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

Gibson, Andrew & Kitto, Kirsty (2015) Analysing reflective text for learning analytics : an approach using anomaly recontextualisation. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, Association for Computing Machinery, Poughkeepsie, New York, USA, pp. 275-279.

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Reflective writing is an important learning task to help foster reflective 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 recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

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ID Code: 81674
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Reflective Text, Learning Analytics, Affective Computing, Machine Learning, HERN
DOI: 10.1145/2723576.2723635
ISBN: 978-1-4503-3417-4
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Andrew Gibson and Kirsty Kitto
Copyright Statement: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Deposited On: 09 Feb 2015 01:31
Last Modified: 24 Jun 2015 01:45

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