The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Self-Efficacy: An Essential Motive to Learn
Zimmerman, B. J. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1016 · Cited by 4,294
Self-Efficacy and Academic Motivation
Schunk, D. H. (1991) · Educational Psychologist · doi:10.1080/00461520.1991.9653133 · Cited by 4,292
Praise for intelligence can undermine children's motivation and performance.
Mueller, C. M., & Dweck, C. S. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.75.1.33 · Cited by 1,709
Intelligent Tutoring Goes To School in the Big City
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (2018) · doi:10.1184/r1/6470153 · Cited by 1,105
Theories and Principles of Motivation
Berliner, D. C., & Calfee, R. C. (2013) · Routledge eBooks · doi:10.4324/9780203053874-10 · Cited by 540
The Academic Lives of Neglected, Rejected, Popular, and Controversial Children
Wentzel, K. R., & Asher, S. R. (1995) · Child Development · doi:10.1111/j.1467-8624.1995.tb00903.x · Cited by 532
Affective Learning — A Manifesto
Picard, R. W., Papert, S. A., Bender, W. R., Blumberg, B. M., Breazeal, C., Cavallo, D. P., Machover, T., Resnick, M., Roy, D. K., & Strohecker, C. (2004) · BT Technology Journal · doi:10.1023/b:bttj.0000047603.37042.33 · Cited by 518
Using Bayesian Networks to Manage Uncertainty in Student Modeling
Conati, C., Gertner, A., & VanLehn, K. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021258506583 · Cited by 493
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
Repairing Disengagement With Non-Invasive Interventions
Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Meheranian, H., Fisher, D., Barto, A. G., Mahadevan, S., & Woolf, B. P. (2007) · Cited by 126
Picard, R. W., & Burleson, W. (2006) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 111
Viewing Student Affect and Learning through Classroom Observation and Physical Sensors
Dragon, T., Arroyo, I., Woolf, B. P., Burleson, W., el Kaliouby, R., & Eydgahi, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_8 · Cited by 81
Emotions and Learning with AutoTutor
Graesser, A. C., Chipman, P., King, B. G., McDaniel, B., & D’Mello, S. K. (2007) · Cited by 76
Affective Gendered Learning Companions
Arroyo, I., Park, W. B., Royer, J. M., & Minghui, T. (2009) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-028-5-41 · Cited by 74
Self-citationWeb-Based Intelligent Multimedia Tutoring for High Stakes Achievement Tests
Arroyo, I., Beal, C., Murray, T., Walles, R., & Woolf, B. P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_44 · Cited by 74
Evaluating a Probabilistic Model of Student Affect
Conati, C., & Maclare, H. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_6 · Cited by 72
Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments
D’Mello, S. K., & Graesser, A. C. (2007) · Cited by 51
Mind-reading machines: automated inference of complex mental states
el Kaliouby, & Ayman, R. (2005) · Cited by 49
Diagnosing Self-efficacy in Intelligent Tutoring Systems: An Empirical Study
McQuiggan, S. W., & Lester, J. C. (2006) · Lecture notes in computer science · doi:10.1007/11774303_56 · Cited by 42
Cited by
Sorted by citations
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
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364
“Based on the maturity of biofeedback technique, such as eye-tracking and EEG, affection computing was increasingly adopted to investigate students' internal motivations on learning, such as creativity and responsibility [49, 50].”
MethodConfusion can be beneficial for learning.
D'Mello, S. K., Lehman, B. A., Pekrun, R., & Graesser, A. (2014) · Learning and Instruction · doi:10.1016/j.learninstruc.2012.05.003 · Cited by 782
A review of recent advances in learner and skill modeling in intelligent learning environments
Desmarais, M. C., & Baker, R. S. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9106-8 · Cited by 477
Learner Engagement in Blended Learning Environments: A Conceptual Framework
Halverson, L. R., & Graham, C. R. (2019) · Online Learning · doi:10.24059/olj.v23i2.1481 · Cited by 566
Metadata
- 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
- 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
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 ›
Are you an author, or do you see a problem with this page? Request a review
