World's Best Scientists 2026 revealed!

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Computer Science

D-Index
77
Citations
22288
World Ranking
1284
National Ranking
681

Psychology

D-Index
80
Citations
25113
World Ranking
1446
National Ranking
870

Overview

Sidney K. D'Mello is affiliated with the University of Colorado Boulder in the United States. Their research spans multiple interconnected fields, primarily focusing on psychology, neuroscience, and computer science. Within these, they have contributed extensively to subfields such as cognitive neuroscience, developmental and educational psychology, experimental and cognitive psychology, artificial intelligence, and education.

Their work addresses various topics, including:

  • Innovative teaching and learning methods
  • Mind wandering and attention
  • Neural and behavioral psychology studies
  • Mental health research topics
  • Educational games and gamification
  • Intelligent tutoring systems and adaptive learning
  • Online learning and analytics

Recent publications by Sidney K. D'Mello illustrate their focus on cognitive and educational aspects of psychology and technology. Notable papers include:

  • "The eye-mind wandering link: Identifying gaze indices of mind wandering across tasks." (2020) published in Journal of Experimental Psychology Human Perception & Performance
  • "The relationship between collaborative problem solving behaviors and solution outcomes in a game-based learning environment" (2021) published in Computers in Human Behavior
  • "Eye-Mind reader: an intelligent reading interface that promotes long-term comprehension by detecting and responding to mind wandering" (2020) published in Human-Computer Interaction
  • "Large studies reveal how reference bias limits policy applications of self-report measures" (2022) published in Scientific Reports
  • "A Conceptual Framework for Investigating and Mitigating Machine-Learning Measurement Bias (MLMB) in Psychological Assessment" (2022) published in Advances in Methods and Practices in Psychological Science

Their collaborative network features frequent co-authors such as Rosy Southwell, Stephen M. Mattingly, Gloria Mark, Brandon M. Booth, and Gonzalo J. Martinez.

Their work has appeared regularly in venues like:

  • Proceedings of the ACM on Human-Computer Interaction
  • User Modeling and User-Adapted Interaction
  • Zenodo (CERN European Organization for Nuclear Research)
  • International Journal of Artificial Intelligence in Education
  • arXiv (Cornell University)

Best Publications

  • Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

    Rafael A Calvo;Sidney D'Mello

  • 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

    Ryan S. J. d. Baker;Sidney K. D'Mello;Ma.Mercedes T. Rodrigo;Arthur C. Graesser

  • Dynamics of affective states during complex learning

    Sidney D’Mello;Art Graesser

  • Confusion can be beneficial for learning

    Sidney D’Mello;Blair Lehman;Reinhard Pekrun;Art Graesser

  • Boring but important: A self-transcendent purpose for learning fosters academic self-regulation.

    David Scott Yeager;Marlone Deshaun Henderson;David Paunesku;Gregory M. Walton

  • A Review and Meta-Analysis of Multimodal Affect Detection Systems

    Sidney K. D'mello;Jacqueline Kory

  • Toward an Affect-Sensitive AutoTutor

    S. D'Mello;A. Graesser;R.W. Picard

  • Automatic detection of learner's affect from conversational cues

    Sidney K. D'Mello;Scotty D. Craig;Amy Witherspoon;Bethany Mcdaniel

  • The Oxford Handbook of Affective Computing

    Rafael A. Calvo;Sidney D'Mello;Jonathan Gratch;Arvid Kappas

  • Gaze tutor: A gaze-reactive intelligent tutoring system

    Sidney D'Mello;Andrew Olney;Claire Williams;Patrick Hays

  • AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back

    Sidney D'mello;Art Graesser

  • Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features

    Sidney K. D'Mello;Arthur Graesser

  • Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning.

    Sidney D'Mello;Ed Dieterle;Angela Duckworth

  • A Selective Meta-Analysis on the Relative Incidence of Discrete Affective States during Learning with Technology

    Sidney D'Mello

  • Mind wandering while reading easy and difficult texts

    Shi Feng;Sidney D’Mello;Arthur C. Graesser

  • Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate

    Hamed Monkaresi;Nigel Bosch;Rafael A. Calvo;Sidney K. D'Mello

  • LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

    Stan Franklin;Tamas Madl;Sidney D'Mello;Javier Snaider

  • The half-life of cognitive-affective states during complex learning

    Sidney D'Mello;Art Graesser

  • Improving automated source code summarization via an eye-tracking study of programmers

    Paige Rodeghero;Collin McMillan;Paul W. McBurney;Nigel Bosch

  • Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning

    Scotty D. Craig;Sidney D'Mello;Amy Witherspoon;Art Graesser

Frequent Co-Authors

Arthur C. Graesser
Arthur C. Graesser University of Memphis
Caitlin Mills
Caitlin Mills University of Minnesota
Rafael A. Calvo
Rafael A. Calvo Imperial College London
Valerie J. Shute
Valerie J. Shute Florida State University
Ryan S. Baker
Ryan S. Baker University of Pennsylvania
Angela L. Duckworth
Angela L. Duckworth University of Pennsylvania
Gloria Mark
Gloria Mark University of California, Irvine
Munmun De Choudhury
Munmun De Choudhury Georgia Institute of Technology
Evan F. Risko
Evan F. Risko University of Waterloo
Gabriel A. Radvansky
Gabriel A. Radvansky University of Notre Dame

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