Masked Deep Q-Recommender for Effective Question Scheduling
This work addresses the challenge of reducing manual effort for teachers in personalized learning by automating question scheduling, though it is incremental as it builds on existing knowledge tracing and reinforcement learning methods.
The paper tackles the problem of personalized question scheduling for students by introducing a reinforcement learning-based recommender that uses knowledge tracing to predict student knowledge and select questions, resulting in a 21.3% increase in average student knowledge level compared to a 10% increase from an expert-designed baseline in a simulated two-week experiment.
Providing appropriate questions according to a student's knowledge level is imperative in personalized learning. However, It requires a lot of manual effort for teachers to understand students' knowledge status and provide optimal questions accordingly. To address this problem, we introduce a question scheduling model that can effectively boost student knowledge level using Reinforcement Learning (RL). Our proposed method first evaluates students' concept-level knowledge using knowledge tracing (KT) model. Given predicted student knowledge, RL-based recommender predicts the benefits of each question. With curriculum range restriction and duplicate penalty, the recommender selects questions sequentially until it reaches the predefined number of questions. In an experimental setting using a student simulator, which gives 20 questions per day for two weeks, questions recommended by the proposed method increased average student knowledge level by 21.3%, superior to an expert-designed schedule baseline with a 10% increase in student knowledge levels.