Learner Engagement Examination Via Computer Usage Behaviors

Yu, K; Xu, J; Ou, Y; Luo, L; Chen, F

Learner Engagement Examination Via Computer Usage Behaviors

Yu, K

Xu, J Ou, Y

Luo, L Chen, F

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Humanity Driven AI, 2022, pp. 241-253
Issue Date:: 2022-12-02

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Field	Value	Language
dc.contributor.author	Yu, K https://orcid.org/0000-0001-5138-6749
dc.contributor.author	Xu, J
dc.contributor.author	Ou, Y https://orcid.org/0000-0001-5922-9406
dc.contributor.author	Luo, L
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2023-02-01T22:16:04Z
dc.date.available	2023-02-01T22:16:04Z
dc.date.issued	2022-12-02
dc.identifier.citation	Humanity Driven AI, 2022, pp. 241-253
dc.identifier.isbn	978-3-030-72187-9
dc.identifier.uri	http://hdl.handle.net/10453/165801
dc.description.abstract	Over the 40 years since its debut, computer-aided teaching and learning have grown from novelty to near pervasive, with a host of dynamic interactive digital systems underpinning many aspects of the modern student experience. While potentially transformative, the wholesale integration of computing systems into contemporary schooling has not yet delivered solutions to enduring problems in the education sector: so far few techniques enable the tracking of engagement levels in real time. This chapter addresses a new method to examine the engagement of learners by utilizing webcam and mouse information. Based on a study conducted with learners undertaking an online course, a dataset was collected including the mouse and keyboard usages, face images and screenshots. The learner’s engagement level was inferred via the eye gaze direction and mouse movements. The analytics suggests that the learning behavior data, which potentially reflects the learner’s engagement, could be an indicator to the learning outcome of individual tasks. We have also identified a few practical issues faced by real-time engagement examination systems. It provides a novel means of examining engagement quantitatively in online learning and brings new insights into innovative education solutions.
dc.format.extent	15
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Humanity Driven AI
dc.relation.isbasedon	10.1007/978-3-030-72188-6_12
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Learner Engagement Examination Via Computer Usage Behaviors
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-01T22:16:03Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
dc.location	Switzerland

Abstract:

Over the 40 years since its debut, computer-aided teaching and learning have grown from novelty to near pervasive, with a host of dynamic interactive digital systems underpinning many aspects of the modern student experience. While potentially transformative, the wholesale integration of computing systems into contemporary schooling has not yet delivered solutions to enduring problems in the education sector: so far few techniques enable the tracking of engagement levels in real time. This chapter addresses a new method to examine the engagement of learners by utilizing webcam and mouse information. Based on a study conducted with learners undertaking an online course, a dataset was collected including the mouse and keyboard usages, face images and screenshots. The learner’s engagement level was inferred via the eye gaze direction and mouse movements. The analytics suggests that the learning behavior data, which potentially reflects the learner’s engagement, could be an indicator to the learning outcome of individual tasks. We have also identified a few practical issues faced by real-time engagement examination systems. It provides a novel means of examining engagement quantitatively in online learning and brings new insights into innovative education solutions.

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http://hdl.handle.net/10453/165801