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Modelling Affect in Learning Environments Motivation and Methods
| Citations: | 3 - 0 self |
BibTeX
@MISC{Afzal_modellingaffect,
author = {Shazia Afzal and Peter Robinson},
title = {Modelling Affect in Learning Environments Motivation and Methods},
year = {}
}
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Abstract
Abstract — 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.
Keyphrases
learning environment motivation emotional experience recent surge social dynamic highlight available technique survey recent effort automatic system functional relevance affect inference environment aim important aspect pertinent emotion expert human mentoring current state-of-art contextsensitive dataset abstract emotion methodological issue affective diagnosis
