A Deep Reinforcement Learning Framework for Instructional Sequencing

Reinforcement Learning, a common framework for AI planing or decision making, is regarded as an effective framework fro planning students' learning sequences. However, previous research efforts rely on simulation data or students and few of them have verified effectiveness of their algorithms with teaching students in real educational environments. Also, the previous research is hard to using in real task such as online course or real classroom because of the strong assumptions and restrictions. In this paper, We propose a new deep reinforcement learning framework for instructional sequencing that can recommend students with personalized learning exercises in both MOOCs and classrooms. Based on our ATC model, is used to trace students learning process and Deep Reinforcement Learning Agent is used to induce efficient l earning sequences for students adaptively. Both simulation and experiment in classrooms confirm the effectiveness of our method.