Abstract
This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped and implemented within the scope of the PERCEPOLIS cyber-infrastructure and its effectiveness has been verified on a testbed of real student records for select academic programs.
Recommended Citation
C. Walker et al., "Learning from the Past: using Peer Data to Improve Course Recommendations in Personalized Education," Proceedings - 2024 IEEE 48th Annual Computers, Software, and Applications Conference, COMPSAC 2024, pp. 127 - 137, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/COMPSAC61105.2024.00028
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
collaborative filtering; e-learning; Q-learning; recommender system; reinforcement learning
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Comments
National Science Foundation, Grant DUE-1742523