Enhancing Learning Path Recommendation via Multi-task Learning

• URL: http://arxiv.org/abs/2507.05295v1
• Date: Sat, 05 Jul 2025 21:16:02 GMT
• Title: Enhancing Learning Path Recommendation via Multi-task Learning
• Authors: Afsana Nasrin, Lijun Qian, Pamela Obiomon, Xishuang Dong,
• Abstract summary: This paper proposes a multi-task LSTM model that enhances learning path recommendation by leveraging shared information across tasks.<n> Experiments on the ASSIST09 dataset show that the proposed model significantly outperforms baseline methods for the learning path recommendation.
• Score: 1.392448435105643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing [54.44838681588145]
  ExRec is a framework for personalized exercise recommendation with semantically-grounded knowledge tracing.<n>We show that ExRec generalizes robustly to new, unseen questions and that it produces interpretable student learning trajectories.
  arXiv  Detail & Related papers  (2025-07-15T07:54:04Z)
• Education-Oriented Graph Retrieval-Augmented Generation for Learning Path Recommendation [56.37740554448673]
  We propose Discrimination Learning Enhances Learning Path Recommendation (DLELP) to enhance learning path recommendations.<n>We introduce a knowledge concept structure graph generation module that adaptively constructs knowledge concept structure graphs for different educational datasets.<n>We then propose a Discrimination Learning-driven Reinforcement Learning framework, which mitigates the issue of blocked learning paths.
  arXiv  Detail & Related papers  (2025-06-27T15:15:42Z)
• Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning [57.28766250993726]
  This work explores adapting to dynamic user interests without any model updates. Existing Large Language Model (LLM)-based recommenders often lose the in-context learning ability during recommendation tuning. We propose RecICL, which customizes recommendation-specific in-context learning for real-time recommendations.
  arXiv  Detail & Related papers  (2024-10-30T15:48:36Z)
• Personalized Multi-task Training for Recommender System [80.23030752707916]
  PMTRec is the first personalized multi-task learning algorithm to obtain comprehensive user/item embeddings from various information sources. Our contributions open new avenues for advancing personalized multi-task training in recommender systems.
  arXiv  Detail & Related papers  (2024-07-31T06:27:06Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)
• Towards a General Pre-training Framework for Adaptive Learning in MOOCs [37.570119583573955]
  We propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. We find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors.
  arXiv  Detail & Related papers  (2022-07-18T13:18:39Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.