A social network analysis of college students’ online learning during the epidemic era: A triadic reciprocal determinism perspective

• Published in: Heliyon 2024-03, Vol.10 (6), p.e28107-e28107, Article e28107
• Main Authors: Chai, Jun, Ye, Jian-Hong
• Format: Article
• Language: English
• Subjects: Data science applications in education; Distance education; Learning experience; Online learning; Teaching/learning strategies
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e28107

The way in which college students learn online has dramatically altered due to the COVID-19 pandemic. Using the triadic reciprocal determinism (TRD) theory, this study aimed to identify the key factors influencing college students' online learning experience through sentiment analysis, text mining, and social network analysis (SNA). Macro- and micro-level parsing was conducted on the SNA model, which was divided into core, mantle, and shell layers to determine the most influential factors in the core layer. This study found that learners' personal factors, learning behaviors, and related elements in the online learning environment significantly influenced the learning outcomes of college students enrolled in online courses. Additionally, this study explored the distribution of SNA model elements in the mantle and peripheral shell layers, which also impact the online learning experience of college students. Overall, this study provides a comprehensive overview of the various factors affecting college students' online learning experience, and highlights the importance of considering these factors when designing online learning environments for college students. •Distance learning is extremely important and is now as important as face-to-face learning.•Online learning can provide support for students and schools, and can create unique opportunities under emergency management.•The big data bibliometric approach involves synthesizing and analyzing existing literature.•Social network analysis focuses on network structure and topological features.•The SNA model is divided into core, mantle and crust layers.