Affecting Off-Task Behaviour: How Affect-aware feedback can improve student learning

Beate Grawemeyer, Manolis Mavrikis, Wayne Holmes, Gutierrez Santos Sergio, Michael Wiedmann, Nikol Rummel

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

14 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact
Subtitle of host publicationConvergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages104-113
Number of pages10
Volume25-29-April-2016
ISBN (Electronic)9781450341905
DOIs
Publication statusPublished - 25 Apr 2016
Externally publishedYes
Event6th International Conference on Learning Analytics and Knowledge, LAK 2016 - Edinburgh, United Kingdom
Duration: 25 Apr 201629 Apr 2016

Conference

Conference6th International Conference on Learning Analytics and Knowledge, LAK 2016
CountryUnited Kingdom
CityEdinburgh
Period25/04/1629/04/16

Keywords

  • Affect
  • Exploratory learning environments
  • Feedback

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Grawemeyer, B., Mavrikis, M., Holmes, W., Sergio, G. S., Wiedmann, M., & Rummel, N. (2016). Affecting Off-Task Behaviour: How Affect-aware feedback can improve student learning. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (Vol. 25-29-April-2016, pp. 104-113). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883936