Analysing reflective text for learning analytics: An approach using anomaly recontextualisation

Gibson, A; Kitto, K

Analysing reflective text for learning analytics: An approach using anomaly recontextualisation

Gibson, A

Kitto, K

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Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2015, 16-20-March-2015 pp. 275 - 279
Issue Date:: 2015-03-16

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Field	Value	Language
dc.contributor.author	Gibson, A https://orcid.org/0000-0003-4619-6515	en_US
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121	en_US
dc.date.issued	2015-03-16	en_US
dc.identifier.citation	ACM International Conference Proceeding Series, 2015, 16-20-March-2015 pp. 275 - 279	en_US
dc.identifier.isbn	9781450334174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120379
dc.description.abstract	Reective writing is an important learning task to help foster reective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontex-tualisation of anomalous features within reective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Re- contextualisation process for Learning Analytics.	en_US
dc.relation.ispartof	ACM International Conference Proceeding Series	en_US
dc.relation.isbasedon	10.1145/2723576.2723635	en_US
dc.title	Analysing reflective text for learning analytics: An approach using anomaly recontextualisation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	16-20-March-2015	en_US
utslib.for	130306 Educational Technology and Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	16-20-March-2015	en_US

Abstract:

Reective writing is an important learning task to help foster reective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontex-tualisation of anomalous features within reective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Re- contextualisation process for Learning Analytics.

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

http://hdl.handle.net/10453/120379