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Affect, Meta-affect, and Affect Regulation During Complex Learning

  • Sidney K. D’Mello
  • Amber Chauncey Strain
  • Andrew Olney
  • Art Graesser
Chapter
Part of the Springer International Handbooks of Education book series (SIHE, volume 28)

Abstract

Complex learning of difficult subject matter with educational technologies involves a coordination of cognitive, metacognitive, and affective processes. While extensive theoretical and empirical research has examined learners’ cognitive and metacognitive processes, research on affective processes during learning has been slow to emerge. Because learners’ affective states can significantly impact their thoughts, feelings, behavior, and learning outcomes, inquiry into how these states emerge and influence engagement and learning is of vital importance. In this chapter, we describe several key theories of affect, meta-affect, and affect regulation during learning. We then describe our own empirical research that focuses on identifying the affective states that spontaneously emerge during learning with educational technologies, how affect relates to learning outcomes, and how affect can be regulated. The studies that we describe incorporate a variety of educational technologies, different learning contexts, a number of student populations, and diverse methodologies to track affect. We then describe and evaluate an affect-sensitive version of AutoTutor, a fully-automated intelligent tutoring system that detects and helps learners regulate their negative affective states (frustration, boredom, confusion) in order to increase engagement, task persistence, and learning gains. We conclude by discussing future directions of research on affect, meta-affect, and affect regulation during learning with educational technologies.

Keywords

Negative Emotion Educational Technology Affective State Deep Learning Emotion Regulation Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research was supported by the National Science Foundation (ITR 0325428, HCC 0834847, DRL 1235958) and Institute of Education Sciences, US Department of Education (R305A080594 and R305B070349). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.

We thank our research colleagues in the Emotive Computing Group and the Tutoring Research Group (TRG) at the University of Memphis (http://emotion.autotutor.org). We gratefully acknowledge our partners in the Affective Computing group at the MIT Media Lab.

Requests for reprints should be sent to Sidney D’Mello, 384 Fitzpatrick, University of Notre Dame, Notre Dame, IN 46556, USA. sdmello@nd.edu.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sidney K. D’Mello
    • 1
  • Amber Chauncey Strain
    • 2
  • Andrew Olney
    • 2
  • Art Graesser
    • 2
  1. 1.University of Notre DameNotre DameUSA
  2. 2.University of MemphisMemphisUSA

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