Significant Accomplishments, New Challenges, and New Perspectives

  • Sidney K. D’MelloEmail author
  • Rafael A. Calvo
Part of the Explorations in the Learning Sciences, Instructional Systems and Performance Technologies book series (LSIS, volume 3)


This concluding chapter provides an integrative summative evaluation of the various threads of interdisciplinary research described in this book. After reflecting on the recent emphasis on emotions in seemingly disparate fields such as cognitive psychology, computer science, and education, we synthesize some of the important milestones achieved in the still nascent field of affect-sensitive learning technologies. These defining accomplishments include (a) an infusion of theories on emotions and learning, (b) the identification of affective states that are relevant to learning along with some of their antecedents and consequents, (c) the advance of automated affect detection systems, and (d) the emergence of some of the first fully automated affect-sensitive learning environments. Next, we highlight some of the open problems and promising areas for future research. These include (a) obtaining coherence among multiple levels of analysis, (b) modeling complex interactions between affective traits, moods, affect-elicitation events, and emotions, (c) incorporating temporal dependencies and affective dynamics into models of emotion, (d) reconceptualizing existing affect detection systems, (e) revisiting reactive emotion regulation strategies, (f) the need for proactive emotionally intelligent strategies, and (g) the importance of broadening the scope of affect and learning research so that next-generation learning technologies are consistent with the learning styles of the twenty-first century and beyond.


Affective State Basic Emotion Social Emotion Emotion Theory Affective Computing 
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.



Sidney D’Mello was supported by the National Science Foundation (ITR 0325428, HCC 0834847) and the Institute of Education Sciences (R305A080594). Any opinions, findings, and conclusions, or recommendations expressed in this chapter are those of the authors and do not necessarily reflect the views of NSF and IES.


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Psychology Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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