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[2602.15879] BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Summary
The paper presents BamaER, a memory-augmented model designed for personalized exercise recommendations based on students' learning behaviors and history, outperforming existing methods.
Why It Matters
This research addresses critical gaps in exercise recommendation systems by integrating behavioral data and memory augmentation, enhancing the accuracy of learning progress assessments. As personalized education becomes increasingly important, BamaER's approach could significantly improve student engagement and outcomes.
Key Takeaways
- BamaER leverages a tri-directional hybrid encoding scheme for better learning progress prediction.
- The model includes a dynamic memory matrix for improved knowledge state estimation.
- Exercise recommendations are optimized using the Hippopotamus Optimization Algorithm to enhance diversity and coverage.
Computer Science > Machine Learning arXiv:2602.15879 (cs) [Submitted on 3 Feb 2026] Title:BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation Authors:Qing Yang, Yuhao Jiang, Rui Wang, Jipeng Guo, Yejiang Wang, Xinghe Cheng, Zezheng Wu, Jiapu Wang, Jingwei Zhang View a PDF of the paper titled BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation, by Qing Yang and 8 other authors View PDF HTML (experimental) 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 join...
