Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis

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...