S. D’Mello

Experimental and Cognitive Psychology · 2006–2015

A researcher in the Science corpus: 14 papers; trusts 117 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Experimental and Cognitive Psychology — 159 citations into it; #1,327 of 9,731 by trust here, #1,753 by citations
• Artificial Intelligence — 156 citations into it; #4,897 of 35,345 by trust here, #4,747 by citations
• Social Psychology — 143 citations into it
• Developmental and Educational Psychology — 89 citations into it; #1,523 of 8,223 by trust here, #631 by citations
• Cognitive Neuroscience — 82 citations into it; #1,564 of 10,852 by trust here, #2,529 by citations
• Education — 58 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Computer Science Applications — 18 citations into it; #273 of 1,855 by trust here, #172 by citations

Whom S. D’Mello trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. A. Graesser
2. Cristina Conati
3. Kurt VanLehn
4. James Russell
5. Lisa Feldman Barrett
6. T. Landauer
7. Paul Ekman
8. Rosalind W. Picard
9. Roger Azevedo
10. Reinhard Pekrun

Who trusts S. D’Mello overall

1. A. Graesser
2. Benjamin D. Nye
3. W. L. Johnson
4. Krista R. Muis
5. Lisa R. Halverson
6. James C. Lester
7. David B. Miele
8. Wei-Long Zheng
9. Rafael A. Calvo
10. Curtis R. Henrie

Papers from S. D’Mello's perspective

Ordered by how strongly the researchers S. D’Mello trusts endorse each paper. Same corpus, a different order for every researcher.

1. Core affect and the psychological construction of emotion. (Psychology Review)
2. Toward Machine Emotional Intelligence: Analysis of Affective Physiological State (IEEE Transactions on Pattern Analysis and Machine Intelligence)
3. An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor
4. Mind in society: The development of higher psychological processes. L. S. Vygotsky.
5. Automated Posture Analysis for Detecting Learner's Interest Level (2003 Conference on Computer Vision and Pattern Recognition Workshop)
6. Eliciting Self-Explanations Improves Understanding
7. An argument for basic emotions
8. Probabilistic assessment of user's emotions in educational games (Applied Artificial Intelligence)
9. An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion (Proceedings IEEE International Conference on Advanced Learning Technologies)
10. Learning from human tutoring (Cognitive Sciences)

Their papers

• A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015) — 615 citations
• Interest-based text preference moderates the effect of text difficulty on engagement and learning (2015) — 92 citations
• Confusion can be beneficial for learning. (2014) — 755 citations
• The Oxford Handbook of Affective Computing (2014) — 388 citations
• Gaze tutor: A gaze-reactive intelligent tutoring system (2012) — 357 citations
• AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012) — 427 citations
• Emotions During the Learning of Difficult Material (2012) — 109 citations
• Dynamics of affective states during complex learning (2012) — 1,000 citations
• Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010) — 1,781 citations
• 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 (2010) — 908 citations
• Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010) — 346 citations
• Automatic detection of learner’s affect from conversational cues (2008) — 370 citations
• Toward an Affect-Sensitive AutoTutor (2007) — 420 citations
• Detection of Emotions during Learning with AutoTutor (2006) — 150 citations