NR4DER: Neural Re-ranking for Diversified Exercise Recommendation

2025-06-01

AI Summary

To address the challenges of heterogeneous learning pacing and low student engagement in MOOC platforms—which undermine the accuracy and diversity of exercise recommendations—this paper proposes NR4DER, a Neural Re-ranking Framework for Differentiated Exercise Recommendation. NR4DER innovatively integrates multi-layer LSTM-based behavioral filtering, sequence-augmented modeling (specifically designed for inactive learners), personalized difficulty alignment, and diversity-aware neural re-ranking, thereby jointly optimizing recommendation accuracy and adaptability to individual learning rhythms. Extensive experiments on multiple real-world MOOC datasets demonstrate that NR4DER significantly outperforms state-of-the-art baselines: it improves recommendation accuracy by 12.7% and diversity metrics by 23.4%. The framework thus effectively supports differentiated and adaptive learning pathways.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.

Problem

• High dropout rates in existing exercise recommendation methods
• Inability to match diverse student learning paces
• Limited accuracy and diversity in exercise recommendations

Innovation

• mLSTM model for exercise filtering
• Sequence enhancement for inactive students
• Neural re-ranking for diverse recommendations