Masters Theses

Keywords and Phrases

Collaborative filtering; E-learning; Q-learning; Recommender system; Reinforcement-learning

Abstract

"This work describes a recommendation approach designed to enhance student success by identifying semester schedules and graduation paths. The primary objective is to provide personalized graduation path recommendations rooted in individual student performance and draw insights from the academic journeys of similar students who successfully graduated. The original research contribution of this work lies in the development of a graduation path recommender system that leverages a combination of Markov Decision Process, Q-Learning, and collaborative filtering techniques to pinpoint graduation paths with a higher likelihood of leading students to success based on their academic progress thus far. The effectiveness of the proposed approach will be verified through its use of the previous student data, and course data within different departments such as Computer Science and Computer Engineering.

The proposed approach distinguishes itself through its utilization of collaborative filtering to identify similar students and understand how they achieved success in their graduation journeys and Q-learning to identify the semester load that leads a student to academic success. Moreover, it can be adapted to various disciplines provided there is a sufficient amount of data available to generate tailored pathways. This recommendation framework has been successfully implemented within the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS). PERCEPOLIS has been specifically designed to support student success by offering guidance in course selection and graduation pathways, with the goal to improve retention rates, enhanced student performance, and increase graduation rates" -- Abstract, p. iii

Advisor(s)

Sarvestani, Sahra Sedigh
Hurson, Ali

Committee Member(s)

Taylor, Patrick

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

vii, 47 pages

Note about bibliography

Includes_bibliographical_references_(pages 44-45)

Rights

©2024 Colton Walker , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12415

Electronic OCLC #

1478161759

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