Affecting Off-Task Behaviour: How Affect-aware Feedback Can Improve Student Learning Beate Grawemeyer Manolis Mavrikis Wayne Holmes London Knowledge Lab London Knowledge Lab London Knowledge Lab Birkbeck, University of London UCL Institute of Education UCL Institute of Education London, UK University College London, UK University College London, UK
[email protected] [email protected] [email protected] Sergio Gutierrez-Santos Michael Wiedmann Nikol Rummel London Knowledge Lab Institute of Educational Institute of Educational Birkbeck, University of London Research Research London, UK Ruhr-Universität Bochum, DE Ruhr-Universität Bochum, DE
[email protected] [email protected] [email protected] ABSTRACT Keywords This paper describes the development and evaluation of an Affect, Feedback, Exploratory Learning Environments 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 1. INTRODUCTION student’s affective state, which is deduced both from speech The aim of our research is to provide intelligent support to and interaction. The affect prediction is used to determine students, taking into account their affective states in order which type of feedback is provided and how that feedback to enhance their learning experience and performance. is presented (interruptive or non-interruptive). The system It is well understood that affect interacts with and in- includes two Bayesian networks that were trained with data fluences the learning process [19, 10, 3]. While positive gathered in a series of ecologically-valid Wizard-of-Oz stud- affective states (such as surprise, satisfaction or curiosity) ies, where the effect of the type of feedback and the presen- contribute towards learning, negative ones (including frus- tation of feedback on students’ affective states was investi- tration or disillusionment at realising misconceptions) can gated.