Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2025

Abstract

There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose EduQate, a method employing interdependency-aware Q-learning to make informed decisions on arm selection at each time step. We establish the optimality guarantee of EduQate and demonstrate its efficacy compared to baseline policies, using students modeled from both synthetic and real-world data.

Keywords

Agents in Education, Networked RMABs, Q-Learning, Restless Multi-Armed Bandits

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, Detroit, Michigan, USA, May 19-23

First Page

2042

Last Page

2050

ISBN

9798400714269

Identifier

10.5555/3709347.3743842

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.5555/3709347.3743842

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