EduQate: Generating Adaptive Curricula through RMABs in Education Settings

Sidney Tio, Dexun Li, Pradeep Varakantham

Keywords: Machine Learning in Education, Restless Multi-armed bandits, Adaptive Curricula

TL;DR: We introduce a novel RMAB formulation and a teacher algorithm that models interdependencies amongst learning content to maximize learning efficiency.

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.

Primary Area: Machine learning for other sciences and fields

Submission Number: 6309