An Integrated LDA-QFD Approach for Improving Online Course Quality Based on Learners' Reviews

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Author(s): Rui Wang (Jiangxi University of Science and Technology, China), Haili Ling (Jiangxi University of Science and Technology, China), Jie Chen (Jiangxi University of Science and Technology, China)and Huijuan Fu (Jiangxi University of Science and Technology, China)
Copyright: 2025
Volume: 23
Issue: 1
Pages: 24
Source title: International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.371203

Keywords: Distance Education / Education / Educational Technologies / Information Science Reference

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Abstract

This study adopted the Latent Dirichlet Allocation (LDA) to extract learners' needs based on 70,145 reviews from online course designed for software design and development in China and then applied Quality Function Deployment (QFD) to map learners' differentiated needs into quality attributes. Taking national first-class courses as the benchmarking object to identify the key quality attributes expected from massive open online courses (MOOCs), the findings reveal that course video, exercise, teaching schedule, and presentation of course material are pivotal factors in the enhancement of online course quality. Among these, presentation of course material and teaching schedule are identified as priority factors of quality improvement, whereas course video and exercise are recognized as supplementary factors. The findings of this research provide effective guidance for MOOC educators to improve course quality.

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An Integrated LDA-QFD Approach for Improving Online Course Quality Based on Learners' Reviews

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