Review paper
Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis
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...
Paper Details
Title
Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis
Published Date
Jun 24, 2021
Journal
Volume
32
Issue
2
Pages
588 - 605
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