BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation

TL;DR

BamaER is a novel behavior-aware, memory-augmented model for exercise recommendation that effectively captures student behaviors, models knowledge states dynamically, and optimizes exercise diversity, leading to improved personalized recommendations.

Contribution

The paper introduces BamaER, integrating behavior modeling, dynamic knowledge tracing, and diversity-aware exercise filtering, addressing limitations of existing methods in personalized exercise recommendation.

Findings

Outperforms state-of-the-art baselines on five datasets

Effectively models heterogeneous student behaviors

Enhances recommendation diversity and coverage

Abstract

Exercise recommendation focuses on personalized exercise selection conditioned on students' learning history, personal interests, and other individualized characteristics. Despite notable progress, most existing methods represent student learning solely as exercise sequences, overlooking rich behavioral interaction information. This limited representation often leads to biased and unreliable estimates of learning progress. Moreover, fixed-length sequence segmentation limits the incorporation of early learning experiences, thereby hindering the modeling of long-term dependencies and the accurate estimation of knowledge mastery. To address these limitations, we propose BamaER, a Behavior-aware memory-augmented Exercise Recommendation framework that comprises three core modules: (i) the learning progress prediction module that captures heterogeneous student interaction behaviors via a tri-directional hybrid encoding scheme; (ii) the memory-augmented knowledge tracing module that maintains a dynamic memory matrix to jointly model historical and current knowledge states for robust mastery estimation; and (iii) the exercise filtering module that formulates candidate selection as a diversity-aware optimization problem, solved via the Hippopotamus Optimization Algorithm to reduce redundancy and improve recommendation coverage. Experiments on five real-world educational datasets show that BamaER consistently outperforms state-of-the-art baselines across a range of evaluation metrics.