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Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor

Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor

Nik Thompson, Tanya Jane McGill
Copyright: © 2012 |Volume: 8 |Issue: 4 |Pages: 15
ISSN: 1550-1876|EISSN: 1550-1337|EISBN13: 9781466612563|DOI: 10.4018/jicte.2012100107
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MLA

Thompson, Nik, and Tanya Jane McGill. "Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor." IJICTE vol.8, no.4 2012: pp.75-89. http://doi.org/10.4018/jicte.2012100107

APA

Thompson, N. & McGill, T. J. (2012). Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor. International Journal of Information and Communication Technology Education (IJICTE), 8(4), 75-89. http://doi.org/10.4018/jicte.2012100107

Chicago

Thompson, Nik, and Tanya Jane McGill. "Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor," International Journal of Information and Communication Technology Education (IJICTE) 8, no.4: 75-89. http://doi.org/10.4018/jicte.2012100107

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Abstract

This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing.

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