Analysis and comparison of learners’ epistemic networks based on interaction content in connectivist learning

Abstract

ABSTRACT As an innovative form of course, connectivist massive open online courses (cMOOCs) have attracted widespread participation. Learners in cMOOCs achieve cognitive network development through instructional interactions, but the underlying laws of this development are not yet fully understood. This study aims to contribute to this exploration by focusing on the analysis and comparison of cognitive networks, as represented by interaction content, among different types of learners. It established a cognitive encoding framework, encoded learners’ interaction content, classified learners based on learning outcomes and engagement durations, and applied epistemic network analysis and statistical analysis to data from the first Chinese cMOOC. The findings indicate that cMOOC learners can be classified into four categories: successful long-term learners, failed long-term learners, successful short-term learners and failed short-term learners. Learners with different outcomes exhibit varying cognitive networks, with certificated learners showing more complex structures. Learners with different time investments do not exhibit differences in their cognitive networks. Successful short-term learners, demonstrating more complex cognitive networks, tend to rush to complete assignments. This study contributes to the field by providing recommendations for promoting cognitive development of cMOOC learners. These insights are valuable for instructional design, learners’ cognitive assessment and learning interventions for future cMOOCs.