The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Relating Children’s Automatically Detected Facial Expressions to Their Behavior in RoboTutor
Open accessAbstract
Can student behavior be anticipated in real-time so that an intelligent tutor system can adapt its content to keep the student engaged? Current methods detect affective states of students during learning session to determine their engagement levels but apply the learning in next session in the form of intervention policies and tutor responses. However, if students' imminent behavioral action could be anticipated from their affective states in real-time, this could lead to much more responsive intervention policies by the tutor and assist in keeping the student engaged in an activity, thereby increasing tutor efficacy as well as student engagement levels. In this paper we explore if there exist any links between a student's affective states and his/her imminent behavior action in RoboTutor, an intelligent tutor system for children to learn math, reading and writing. We then exploit our findings to develop a real-time student behavior prediction module.
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“AIEd systems have great potential to address inequities in education, for example by filling gaps in the educational services otherwise available to learners (Madaio et al., 2020; Saxena et al., 2018; Uchidiuno et al., 2018).”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Saxena et al., 2018
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Cites
OpenFace: An open source facial behavior analysis toolkit
Baltrušaitis, T., Robinson, P., & Morency, L. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477553 · Cited by 1,794
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions
Whitehill, J., Serpell, Z. N., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2316163 · Cited by 734
D’Mello, S. K., & Graesser, A. (2010) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9074-4 · Cited by 323
Craig, S. D., D’Mello, S. K., Witherspoon, A. M., & Graesser, A. C. (2008) · Cognition & Emotion · doi:10.1080/02699930701516759 · Cited by 207
Facial Features for Affective State Detection in Learning Environments
McDaniel, B., D’Mello, S. K., King, B. G., Chipman, P., Tapp, K. M., & Graesser, A. C. (2007) · eScholarship (California Digital Library) · Cited by 132
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Holstein, K., & Doroudi, S. (2021) · arXiv · doi:10.48550/arxiv.2104.12920 · Cited by 29
“AIEd systems have great potential to address inequities in education, for example by filling gaps in the educational services otherwise available to learners (Madaio et al., 2020; Saxena et al., 2018; Uchidiuno et al., 2018).”
BackgroundEarly Prediction of Student Engagement-Related Events from Facial and Contextual Features
Kaushik, R., & Simmons, R. G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90525-5_26 · Cited by 1
Metadata
- Authors
- Saxena, M., Pillai, R., & Mostow, J.
- Affiliations
- Delhi Technological University (IN)
- Carnegie Mellon University Qatar (QA)
- Carnegie Mellon University (US)
- ORCID
- Saxena, Mayank
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-29
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2807588352
- Cited by
- 2
- References
- 5
- Citations by year
- 2021: 2
- FWCI
- 0.20
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- facial expression recognition, student behavior prediction, affective states, intelligent tutoring systems, student engagement, children's learning, adaptive tutoring
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- Saxena, M., Pillai, R., & Mostow, J. (2018). Relating Children’s Automatically Detected Facial Expressions to Their Behavior in RoboTutor. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v32i1.12190
