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date: 10 December 2019

Abstract and Keywords

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.

Keywords: affect-aware learning technology, proactive systems, reactive systems, Affective AutoTutor, GazeTutor, UNC-ITSpoke, Crystal Island, ConfusionTutor

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