Feature based sentiment analysis for evaluating the mobile pedagogical affordances of apps

Bano, M; Zowghi, D; Kearney, M

Feature based sentiment analysis for evaluating the mobile pedagogical affordances of apps

Bano, M

Zowghi, D

Kearney, M

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Publication Type:: Conference Proceeding
Citation:: IFIP Advances in Information and Communication Technology, 2017, 515 pp. 281 - 291
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Bano, M https://orcid.org/0000-0002-1447-9521	en_US
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155	en_US
dc.contributor.author	Kearney, M https://orcid.org/0000-0002-6396-0418	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	IFIP Advances in Information and Communication Technology, 2017, 515 pp. 281 - 291	en_US
dc.identifier.isbn	9783319743097	en_US
dc.identifier.issn	1868-4238	en_US
dc.identifier.uri	http://hdl.handle.net/10453/96586
dc.description.abstract	© 2017, IFIP International Federation for Information Processing. The launch of millions of apps has made it challenging for teachers to select the most suitable educational app to support students’ learning. Several evaluation frameworks have been proposed in the research literature to assist teachers in selecting the right apps for their needs. This paper presents preliminary results of an innovative technique for evaluating educational mobile apps by analysing the feedback of past app users through the lens of a mobile pedagogical perspective. We have utilized a sentiment analysis tool to assess the opinions of the app users through the lens of the criteria offered by a rigorous mobile learning pedagogical framework highlighting the learners’ experience of Personalization, Authenticity and Collaboration (iPAC). The investigation has provided initial confirmation of the powerful utility of the feature based sentiment analysis technique for evaluating the mobile pedagogical affordances of learning apps.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101214
dc.relation.ispartof	IFIP Advances in Information and Communication Technology	en_US
dc.relation.isbasedon	10.1007/978-3-319-74310-3_30	en_US
dc.title	Feature based sentiment analysis for evaluating the mobile pedagogical affordances of apps	en_US
dc.type	Conference Proceeding
utslib.citation.volume	515	en_US
utslib.for	130306 Educational Technology and Computing	en_US
utslib.for	0806 Information Systems	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/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/FASS Faculty Administration
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	515	en_US

Abstract:

© 2017, IFIP International Federation for Information Processing. The launch of millions of apps has made it challenging for teachers to select the most suitable educational app to support students’ learning. Several evaluation frameworks have been proposed in the research literature to assist teachers in selecting the right apps for their needs. This paper presents preliminary results of an innovative technique for evaluating educational mobile apps by analysing the feedback of past app users through the lens of a mobile pedagogical perspective. We have utilized a sentiment analysis tool to assess the opinions of the app users through the lens of the criteria offered by a rigorous mobile learning pedagogical framework highlighting the learners’ experience of Personalization, Authenticity and Collaboration (iPAC). The investigation has provided initial confirmation of the powerful utility of the feature based sentiment analysis technique for evaluating the mobile pedagogical affordances of learning apps.

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http://hdl.handle.net/10453/96586