S
Sidney K. D'Mello
Researcher at University of Colorado Boulder
Publications - 371
Citations - 17216
Sidney K. D'Mello is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Computer science & Intelligent tutoring system. The author has an hindex of 58, co-authored 337 publications receiving 13985 citations. Previous affiliations of Sidney K. D'Mello include University of Memphis & University of Notre Dame.
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Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
TL;DR: This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations.
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Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments
TL;DR: Findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the ''vicious cycles'' which occur when a student becomes bored and remains bored for long periods of time.
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Dynamics of affective states during complex learning
TL;DR: The authors proposed a model to explain the dynamics of affective states that emerge during deep learning activities, which predicts that learners in a state of engagement/flow will experience cognitive disequilibrium and confusion when they face contradictions, incongruities, anomalies, obstacles to goals, and other impasses.
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Confusion can be beneficial for learning
TL;DR: In this paper, confusion was experimentally induced via a contradictory-information manipulation involving the animated agents expressing incorrect and/or contradictory opinions and asking the human learners to decide which opinion had more scientific merit.
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A Review and Meta-Analysis of Multimodal Affect Detection Systems
TL;DR: A quantitative review and meta-analysis of 90 Multimodal affect detection systems revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%).