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Data-driven unsupervised clustering of online learner behaviour

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Title: Data-driven unsupervised clustering of online learner behaviour
Authors: Peach, R
Yaliraki, S
Lefevre, D
Barahona, M
Item Type: Journal Article
Abstract: 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 pairwise 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 datasets: a different cohort of the same course, and time series of different format from another university.
Issue Date: 3-Sep-2019
Date of Acceptance: 17-Jul-2019
URI: http://hdl.handle.net/10044/1/72218
DOI: 10.1038/s41539-019-0054-0
ISSN: 2056-7936
Publisher: Nature Publishing Group
Journal / Book Title: npj Science of Learning
Volume: 4
Copyright Statement: © The Author(s) 2019. This article is licensed under a Creative CommonsAttribution 4.0 International License, which permits use, sharing,adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the CreativeCommons license, and indicate if changes were made. The images or other third partymaterial in this article are included in the article’s Creative Commons license, unlessindicated otherwise in a credit line to the material. If material is not included in thearticle’s Creative Commons license and your intended use is not permitted by statutoryregulation or exceeds the permitted use, you will need to obtain permission directlyfrom the copyright holder. To view a copy of this license, visithttp://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N014529/1
Keywords: Education
cs.SI
cs.SI
physics.data-an
Publication Status: Published
Article Number: ARTN 14
Appears in Collections:Imperial College Business School
Mathematics
Chemistry
Biological and Biophysical Chemistry
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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