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Integrating Text Mining With DEA-Malmquist Index for Evaluating First-Class Online Course Development Efficiency
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Author(s): Rui Wang (Jiangxi University of Science and Technology, China), Jiani Ren (Jiangxi University of Science and Technology, China), Jie Chen (Jiangxi University of Science and Technology, China)and Chuwei Zhang (The University of Manchester, UK)
Copyright: 2026
Volume: 24
Issue: 1
Pages: 19
Source title:
International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.397920
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Abstract
The dynamic evaluation of development performance in nationally recognized first-class courses holds broad implications for improving the quality of online education. This study examines 512 nationally recognized first-class undergraduate online courses on iCourse platform, framing them as an input–output system and drawing on learner-generated online review data. By integrating text mining techniques with data envelopment analysis and the Malmquist index, the study dynamically assesses their development efficiency from 2019 to 2024. The findings indicate that (1) first-class course development maintained relatively high overall efficiency, although sustainability was constrained by limited post-approval investment; (2) course performance displayed a decline–rebound trajectory, primarily driven by teaching innovation; and (3) significant disciplinary differences emerged, with natural science courses exhibiting greater volatility but stronger resilience, whereas humanities and social science courses showed milder fluctuations and experienced late-stage innovation fatigue and resource aging.
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