction often produces structural bottlenecks that remain invisible without integrated analysis. Understanding how these factors interact, and making those interactions visible, is key to improving how programs are designed, scheduled, and managed across the institution. This paper presents a data-driven curriculum analytics platform that visualizes both instructional and institutional elements within a single, interactive framework. The system models a degree plan as a directed graph, where courses are represented as nodes and prerequisite relationships as edges. It then integrates institutional data such as course fill rates, offering patterns, and enrollment pressure with instructional data, including DFW rates, sequencing dependencies, and course complexity. Together, these layers reveal where and why students encounter barriers as they move through a program, allowing users to test potential interventions and assess their ripple effects across the curriculum. The platform provides situational awareness for faculty, advisors, and administrators, helping them identify, interpret, and address the dynamic interactions that constrain student movement. While engineering programs exemplify the challenges of limited scheduling flexibility, the framework applies broadly to any field where instructional and institutional structures combine to influence student outcomes. By layering the instructional and institutional data, the system supports data-informed decision-making that can lead to more efficient, equitable, and sustainable degree pathways.

Authors

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.
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 Affairs and Professor of Electrical and Computer Engineering at the University of Arizona.
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.
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.
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.

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