Engagement Detection and Its Applications in Learning: A Tutorial and Selective Review

Brandon M. Booth, Nigel Bosch, Sidney K. D'Mello

Research output: Contribution to journalArticlepeer-review

Abstract

Engagement is critical to satisfaction and performance in a number of domains but is challenging to measure and sustain. Thus, there is considerable interest in developing affective computing technologies to automatically measure and enhance engagement, especially in the wild and at scale. This article provides an accessible introduction to affective computing research on engagement detection and enhancement using educational applications as an application domain. We begin with defining engagement as a multicomponential construct (i.e., a conceptual entity) situated within a context and bounded by time and review how the past six years of research has conceptualized it. Next, we examine traditional and affective computing methods for measuring engagement and discuss their relative strengths and limitations. Then, we move to a review of proactive and reactive approaches to enhancing engagement toward improving the learning experience and outcomes. We underscore key concerns in engagement measurement and enhancement, especially in digitally enhanced learning contexts, and conclude with several open questions and promising opportunities for future work.

Original languageEnglish (US)
Pages (from-to)1398-1422
Number of pages25
JournalProceedings of the IEEE
Volume111
Issue number10
DOIs
StatePublished - Oct 1 2023

Keywords

  • Affective computing
  • engagement
  • learning outcomes
  • procedural justice

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering

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