Grawemeyer, B;
Mavrikis, M;
Holmes, W;
Sergio, GS;
Wiedmann, M;
Rummel, N;
(2016)
Affecting Off-Task Behaviour: How Affect-aware feedback can improve student learning.
In: Gašević, D and Lynch, G and Dawson, S and Drachsler, H and Penstein Rosé, C, (eds.)
LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge.
(pp. pp. 104-113).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.
| Type: | Proceedings paper |
|---|---|
| Title: | Affecting Off-Task Behaviour: How Affect-aware feedback can improve student learning |
| Event: | LAK '16: Sixth International Conference on Learning Analytics & Knowledge, 25-29 April 2016, Edinburgh, UK |
| ISBN-13: | 9781450341905 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/2883851.2883936 |
| Publisher version: | http://dx.doi.org/10.1145/2883851.2883936 |
| Language: | English |
| Additional information: | Copyright © 2016 ACM. |
| Keywords: | Affect, Feedback, Exploratory Learning Environments |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1513497 |
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