用多任务学习提升个性化学习路径推荐效果
Enhancing Learning Path Recommendation via Multi-task Learning
- 将学习路径推荐建模为序列到序列问题，共享知识追踪特征
- 在ASSIST09数据集上准确率显著优于基线方法
- 适合教育科技、智能辅导系统研发者参考
个性化学习是以学生为中心的教育模式，通过调整内容、进度和评估来满足个体需求。学习路径推荐是实现个性化学习的关键技术，可依次推荐讲座、练习等学习资源。深度学习尤其是深度强化学习的发展使此类推荐更高效。本文提出一种多任务LSTM模型，通过跨任务共享信息增强学习路径推荐。该方法将学习路径推荐重构为序列到序列预测问题，从学习者历史交互中生成个性化路径。模型采用共享LSTM层捕捉学习路径推荐与深度知识追踪的共同特征，并为每个目标设置专用LSTM层。为避免重复推荐，引入非重复损失函数惩罚路径中的重复项。在ASSIST09数据集上的实验表明，所提模型在学习路径推荐任务上显著优于基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。