Open AccessA Systematic Review of Questionnaire-Based Quantitative Research on MOOCs
Author(s) -
Lu Ming,
Tianyi Cui,
Zhenyu Huang,
Zhao Hong,
Tao Li,
Kai Wang
Publication year - 2021
Publication title -
international review of research in open and distance learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.436
H-Index - 68
ISSN - 1492-3831
DOI - 10.19173/irrodl.v22i2.5208
Subject(s) - continuance , citation , computer science , citation index , descriptive statistics , dropout (neural networks) , extant taxon , psychology , data science , knowledge management , world wide web , statistics , mathematics , social psychology , machine learning , evolutionary biology , biology
Massive open online courses (MOOCs) have attracted much interest from educational researchers and practitioners around the world. There has been an increase in empirical studies about MOOCs in recent years, most of which used questionnaire surveys and quantitative methods to collect and analyze data. This study explored the research topics and paradigms of questionnaire-based quantitative research on MOOCs by reviewing 126 articles available in the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) databases from January 2015 to August 2020. This comprehensive overview showed that: (a) the top three MOOC research topics were the factors influencing learners’ performance, dropout rates and continuance intention to use MOOCs, and assessing MOOCs; (b) for these three topics, many studies designed questionnaires by adding new factors or adjustments to extant theoretical models or survey instruments; and (c) most researchers used descriptive statistics to analyze data, followed by the structural equation model, and reliability and validity analysis. This study elaborated on the relationship of research topics and key factors in the research models by building factors-goals (F-G) graphs. Finally, we proposed some directions and recommendations for future research on MOOCs.