Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 2023, Wuhan, China

Research Article

Research Hotspots and Trend Analysis of MOOC Learner Loyalty Based on Data Mining

Download141 downloads
  • @INPROCEEDINGS{10.4108/eai.8-9-2023.2340191,
        author={Yanrong  Huang and Rui  Wang},
        title={Research Hotspots and Trend Analysis of MOOC Learner Loyalty Based on Data Mining},
        proceedings={Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8--10, 2023, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICMEIM},
        year={2023},
        month={11},
        keywords={mooc; learner loyalty; trend analysis; data mining},
        doi={10.4108/eai.8-9-2023.2340191}
    }
    
  • Yanrong Huang
    Rui Wang
    Year: 2023
    Research Hotspots and Trend Analysis of MOOC Learner Loyalty Based on Data Mining
    ICMEIM
    EAI
    DOI: 10.4108/eai.8-9-2023.2340191
Yanrong Huang1,*, Rui Wang2
  • 1: Zhejiang University of Water Resources and Electric Power
  • 2: Jiangxi University of Science and Technology
*Contact email: hyanrong@whu.edu.cn

Abstract

Massive open online courses (MOOC) have been widely used in colleges and universities worldwide. During the COVID-19 epidemic, MOOC has become the primary online teaching mode in education system. The phenomenon of "high registration rate" coexisting with "low return rate" and "high dropout rate" on the MOOC platform has attracted scholars' attention to improving MOOC learner loyalty. This paper takes 596 articles of MOOC learner loyalty research from 2013 to 2023 in the Web of Science database as samples, uses the CiteSpace Knowledge graph to conduct literature mining and visual analysis, and proposes the research status, research hotspots, and trends. Research result shows that MOOC learner loyalty research has formed nine clusters, including "empirical investment," "learning experience," "online learner," "self-directed learning," "Kenyan cloud school," "building capacity," "interpretable model," "discovering MOOC learner motivation," and "preference-based group." With the development of computer technology, teaching, and learning methods innovation, interdisciplinary integration research to improve MOOC learner loyalty has become a new trend.