Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis
Ju Fan, Yuanchun Jiang, Yezheng Liu, Yonghang Zhou
Abstract
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.