Automatic prediction of frustration
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Detecting students' frustration can help improve e-learning experiences.
“Detecting students’ frustration can help improve e-learning experiences [44].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [44]
Most of AFER was used for predicting primary affective states of basic emotions, but FEs were also used for predicting non-primary affective states such as complex mental states, fatigue, frustration, pain, depression, mood and personality traits.
“Most of AFER was used for predicting primary affective states of basic emotions, such as anger or happiness but FEs were also used for predicting non-primary affective states such as complex mental states [223], fatigue [224], frustration [44], pain [185], [186], [225], depression [53], [250], mood and personality traits [251], [252].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [44]
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Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Self-citationIntimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
Moon, Y. (2000) · Journal of Consumer Research · doi:10.1086/209566 · Cited by 842 · see citing works
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Baker, R. S. J. D., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.12.003 · Cited by 936 · see citing works
Social Robots for Long-Term Interaction: A Survey
Leite, I., Martinho, C., & Paiva, A. (2013) · International Journal of Social Robotics · doi:10.1007/s12369-013-0178-y · Cited by 871 · see citing works
Fundamentals of physiological computing
Fairclough, S. H. (2009) · Interacting with Computers · doi:10.1016/j.intcom.2008.10.011 · Cited by 608 · see citing works
Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · 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
Self-citationShort-term emotion assessment in a recall paradigm
Chanel, G., Kierkels, J. J. M., Soleymani, M., & Pun, T. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.005 · Cited by 367 · see citing works
Learning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287 · see citing works
Facial Expression Recognition Using Facial Movement Features
Zhang, L., & Tjondronegoro, D. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.13 · Cited by 253 · see citing works
Automatic analysis of affective postures and body motion to detect engagement with a game companion,
Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P. W., & Paiva, A. (2011) · Proceedings of the 6th international conference on Human-robot interaction · doi:10.1145/1957656.1957781 · Cited by 250 · see citing works
Towards an intelligent framework for multimodal affective data analysis
Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.005 · Cited by 236 · see citing works
Facial Expression Recognition: A Survey
Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195 · see citing works
Automatic Detection of Learning-Centered Affective States in the Wild
Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701397 · Cited by 178 · see citing works
A survey of affect recognition methods: audio, visual and spontaneous expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2007) · Proceedings of the 9th international conference on Multimodal interfaces · doi:10.1145/1322192.1322216 · Cited by 144 · see citing works
Experience sampling for building predictive user models: a comparative study
Kapoor, A., & Horvitz, E. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357159 · Cited by 88 · see citing works
Self-citationEmotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
Wei, W., Jia, Q., Feng, Y., & Chen, G. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/5296523 · Cited by 82 · see citing works
Food and Mood: Just-in-Time Support for Emotional Eating
Carroll, E. A., Czerwinski, M., Roseway, A., Kapoor, A., Johns, P., Rowan, K., & Schraefel, M. C. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.48 · Cited by 64 · see citing works
Self-citationCapturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation
Fairclough, S. H., Gilleade, K., Ewing, K. C., & Roberts, J. (2013) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2013.050694 · Cited by 60 · see citing works
Virtual Affective Agents and Therapeutic Games
Hudlicka, E. (2016) · Artificial Intelligence in Behavioral and Mental Health Care · doi:10.1016/b978-0-12-420248-1.00004-0 · Cited by 34 · see citing works
Han, B., Ma, Y., Coghlan, S., McKay, D., Buchanan, G., & Smith, W. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790360 · Cited by 3 · see citing works
- Authors
- Kapoor, A., Burleson, W., & Picard, R. W.
- Affiliations
- Microsoft (United States) (US)
- Arizona State University (US)
- MIT Media Lab (US)
- Corresponding author
- Picard, Rosalind W.
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 2007-08
- Volume
- 65
- Issue
- 8
- Pages
- 724-736
- ISSN
- 1071-5819 (ISSN-L)
- OpenAlex
- W2127429655
- Cited by
- 592
- References
- 2
- Citations by year
- 2026: 24 · 2025: 11 · 2024: 9 · 2023: 16 · 2022: 25 · 2021: 32 · 2020: 24 · 2019: 34 · 2018: 26 · 2017: 26 · 2016: 40 · 2015: 42 · 2014: 44 · 2013: 51 · 2012: 49
- FWCI
- 37.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Studies (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
- learner frustration, intelligent tutoring systems, affective computing, Gaussian process classification, Bayesian inference, student engagement
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