Masters Theses

Keywords and Phrases

academic advising; class scheduling; constraint programming; course recommendation; discrete optimization; technology-enhanced learning

Abstract

This work presents a degree planning tool developed as part of the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS) project which generates complete, valid, and personalized degree paths at any point from admission to graduation. This eliminates tedious calculation and double-checking, allowing advisors to focus on a student’s long-term plans and students to proactively explore potential degree paths. The original research contribution of this work is the use of a unified model for academic requirements to automatically translate complex, real-world curricula into a constraint programming model that can be quickly optimized based on personalized student criteria.

Automatically translating existing data into my unified model as an intermediary step allows complex requirements to be broken down into simple components, and lets data from multiple sources interoperate within the same model. I describe the implementation of translations from the PeopleSoft and uAchieve internal data formats, the two systems in use at Missouri S&T, but the unified model is general enough to represent a wide variety of requirements. The system can be straightforwardly extended to support academic requirements in different formats and from different universities. Supporting this complex, real-world data is what ultimately differentiates this approach; previous tools either limit the expressiveness of their academic requirements or only generate approximate degree paths. This creates a gap between the hypothetical world being explored by users and the real world they must make decisions in. Using this approach however, PERCEPOLIS gets the best of both worlds: users can trust that the generated degree paths satisfy all academic requirements while prioritizing their personal criteria like preferred courses, semester difficulty, and time-to-degree.

Advisor(s)

Sedigh, Sahra
Hurson, A. R.

Committee Member(s)

Morales, Ricardo

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2025

Pagination

ix, 63 pages

Note about bibliography

Includes_bibliographical_references_(pages 58-61)

Rights

© 2025 Mitchell Lee Skaggs , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12547

Download

Share

 
COinS