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Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students’ interactions and self-regulation
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.
2019 (English)In: International Journal of Technology Enhanced Learning, ISSN 1753-5255, E-ISSN 1753-5263, Vol. 11, no 4, p. 398-412Article in journal (Refereed) Published
Abstract [en]

In this learning analytics study, we attempt to understand the role of temporality measures for the prediction of academic performance. The study included four online courses over a full-year duration. Temporality was studied on daily, weekly, course-wise and year-wise. Visualising the activities has highlighted certain patterns. On the week level, early participation was a consistent predictor of high achievement. This finding was consistent from course to course and during most periods of the year. On course level, high achievers were also likely to participate early and consistently. With a focus on temporal measures, we were able to predict high achievers with reasonable accuracy in each course. These findings highlight the idea that temporality dimension is a significant source of information about learning patterns and has the potential to inform educators about students’ activities and to improve the accuracy and reproducibility of predicting students’ performance.

Place, publisher, year, edition, pages
2019. Vol. 11, no 4, p. 398-412
Keywords [en]
Learning analytics, Temporality, Time, Problem based learning, Collaborative learning, Social network analysis, Self-regulation
National Category
Computer Sciences Educational Sciences
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-159753DOI: 10.1504/IJTEL.2019.102549ISI: 000488979200004OAI: oai:DiVA.org:su-159753DiVA, id: diva2:1245425
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-11-11Bibliographically 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: 2020-05-11Bibliographically approved

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