Modelling Affect in Learning Environments - Motivation and Methods

Emotions have a functional relevance to learning and achievement. Not surprisingly then, affective diagnoses are an important aspect of expert human mentoring. Computerbased learning environments aim to model such social dynamics to make learning with computers more immersive, engaging and hence, more effective. This paper draws on the recent surge of interest in studying emotions in learning, highlights available techniques for measuring emotions and surveys recent efforts to automatically measure emotional experience in learning environments. Finally, a contextsensitive dataset is used to develop an automatic system for modeling six pertinent emotions. This paper attempts to bring together the motivation, methodological issues, and modeling approaches for affect inference in learning environments in order to contribute to an understanding of the problem and the current state-of-art. Keywords - Emotion, Affective Computing, Computer-based Learning

• Type: Journal
• Year: 2010
• Where: ICALT
• Authors: Shazia Afzal, Peter Robinson
• Keywords: Affective Diagnoses, Computerbased Learning, Functional Relevance, ICALT 2010, Machine Learning