Grawemeyer, Beate and Mavrikis, M. and Holmes, W. and Gutierrez-Santos, Sergio (2015) Adapting feedback types according to students’ affective states. In: Conati, C. and Heffernan, N. and Mitrovic, A. and Verdejo, M. (eds.) Artificial Intelligence in Education. Lecture Notes in Computer Science 9112. New York, U.S.: Springer, pp. 586-590. ISBN 9783319197722.
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Abstract
Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes the development of an affective state reasoner that is able to adapt the feedback type according to students’ affective states in order to evoke positive affective states and as such improve their learning experience. The reasoner relies on a dynamic Bayesian network trained with data gathered in a series of ecologically valid Wizard-of-Oz studies, where the effect of feedback on students’ affective states was investigated.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | 17th International Conference, AIED 2015, Madrid, Spain, June 22-26, 2015. Proceedings. The final publication is available at Springer via the link above. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 05 Jul 2016 10:10 |
| Last Modified: | 09 Aug 2023 12:38 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/15679 |
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