Affective learning: improving engagement and enhancing learning with affect-aware feedback

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

Research output: Contribution to journalArticle

24 Citations (Scopus)
9 Downloads (Pure)

Abstract

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

Original languageEnglish
Pages (from-to)119-158
Number of pages40
JournalUser Modeling and User-Adapted Interaction
Volume27
Issue number1
Early online date7 Feb 2017
DOIs
Publication statusPublished - 1 Mar 2017
Externally publishedYes

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-017-9188-z

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright
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Keywords

  • Affective learning
  • Bayesian networks
  • Formative feedback
  • Learner modelling

ASJC Scopus subject areas

  • Education
  • Human-Computer Interaction
  • Computer Science Applications

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