With the increasing popularity of online courses, their quality has become a public concern. Based on the perspective of knowledge management, this study aims to identify comprehensive and granular quality factors of online courses and analyze the relationships between the factors.
Following the principles of the grounded theory, interpretive structural modeling and cross-impact matrix multiplication applied to classification (MICMAC) analysis methods, this research uses reviews and comments garnered from Zhihu, which is the most popular online question-and-answer community in China, to conduct the analysis.
Based on the text data, 50 factors that potentially affect the quality of online courses are obtained. The analysis identifies the hierarchical relationships and dependent correlations between the factors.
The research uses the knowledge transformation model to classify content elements according to their degree of descriptiveness and provides practical and effective suggestions for improving the quality of online courses.
