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

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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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.
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