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Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

  • Jason M. Harley
  • Susanne P. Lajoie
  • Claude Frasson
  • Nathan C. Hall
Article

Abstract

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners’ experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic’s breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion-aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D’Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.

Keywords

Emotions Affect Emotion regulation Emotion-aware systems Advanced learning technologies Intelligent tutoring systems 

Notes

Acknowledgments

The research presented in this paper has been supported by a postdoctoral fellowship from the Fonds Québécois de recherche – Société et culture (FQRSC) awarded to the first author. This research has also been supported by funding from the Social Sciences and Humanities Research Council of Canada. The authors would like to thank Reinhard Pekrun and James Gross for their thoughts and feedback on similarities between their theory and model with regard to emotion regulation.

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© International Artificial Intelligence in Education Society 2016

Authors and Affiliations

  • Jason M. Harley
    • 1
  • Susanne P. Lajoie
    • 2
  • Claude Frasson
    • 3
  • Nathan C. Hall
    • 2
  1. 1.Educational PsychologyUniversity of AlbertaEdmontonCanada
  2. 2.Educational and Counselling PsychologyMcGill UniversityMontréalCanada
  3. 3.Computer Science and Operations ResearchUniversité de MontréalMontréalCanada

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