Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing

TL;DR

ExRec is a comprehensive framework that combines semantic knowledge tracing with reinforcement learning to personalize exercise recommendations, improving learning trajectories and generalization in online math education.

Contribution

We propose ExRec, integrating semantic question representations and tailored RL methods with knowledge tracing for personalized, interpretable learning paths.

Findings

ExRec outperforms baseline methods across four educational tasks.

The framework generalizes well to unseen questions.

ExRec produces interpretable student learning trajectories.

Abstract

We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing exercise recommendation approaches simulate student performance via knowledge tracing (KT) but they often overlook two key aspects: (a) the semantic content of questions and (b) the sequential, structured progression of student learning. To address this, our ExRec presents an end-to-end pipeline, from annotating the KCs of questions and learning their semantic representations to training KT models and optimizing several reinforcement learning (RL) methods. Moreover, we improve standard Q-learning-based continuous RL methods via a tailored model-based value estimation (MVE) approach that directly leverages the components of KT model in estimating cumulative knowledge improvement. We validate the effectiveness of our ExRec using various RL methods across four real-world tasks with different educational goals in online math learning. We further show that ExRec generalizes robustly to new, unseen questions and that it produces interpretable student learning trajectories. Together, our findings highlight the promise of KT-guided RL for effective personalization in education.