Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. The aim of this work was to assess the potential of social network analysis (SNA) for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance.
Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis.
By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators.
When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user’s position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student’s position and role in information relay in online case discussions, combined with the strength of that student’s network (social capital), can be used as predictors of performance in relevant settings.
By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students’ and teachers’ interactions that can be valuable in guiding teachers, improve students’ engagement, and contribute to learning analytics insights.
Read the full article on collaborative learning by Mohammed Saqr, Uno Fors, and Matti Tedre at BMC Medical Education
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