EduQate: Generating Adaptive Curricula through RMABs in Education Settings
This addresses the challenge of adaptive curriculum generation for students by incorporating content interdependencies, representing an incremental improvement over existing methods that assume independence.
The paper tackles the problem of efficiently achieving mastery across interdependent learning content in personalized education by introducing EduQate, a method using interdependency-aware Q-learning with EdNetRMABs, which demonstrates efficacy compared to baseline policies on synthetic and real-world student data.
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.