The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions
Abstract
Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen's κ = 0.96). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( κ = 0.56). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson r=0.85), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( r=0.47) as from pre-test scores ( r=0.44).
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
School Engagement: Potential of the Concept, State of the Evidence
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004) · Review of Educational Research · doi:10.3102/00346543074001059 · Cited by 12,581
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
Acquisition of cognitive skill
Anderson, J. R. (1982) · Psychological Review · doi:10.1037/0033-295x.89.4.369 · Cited by 3,663
Student engagement in high school classrooms from the perspective of flow theory
Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003) · School Psychology Quarterly · doi:10.1521/scpq.18.2.158.21860 · Cited by 1,237
Toward an Affect-Sensitive AutoTutor
D'Mello, S., Picard, R. W., & Graesser, A. (2007) · IEEE Intelligent Systems · doi:10.1109/mis.2007.79 · Cited by 386
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
Automatic coding of facial expressions displayed during posed and genuine pain
Littlewort, G. C., Bartlett, M. S., & Lee, K. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.12.010 · Cited by 216
Cited by
Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
Microexpression Identification and Categorization Using a Facial Dynamics Map
Xu, F., Zhang, J., & Wang, J. Z. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2518162 · Cited by 283
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
Yang, J., Wang, K., Peng, X., & Qiao, Y. (2018) · Proceedings of the 20th ACM International Conference on Multimodal Interaction · doi:10.1145/3242969.3264981 · Cited by 79
On Incorporating Affective Support to an Intelligent Tutoring System: an Empirical Study
Cunha-Perez, C., Arevalillo-Herraez, M., Marco-Gimenez, L., & Arnau, D. (2018) · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje · doi:10.1109/rita.2018.2831760 · Cited by 16
Metadata
- Authors
- Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2014-01-01
- Volume
- 5
- Issue
- 1
- Pages
- 86-98
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2081112272
- Cited by
- 677
- References
- 10
- FWCI
- 28.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2014.2316163
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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