ERIC Number: EJ1431386
Record Type: Journal
Publication Date: 2019
Pages: 11
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2056-7936
Available Date: N/A
Data-Driven Unsupervised Clustering of Online Learner Behaviour
Robert L. Peach; Sophia N. Yaliraki; David Lefevre; Mauricio Barahona
npj Science of Learning, v4 Article 14 2019
The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pair-wise similarity between time-series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high-performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional data sets: a different cohort of the same course, and time-series of different format from another university.
Descriptors: Learning Analytics, Web Based Instruction, Online Courses, Learner Engagement, Algorithms, Identification, Data Analysis, Behavior Patterns, Task Analysis, Comparative Analysis, Academic Achievement, Business Schools, Graduate Students, Foreign Countries
Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United Kingdom (London)
Grant or Contract Numbers: N/A
Author Affiliations: N/A