Emotion Sensors Go To School
Abstract
This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experiments the tutor dynamically collected data streams of physiological activity and students' self-reports of emotions. Evidence indicates that state-based fluctuating student emotions are related to larger, longer-term affective variables such as self-concept in mathematics. Students produced self-reports of emotions and models were created to automatically infer these emotions from physiological data from the sensors. Summaries of student physiological activity, in particular data streams from facial detection software, helped to predict more than 60% of the variance of students emotional states, which is much better than predicting emotions from other contextual variables from the tutor, when these sensors are absent. This research also provides evidence that by modifying the “context” of the tutoring system we may well be able to optimize students' emotion reports and in turn improve math attitudes.
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Affection computing was increasingly adopted to investigate students' internal motivations on learning, such as creativity and responsibility, based on the maturity of biofeedback techniques such as eye-tracking and EEG.
“Based on the maturity of biofeedback technique, such as eyetracking and EEG, affection computing was increasingly adopted to investigate students' internal motivations on learning, such as creativity and responsibility [49,50].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [49]
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Emotion Recognition with the Help of Privileged Information
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Embedding emotion awareness into e-learning environments
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Keep Me in the Loop: Real-Time Feedback with Multimodal Data
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- Authors
- Ivon, A., Cooper David, G., Winslow, B., Park, W. B., Kasia, M., & Robert, C.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- Frontiers in Artificial Intelligence and Applications
- Publisher
- IOS Press
- Published
- 2009
- ISSN
- 0922-6389 (ISSN-L)
- OpenAlex
- W1929610448
- Cited by
- 382
- References
- 19
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 13 · 2023: 13 · 2022: 15 · 2021: 17 · 2020: 23 · 2019: 26 · 2018: 24 · 2017: 18 · 2016: 28 · 2015: 26 · 2014: 32 · 2013: 43 · 2012: 27
- FWCI
- 48.56
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Frontiers in artificial intelligence and applications (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
- intelligent tutoring systems, affective computing, student affective states, physiological signals, face detection, emotion inference, mathematics self-concept, student attitudes toward mathematics, classroom experiments
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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.
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