Abstract

University course scheduling is one of the most complex optimization problems in higher education institutions. With universities growing in size and offering a broad spectrum of majors and disciplines, the number of possible course scheduling combinations increases exponentially, rendering traditional ways of scheduling ineffective.

Although operations research has extensively studied automated scheduling algorithms, there has been limited investigations into the organization readiness of academic departments to implement such systems. This paper offers a hybrid data science framework that assesses departmental readiness for scheduling automation.

The study combines qualitative Zoom interview data from 19 academic departments with institutional scheduling rules from the Minnesota State University, Mankato’s Common Bell system, which was introduced in 2019 as a set of hard constraints governing when and how courses may be offered across general purpose and telepresence classrooms.

The methodology uses a combinatorial analysis to translate qualitative Zoom interview transcripts into quantitative features through a process of constraint engineering derived from information theory while integrating multiple data science techniques such as feature engineering, clustering analysis, random forest classification with Leave-One-Out Validation (LOOC), topic modeling using Latent Dirichlet Allocation (LDA) and sentiment analysis using VADER.

Advisor

Rushit Dave

Committee Member

Rajeev Bukralia

Committee Member

Mansi Bhavsar

Committee Member

Lin Chase

Date of Degree

2026

Language

english

Document Type

APP

Degree

Master of Science (MS)

Program of Study

Data Science

Department

Computer Information Science

College

Science, Engineering and Technology

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