Masked Deep Q-Recommender for Effective Question Scheduling

TL;DR

This paper presents a reinforcement learning-based question scheduling model that personalizes question selection to improve student knowledge levels effectively, reducing manual effort for teachers.

Contribution

It introduces a novel RL-based recommender combined with knowledge tracing for personalized question scheduling, outperforming baseline methods in simulated experiments.

Findings

Questions recommended increased student knowledge by 21.3%.

The method outperformed expert-designed schedules with a 10% increase.

Effective in boosting knowledge levels with automated scheduling.

Abstract

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.