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How the study of online collaborative learning can guide teachers and predict students' performance in a medical course
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Qassim University, Kingdom of Saudi Arabia.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Number of Authors: 32018 (English)In: BMC Medical Education, ISSN 1472-6920, E-ISSN 1472-6920, Vol. 18, article id 24Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 18, article id 24
Keywords [en]
Collaborative learning, E-learning, Social network analysis, Computer-supported collaborative learning, Blended learning, Clinical, Case discussions, Learning analytics
National Category
Educational Sciences Computer and Information Sciences
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-153779DOI: 10.1186/s12909-018-1126-1ISI: 000424454500001PubMedID: 29409481OAI: oai:DiVA.org:su-153779DiVA, id: diva2:1192123
Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-09-24Bibliographically approved
In thesis
1. Using Learning Analytics to Understand and Support Collaborative Learning
Open this publication in new window or tab >>Using Learning Analytics to Understand and Support Collaborative Learning
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. The prime objective of this thesis is to investigate the potential of learning analytics in understanding learning patterns and learners’ behavior in collaborative learning environments with the premise of improving teaching and learning. More specifically, the research questions comprise: How can learning analytics and social network analysis (SNA) reliably predict students’ performance using contextual, theory-based indicators, and how can social network analysis be used to analyze online collaborative learning, guide a data-driven intervention, and evaluate it. The research methods followed a structured process of data collection, preparation, exploration, and analysis. Students’ data were collected from the online learning management system using custom plugins and database queries. Data from different sources were assembled and verified, and corrupted records were eliminated. Descriptive statistics and visualizations were performed to summarize the data, plot variables’ distributions, and detect interesting patterns. Exploratory statistical analysis was conducted to explore trends and potential predictors, and to guide the selection of analysis methods. Using insights from these steps, different statistical and machine learning methods were applied to analyze the data. The results indicate that a reasonable number of underachieving students could be predicted early using self-regulation, engagement, and collaborative learning indicators. Visualizing collaborative learning interactions using SNA offered an easy-to-interpret overview of the status of collaboration, and mapped the roles played by teachers and students. SNA-based monitoring helped improve collaborative learning through a data-driven intervention. The combination of SNA visualization and mathematical analysis of students’ position, connectedness, and role in collaboration was found to help predict students’ performance with reasonable accuracy. The early prediction of performance offers a clear opportunity for the implementation of effective remedial strategies and facilitates improvements in learning. Furthermore, using SNA to monitor and improve collaborative learning could contribute to better learning and teaching.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2018. p. 143
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 18-011
Keywords
Learning analytics, Social Network Analysis, Collaborative Learning, Medical Education, Interaction Analysis, Machine Learning
National Category
Computer Sciences
Research subject
Information Society
Identifiers
urn:nbn:se:su:diva-159479 (URN)978-91-7797-440-6 (ISBN)978-91-7797-441-3 (ISBN)
Public defence
2018-10-22, L70, NOD-huset Borgarfjordsgatan 12, Kista, 09:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Accepted.

Available from: 2018-09-27 Created: 2018-09-05 Last updated: 2018-09-27Bibliographically approved

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