Improving effectiveness of online learning for higher education students during the COVID-19 pandemic - Li, Xuelan; Pei, Zhiqiang (Frontiers in Psychology, 2023)
Under the COVID-19, online learning has become one of the important ways for higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has been the focus of education research, and the role of "online learning community" cannot be ignored. In the context of the In-ternet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use clustering algorithm to identify the nature of different learning style groups; (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire, and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of online learning community. We take business administration majors and students in universities as an example. The results show that: (1) As a machine learning method, clustering algorithm method is superior to random construction method in identifying different learning style groups; (2) Our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education.