Masters Theses

Keywords and Phrases

course recommendation; multi-objective optimization; recommender systems; technology-enhanced learning

Abstract

"A student’s academic history, course availability at their institution, and the overall degree of difficulty of the schedule for each semester are all critical factors in their academic success and experience. This thesis proposes an advanced recommendation algorithm that considers these real and conflicting factors that are involved in identifying a prudent degree path - a semester-by-semester course schedule to graduation - for each student. The original contribution of this work is the use of weighted-sum multi-objective constraint programming to minimize an increased number of optimization criteria and identify Pareto-optimal degree paths to be recommended to the student. The conflicting objectives optimized in this problem are minimum time-to-degree, maximum projected course grades, maximum alignment with the student’s interest, and maximum degree path robustness. The proposed approach is validated through simulation and its efficacy is compared to previous results on the Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support (PERCEPOLIS) platform" -- Abstract, p. iii

Advisor(s)

Sarvestani, Sahra Sedigh
Hurson, Ali

Committee Member(s)

Alsharoa, Ahmad

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

viii, 38 pages

Note about bibliography

Includes_bibliographical_references_(pages 34-35)

Rights

©2024 Arianna Gail Sy Chaves , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12375

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