Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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: 3
2018 (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
Keyword [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
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: 2018-03-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Fors, Uno
By organisation
Department of Computer and Systems Sciences
In the same journal
BMC Medical Education
Educational SciencesComputer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf