Enhancing Learning Path Recommendation via Multi-task Learning

2025-07-05

AI Summary

Personalized learning path recommendation faces challenges stemming from high inter-user demand variability and excessive recommendation redundancy. To address these issues, this work formulates the task as a sequence-to-sequence prediction problem and proposes a multi-task learning framework that jointly optimizes learning path recommendation and deep knowledge tracing. The model employs a shared LSTM encoder with task-specific LSTM decoders and incorporates a non-repetition loss function to explicitly penalize repeated concept recommendations within a path, thereby enhancing both diversity and personalization. Experiments on the ASSIST09 dataset demonstrate that the proposed method significantly outperforms single-task baselines and existing multi-task approaches in recommendation accuracy (MRR@5, HR@5) and knowledge mastery prediction. These results validate the effectiveness of joint modeling and the non-repetition mechanism in supporting personalized learning.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

Abstract

Personalized learning is a student-centered educational approach that adapts content, pace, and assessment to meet each learner's unique needs. As the key technique to implement the personalized learning, learning path recommendation sequentially recommends personalized learning items such as lectures and exercises. Advances in deep learning, particularly deep reinforcement learning, have made modeling such recommendations more practical and effective. This paper proposes a multi-task LSTM model that enhances learning path recommendation by leveraging shared information across tasks. The approach reframes learning path recommendation as a sequence-to-sequence (Seq2Seq) prediction problem, generating personalized learning paths from a learner's historical interactions. The model uses a shared LSTM layer to capture common features for both learning path recommendation and deep knowledge tracing, along with task-specific LSTM layers for each objective. To avoid redundant recommendations, a non-repeat loss penalizes repeated items within the recommended learning path. Experiments on the ASSIST09 dataset show that the proposed model significantly outperforms baseline methods for the learning path recommendation.

Problem

• Enhancing personalized learning path recommendations using multi-task learning
• Modeling learning paths as sequence-to-sequence prediction with LSTM
• Reducing redundant recommendations via non-repeat loss mechanism

Innovation

• Multi-task LSTM model for learning path recommendation
• Seq2Seq prediction for personalized learning paths
• Non-repeat loss to avoid redundant recommendations