Using social network analysis to understand online Problem-Based Learning and predict performance

• Published in: PloS one Vol. 13; no. 9; p. e0203590
• Main Authors: Saqr, Mohammed, Fors, Uno, Nouri, Jalal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.09.2018; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Collaborative learning; Computer and Information Sciences; Information Society; informationssamhället; Interaction analysis; Learning analytics; Medical education; People and Places; Physical Sciences; Problem based learning; Regression Analysis; Research and Analysis Methods; Social network analysis; Social Networking; Social networks; Social Sciences
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0203590

Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics, and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders. Besides, it can facilitate data-driven support services for students. This study included four courses at Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualization, correlation tests as well as predictive and explanatory regression models. Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students' centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with reasonable reliability, which is an obvious opportunity for intervention and support.