Purpose

Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.

Design/methodology/approach

The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.

Findings

The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.

Practical implications

The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.

Originality/value

This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

中文翻译：

---

可解释的 MOOC 推荐：用于个性化学习行为分析的多注意力网络

目的

课程推荐对于提高学习者满意度和降低大规模开放在线课程 (MOOC) 平台的辍学率非常重要。本研究旨在通过整合多个数据源，提出一种可解释的方法来分析学生的学习行为并推荐 MOOC。

设计/方法/方法

该研究提出了一种基于学习记录注意力、单词级复习注意力、句子级复习注意力和课程描述注意力的多注意力机制向学生推荐MOOCs的深度学习方法。所提出的模型使用真实世界的数据进行了验证，该数据由 6,628 名学生的 1,789 门课程和 65,155 条评论的学习记录组成。

发现

本研究的主要贡献是使用所提出的多注意力网络模型探索多种非结构化信息。它为分析学生的学习行为和进行个性化的 MOOC 推荐提供了一种可解释的策略。

实际影响

研究结果表明，MOOC 平台必须充分利用课程评论中隐含的信息来提取个性化的学习偏好。

原创性/价值

本研究首次尝试通过探索学生在课程评论中的偏好来推荐 MOOC。提出的多注意机制提高了 MOOC 推荐的可解释性。