Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis
ISSN: 1066-2243
Article publication date: 24 June 2021
Issue publication date: 15 March 2022
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.
Keywords
Acknowledgements
The authors appreciate the anonymous referees and the Editorial Office for their valuable comments and suggestions. This work is supported by the Major Program of the National Natural Science Foundation of China (91846201), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), the National Natural Science Foundation of China (71722010, 91746302, 71872060) and the Teaching Research Project of Anhui Province (2020jyxm1502).
Citation
Fan, J., Jiang, Y., Liu, Y. and Zhou, Y. (2022), "Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis", Internet Research, Vol. 32 No. 2, pp. 588-605. https://doi.org/10.1108/INTR-08-2020-0477
Publisher
:Emerald Publishing Limited
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