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Theoretical Perspectives on Affect and Deep Learning

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New Perspectives on Affect and Learning Technologies

Abstract

This chapter focuses on connections between affect and cognition that are prevalent during deep learning. Deep learning occurs when a person attempts to comprehend difficult material, to solve a difficult problem, and to make a difficult decision. We emphasize theoretical perspectives that highlight the importance of cognitive disequilibrium to deep learning and problem solving. Cognitive disequilibrium occurs when there are obstacles to goals, interruptions of organized action sequences, impasses, system breakdowns, contradictions, anomalous events, dissonance, incongruities, negative feedback, uncertainty, and deviations from norms, and novelty. Cognitive disequilibrium launches a trajectory of cognitive and affective processes such as confusion and frustration until equilibrium is restored or disequilibrium is dampened via effortful problem solving and impasse resolution. We discuss the role of cognitive and task constraints in dictating the time-course of cognitive disequilibrium and affiliated affective states such as surprise, delight, confusion, and frustration. We conclude by discussing how these states and processes are mediated by self-concepts, goals, meta-knowledge, social interaction, and the learning environment.

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Acknowledgments

The research on was supported by the National Science Foundation (ITR 0325428, ALT-0834847, DRK-12-0918409), and the Institute of Education Sciences (R305H050169, R305B070349, R305A080589, R305A080594). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF or IES.

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Authors and Affiliations

  1. Department of Psychology, Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA

    Sidney K. D’Mello

Authors
  1. Art Graesser
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  2. Sidney K. D’Mello
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Correspondence to Sidney K. D’Mello .

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Editors and Affiliations

  1. , School of Electrical and Information Eng, The University of Sydney, Sydney, 2006, Australia

    Rafael A. Calvo

  2. , Department of Psychology, The University of Memphis, Memphis, 38152, USA

    Sidney K. D'Mello

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Graesser, A., D’Mello, S.K. (2011). Theoretical Perspectives on Affect and Deep Learning. In: Calvo, R., D'Mello, S. (eds) New Perspectives on Affect and Learning Technologies. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9625-1_2

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