  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.
