The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Automatic Detection of Learning-Centered Affective States in the Wild
Abstract
Affect detection is a key component in developing intelligent educational interfaces that are capable of responding to the affective needs of students. In this paper, computer vision and machine learning techniques were used to detect students' affect as they used an educational game designed to teach fundamental principles of Newtonian physics. Data were collected in the real-world environment of a school computer lab, which provides unique challenges for detection of affect from facial expressions (primary channel) and gross body movements (secondary channel) - up to thirty students at a time participated in the class, moving around, gesturing, and talking to each other. Results were cross validated at the student level to ensure generalization to new students. Classification was successful at levels above chance for off-task behavior (area under receiver operating characteristic curve or (AUC = .816) and each affective state including boredom (AUC =.610), confusion (.649), delight (.867), engagement (.679), and frustration (.631) as well as a five-way overall classification of affect (.655), despite the noisy nature of the data. Implications and prospects for affect-sensitive interfaces for educational software in classroom environments are discussed.
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In the citing paper
“Methods for recognizing other affective states have also been proposed, namely cognitive states like boredom, confusion, delight, concentration and frustration [266], positive and negative affect from groups of people [267] or liking/not-linking of online media for predicting buying behaviour for marketing purposes [268].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [266]
In the citing paper
“Most approaches target primitive FEs of affect, but others recognize cognitive states [266], postive and negative affect from groups of people [267] and liking/not-linking of online media for predicting buying behaviour for marketing purposes [268].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [266]
In the citing paper
“In naturalistic environments, most approaches target primitive FEs of affect. Methods for recognizing cognitive states [266], positive and negative affect from groups of people [267] or liking/not-linking of online media for predicting buying behaviour for marketing purposes [268] are also common.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [266]
Citations
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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×
“Affect detection based on facial features has been the subject of considerable previous research (see [6,42] for reviews); albeit outside of learning contexts and mainly in laboratory settings (see exceptions discussed below).”
BackgroundInfluentialSelf-citationAllison, P. D. (1998) · Cited by 1,721
Dynamics of affective states during complex learning
D’Mello, S. K., & Graesser, A. C. (2011) · Learning and Instruction · doi:10.1016/j.learninstruc.2011.10.001 · Cited by 1,101
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Research works printed but not linked: 3
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- Cook , D. L. The Hawthorne effect in educational research . Phi Delta Kappan , ( 1962 ), 116--122. Cook, D. L. The Hawthorne effect in educational research. Phi Delta Kappan, (1962), 116--122.
- D'Mello , S. , Blanchard , N. , Baker , R. , Ocumpaugh , J. , and Brawner , K . I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu and B. Goldberg, eds., Design Recommendations for Intelligent Tutoring Systems Volume 2 : Instructional Management . 2014 , 35 -- 48 . D'Mello, S., Blanchard, N., Baker, R., Ocumpaugh, J., and Brawner, K. I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu and B. Goldberg, eds., Design Recommendations for Intelligent Tutoring Systems Volume 2: Instructional Management. 2014, 35--48.
Cited by
Sorted by citations
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
Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., Fang, X., Fidopiastis, C., Fragomeni, G., Fu, L. P., et al. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1619259 · Cited by 524
Engagement detection in online learning: a review
Dewan, M. A. A., Murshed, M., & Lin, F. O. (2019) · Smart Learning Environments · doi:10.1186/s40561-018-0080-z · Cited by 390
Computer vision for assistive technologies
Leo, M., Medioni, G. G., Trivedi, M. M., Takeo Kanade, A., & Farinella, G. M. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.09.001 · Cited by 235
Multimodal data as a means to understand the learning experience
Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.02.003 · Cited by 227
Metadata
- Authors
- Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W.
- Affiliations
- University of Notre Dame (US)
- University of Notre Dame, Notre Dame, IN, USA
- Columbia University (US)
- Florida State University (US)
- Venue
- Proceedings of the 20th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2015-03-18
- Pages
- 379-388
- OpenAlex
- W2115417283
- Cited by
- 178
- References
- 45
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 11 · 2023: 11 · 2022: 11 · 2021: 14 · 2020: 17 · 2019: 20 · 2018: 18 · 2017: 21 · 2016: 15 · 2015: 9
- FWCI
- 28.05
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- NSF (DRL 1235958)
- Bill & Melinda Gates Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- affective computing, affect recognition, game-based learning, facial expression recognition, body movement analysis, off-task behavior, boredom, confusion, engagement, frustration, delight, classroom climate
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