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Using social network analysis to understand online Problem-Based Learning and predict performance
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5881-3109
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 9, article id e0203590Article in journal (Refereed) Published
Abstract [en]

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 in 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 visualizatization, 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 a high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with a reasonable reliability, which is an obvious opportunity for intervention and support.

Place, publisher, year, edition, pages
2018. Vol. 13, no 9, article id e0203590
Keywords [en]
Social network analysis, Problem based learning, Learning analytics, Medical education, Interaction analysis
National Category
Computer Sciences Educational Sciences
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-159751DOI: 10.1371/journal.pone.0203590ISI: 000445626400028OAI: oai:DiVA.org:su-159751DiVA, id: diva2:1245412
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-10-15Bibliographically 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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