Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Arguel, A; Lockyer, L; Lipp, OV; Lodge, JM; Kennedy, G

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Arguel, A Lockyer, L

Lipp, OV Lodge, JM Kennedy, G

Permalink

Publication Type:: Journal Article
Citation:: Journal of Educational Computing Research, 2017, 55 (4), pp. 526 - 551
Issue Date:: 2017-07-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted manuscriptAdobe PDF (1.02 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Arguel, A	en_US
dc.contributor.author	Lockyer, L https://orcid.org/0000-0003-1517-2342	en_US
dc.contributor.author	Lipp, OV	en_US
dc.contributor.author	Lodge, JM	en_US
dc.contributor.author	Kennedy, G	en_US
dc.date.available	2020-05-25T19:11:24Z
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Journal of Educational Computing Research, 2017, 55 (4), pp. 526 - 551	en_US
dc.identifier.issn	0735-6331	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124968
dc.description.abstract	© SAGE Publications. Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed.	en_US
dc.relation.ispartof	Journal of Educational Computing Research	en_US
dc.relation.isbasedon	10.1177/0735633116674732	en_US
dc.subject.classification	Education	en_US
dc.title	Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	55	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	1303 Specialist Studies in Education	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	55	en_US

Abstract:

© SAGE Publications. Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124968