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BMC Med Educ. 2018 Feb 6;18(1):24. doi: 10.1186/s12909-018-1126-1.

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

Author information

1
College of Medicine, Qassim University, Qassim, PO Box: 6655, 51452, Kingdom of Saudi Arabia. Saqr@qumed.edu.sa.
2
Department of Computer and System Sciences (DSV), Stockholm University, Borgarfjordsgatan 12, PO Box 7003, SE-164 07, Kista, Sweden. Saqr@qumed.edu.sa.
3
Department of Computer and System Sciences (DSV), Stockholm University, Borgarfjordsgatan 12, PO Box 7003, SE-164 07, Kista, Sweden.
4
School of Computing, University of Eastern Finland, PO Box 111, Joensuu, Finland.

Abstract

BACKGROUND:

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of 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.

METHODS:

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. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance.

RESULTS:

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.

CONCLUSION:

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.

KEYWORDS:

Blended learning; Case discussions; Clinical; Collaborative learning; Computer-supported collaborative learning; E-learning; Learning analytics; Social network analysis

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