  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.
