Decision-Making Support for Adaptive Learning Management Systems based on Bayesian Inference

Bencomo, Nelly; Samin, Huma; Pavlich-Mariscal, Jaime

Abstract

A novel approach will be applied to the domain of virtual education, which involves an adaptive learning management system using Bayesian Learning. The student's progress is considered partially observable based on what has been monitored. The acquired skills by students are monitored by taking into account the results obtained from each activity performed by the student. Bayesian learning and Partially Observable Decision Processes (POMDPs) are used to guide and adapt (with the use of interventions) the learning plans according to the needs and individual characteristics of the students and their learning progress.

Citation

Bencomo, N., Samin, H., & Pavlich-Mariscal, J. (2022, July). Decision-Making Support for Adaptive Learning Management Systems based on Bayesian Inference. Paper presented at CausalEDM'22, Durham

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: CausalEDM'22
• Start Date: Jul 27, 2022
• End Date: Jul 31, 2022
• Acceptance Date: Jul 7, 2022
• Deposit Date: Oct 30, 2024
• Publicly Available Date: Nov 1, 2024
• Peer Reviewed: Peer Reviewed
• Keywords: Learning management system; Reinforcement Learning; POMDP; Bayesian inference; Decision making; Uncertainty
• Public URL: https://durham-repository.worktribe.com/output/2993933