Understanding student emotions when completing assessment: technological, teacher and student perspectives

Hopwood, N; Palmer, TA; Koh, GA; Lai, MY; Dong, Y; Loch, S; Yu, K

Understanding student emotions when completing assessment: technological, teacher and student perspectives

Hopwood, N

Palmer, TA Koh, GA Lai, MY Dong, Y

Loch, S

Yu, K

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Publisher:: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: International Journal of Research and Method in Education, 2024, ahead-of-print, (ahead-of-print)
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Hopwood, N https://orcid.org/0000-0003-2149-5834
dc.contributor.author	Palmer, TA
dc.contributor.author	Koh, GA
dc.contributor.author	Lai, MY
dc.contributor.author	Dong, Y https://orcid.org/0000-0002-7995-9657
dc.contributor.author	Loch, S https://orcid.org/0000-0003-3214-0035
dc.contributor.author	Yu, K https://orcid.org/0000-0001-5138-6749
dc.date.accessioned	2024-10-30T22:54:10Z
dc.date.available	2024-10-30T22:54:10Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Research and Method in Education, 2024, ahead-of-print, (ahead-of-print)
dc.identifier.issn	1743-727X
dc.identifier.issn	1743-7288
dc.identifier.uri	http://hdl.handle.net/10453/181640
dc.description.abstract	Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in an online Data Science assessment task. Three patterns of engagement emerged from the interview data, namely whizz, worker and worrier. Related emotions for these were discerned in the real-time learning analytics data, informing interpretations of associations between emotional and other forms of engagement. Instead of displacing human insights, learning analytics used alongside student self-report and teacher professional insights augment our understanding of student emotions in a naturalistic school assessment setting.
dc.language	English
dc.publisher	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
dc.relation.ispartof	International Journal of Research and Method in Education
dc.relation.isbasedon	10.1080/1743727X.2024.2358792
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	13 Education
dc.subject.classification	Education
dc.subject.classification	39 Education
dc.title	Understanding student emotions when completing assessment: technological, teacher and student perspectives
dc.type	Journal Article
utslib.citation.volume	ahead-of-print
utslib.for	13 Education
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Arts and Social Sciences/School of International Studies and Education
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Research on Education in a Digital Society (CREDS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Research on Education in a Digital Society (CREDS)/Associate Member
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2024-10-30T22:54:07Z
pubs.issue	ahead-of-print
pubs.publication-status	Published
pubs.volume	ahead-of-print
utslib.citation.issue	ahead-of-print

Abstract:

Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in an online Data Science assessment task. Three patterns of engagement emerged from the interview data, namely whizz, worker and worrier. Related emotions for these were discerned in the real-time learning analytics data, informing interpretations of associations between emotional and other forms of engagement. Instead of displacing human insights, learning analytics used alongside student self-report and teacher professional insights augment our understanding of student emotions in a naturalistic school assessment setting.

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http://hdl.handle.net/10453/181640