Cited by 382Intelligent Tutoring Systems and Adaptive LearningEmotion and Mood RecognitionOnline Learning and Analytics

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Emotion Sensors Go To School

Arroyo Ivon, Cooper David G., Burleson Winslow, Woolf Beverly Park, Muldner Kasia, Christopherson Robert·2009·Frontiers in Artificial Intelligence and Applications

DOI 10.3233/978-1-60750-028-5-17

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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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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)
DOI: 10.3233/978-1-60750-028-5-17
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
Physical Sciences › Computer Science › Artificial Intelligence › 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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