Adapting feedback types according to students’ affective states

Beate Grawemeyer, Manolis Mavrikis, Wayne Holmes, Sergio Gutierrez-Santos

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

6 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
PublisherSpringer-Verlag London Ltd
Pages586-590
Number of pages5
Volume9112
ISBN (Print)9783319197722
DOIs
Publication statusPublished - 17 Jun 2015
Externally publishedYes
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: 22 Jun 201526 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Artificial Intelligence in Education, AIED 2015
CountrySpain
CityMadrid
Period22/06/1526/06/15

Keywords

  • Artificial intelligence
  • Bayesian networks
  • Education
  • Feedback

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Grawemeyer, B., Mavrikis, M., Holmes, W., & Gutierrez-Santos, S. (2015). Adapting feedback types according to students’ affective states. In Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (Vol. 9112, pp. 586-590). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer-Verlag London Ltd. https://doi.org/10.1007/978-3-319-19773-9_68