
Contents
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Introduction Introduction
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Finding a Niche for AALTs Within Affect, Learning, and Technology Finding a Niche for AALTs Within Affect, Learning, and Technology
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Which Affective States Are Relevant to Learning with Technology? Which Affective States Are Relevant to Learning with Technology?
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Reactive Affect-Aware Learning Technologies Reactive Affect-Aware Learning Technologies
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Affective AutoTutor: Responding to Boredom, Confusion, and Frustration Affective AutoTutor: Responding to Boredom, Confusion, and Frustration
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GazeTutor: Responding to Boredom and Disengagement GazeTutor: Responding to Boredom and Disengagement
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UNC-ITSpoke UNC-ITSpoke
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Proactive Affect-Aware Learning Technologies Proactive Affect-Aware Learning Technologies
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Crystal Island Crystal Island
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ConfusionTutor—Inducing Confusion via Trialogues with Animated Agents ConfusionTutor—Inducing Confusion via Trialogues with Animated Agents
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Open Issues Open Issues
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Sensors, Sensorless, or Sensor-Lite? Issues of Scalability Sensors, Sensorless, or Sensor-Lite? Issues of Scalability
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How Good Is Good Enough? Issues of Detection Accuracy How Good Is Good Enough? Issues of Detection Accuracy
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How Adaptive Is Adaptive Enough? Issues of Levels of Analysis How Adaptive Is Adaptive Enough? Issues of Levels of Analysis
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Some Suggested Broad Steps Forward Some Suggested Broad Steps Forward
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Acknowledgments Acknowledgments
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References References
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31 Feeling, Thinking, and Computing with Affect-Aware Learning Technologies
Get accessSidney D’Mello is an Assistant Professor in the departments of Computer Science and Psychology at the University of Notre Dame. His primary research interests are in the affective, cognitive, learning, and computing sciences. More specific interests include affective computing, artificial intelligence in education, human-computer interaction, natural language understanding, and computational models of human cognition. He has co-edited six books and has published over 150 journal papers, book chapters, and conference proceedings in these areas. D’Mello’s work has received seven outstanding paper awards at international conferences, has been featured in several media outlets including the Wall Street Journal, and has been supported by the National Science Foundation, Institute for Education Sciences, Gates, Raikes, and Templeton Foundations. D’Mello is an associate editor for IEEE Transactions on Affective Computing and IEEE Transactions on Learning Technologies, a senior reviewer for the Journal of Educational Psychology, and serves on the executive board of the International Artificial Intelligence in Education Society. D’Mello received his PhD. in Computer Science from the University of Memphis in 2009. He also holds a M.S. in Mathematical Sciences and a B.S. in Electrical Engineering.
Arthur C. Graesser, Department of Psychology, Psychology Department, University of Memphis, Memphis, TN
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Published:04 August 2014
Cite
Abstract
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. This chapter discusses some of the exciting research in the nascent field of affect-aware learning technologies (AALTs)—educational technologies that compute affect in addition to cognition, metacognition, and motivation. We begin by positioning AALTs in the complex ecology comprising diverse phenomena and technologies in the cognitive, affective, learning, and computing sciences. This is followed by an overview of the major findings of a recent meta-analysis aimed at identifying a subset of learning-centered affective states that generalize across students, learning technologies, learning tasks, and experimental methodologies. We then turn our attention to the two major types of AALTs: reactive systems that respond to affect once it occurs and proactive systems that aim to induce or impede certain affective states. Affective AutoTutor, GazeTutor, and UNC-ITSpoke are presented as examples of reactive AALTs, while Crystal Island and ConfusionTutor are included as examples of proactive AALTs. Some of the open issues in the area of AALTs are discussed. These include scalability for real-world contexts, “good-enough” classification accuracy, and levels of analysis for affective responding. The chapter concludes by outlining two broad avenues of research for the field.
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