Emotions in Advanced Learning Technologies
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There is evidence that students often remain in the same emotional condition under no intervention condition.
“Importantly, concerning academic emotion transitions, there is evidence that students often remain in the same emotional condition under no intervention condition (Graesser, D'Mello and Strain, 2015).”
From Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial (2021) · cites this paper as Graesser, D'Mello and Strain, 2015
Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 4× · see citing works
“The computers detect emotions of the learner by analyzing different communication channels and their interactions (Calvo & D’Mello, 2010; D’Mello & Graesser, 2010; Picard, 1997, 2010).”
BackgroundMethodInfluentialSelf-citationBarrett, L. F. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00003.x · Cited by 1,859 · see citing works
Ivon, A., Cooper David, G., Winslow, B., Park, W. B., Kasia, M., & Robert, C. (2009) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/978-1-60750-028-5-17 · Cited by 382 · see citing works
Affect-aware tutors: recognising and responding to student affect
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009) · International Journal of Learning Technology · doi:10.1504/ijlt.2009.028804 · Cited by 371 · see citing works
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Schiff, D. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00270-2 · Cited by 489 · see citing works
“Another potential harm emanates from the fact that intelligent tutoring systems and learning robots are increasingly incorporating the capacity to detect and respond to human emotions, such as through text, audio, individual avatars or agents (Dennis et al. 2016; Graesser et al. 2014; Harley et al. 2017; Woolf et al.…”
BackgroundNazari, N., Shabbir, M. S., & Setiawan, R. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07014 · Cited by 405 · see citing works
- Authors
- Graesser, A. C., D’Mello, S. K., & Strain, A. C.
- Venue
- International Handbook of Emotions in Education
- Publisher
- Routledge
- Published
- 2015-02-17
- OpenAlex
- W2501101206
- Cited by
- 59
- References
- 5
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 6 · 2022: 2 · 2021: 9 · 2020: 5 · 2019: 3 · 2018: 9 · 2017: 3 · 2016: 6 · 2015: 5 · 2014: 3
- FWCI
- 15.27
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- emotions, advanced learning technologies, affective computing, educational technology, affective learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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