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Affective learning: Improving engagement and enhancing learning with affect aware feedback

Venue: Online

This talk describes the development 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 the student's affective state. 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 talk reports results from a quasi-experimental evaluation, in realistic settings, that compared a version of iTalk2Learn that adapted feedback based on the 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 (non-affect condition). The results suggest that affect-aware support may contribute to reducing boredom and off-task behavior, and could have an effect on learning.

Contact name:

  • Beate Grawemeyer -

    Dr Beate Grawemeyer is a Lecturer in Computer Science and a research associate at GLEA at Coventry University. Before joining Coventry she was a postdoctoral researcher at Birkbeck and a member of the Birkbeck Knowledge Lab. Her research work is in the area of computer science / artificial intelligence and human computer interaction. She is particularly interested in learning environments which are able to support and adapt to the special characteristics of individuals with the aim of enhancing their learning experience.