EduQate: Generating Adaptive Curricula through RMABs in Education Settings

TL;DR

EduQate introduces a novel interdependency-aware reinforcement learning approach using Restless Multi-armed Bandits to optimize personalized educational curricula, accounting for content interdependencies and demonstrating improved performance over baselines.

Contribution

The paper proposes EduQate, a new method employing network-based RMABs and interdependency-aware Q-learning for adaptive education, with theoretical guarantees and empirical validation.

Findings

EduQate outperforms baseline policies in synthetic and real-world data.

The method effectively models content interdependencies in educational settings.

Optimality guarantees are established for EduQate.

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.