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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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“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]
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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
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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