Toward an Affect-Sensitive AutoTutor

A Coefficient of Agreement for Nominal Scales

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Facial action coding system

Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019 · see citing works

Toward an affect-sensitive multimodal human-computer interaction

Pantic, M., & Rothkrantz, L. J. M. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817122 · Cited by 791 · see citing works

An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

Kort, B. W., Reilly, R., & Picard, R. W. (2002) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2001.943850 · Cited by 751 · see citing works

AutoTutor: an intelligent tutoring system with mixed-initiative dialogue.

Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005) · IEEE Transactions on Education · doi:10.1109/te.2005.856149 · Cited by 750 · see citing works

Affect and learning: An exploratory look into the role of affect in learning with AutoTutor

Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004) · Journal of Educational Media · doi:10.1080/1358165042000283101 · Cited by 630 · see citing works

Probabilistic assessment of user's emotions in educational games

Conati, C. (2002) · Applied Artificial Intelligence · doi:10.1080/08839510290030390 · Cited by 498 · see citing works

Automated Posture Analysis for Detecting Learner's Interest Level

Mota, S., & Picard, R. W. (2003) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2003.10047 · Cited by 315 · see citing works

ITSPOKE

Litman, D. J., & Silliman, S. (2004) · doi:10.3115/1614025.1614027 · Cited by 207 · see citing works

Predicting student emotions in computer-human tutoring dialogues

Litman, D. J., & Forbes-Riley, K. (2004) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1218955.1219000 · Cited by 181 · see citing works

Predicting Affective States expressed through an Emote-Aloud Procedure from AutoTutor's Mixed-Initiative Dialogue

D’Mello, S. K., Craig, S. D., Sullins, J., & Graesser, A. C. (2006) · doi:10.5555/1434940.1434942 · Cited by 136 · see citing works

Detection of Emotions during Learning with AutoTutor

Chipman, P., D’Mello, S. K., Gholson, B., Graesser, A. C., McDaniel, B., & Witherspoon, A. M. (2006) · eScholarship (California Digital Library) · Cited by 122 · see citing works

Generalization of a Vision-Based Computational Model of Mind-Reading

el Kaliouby, R., & Robinson, P. (2005) · Lecture notes in computer science · doi:10.1007/11573548_75 · Cited by 65 · see citing works

Measuring student engagement in technology-mediated learning: A review

Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.09.005 · Cited by 1,010 · see citing works

Historical threads, missing links, and future directions in AI in education

Williamson, B., & Eynon, R. (2020) · Learning, Media and Technology · doi:10.1080/17439884.2020.1798995 · Cited by 981 · see citing works

Empowering Educators to Be AI-Ready

Luckin, R., Cukurova, M., Kent, C., & du Boulay, B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100076 · Cited by 422 · see citing works

Emotion Sensors Go To School

Ivon, A., Cooper David, G., Winslow, B., Park, W. B., Kasia, M., & Robert, C. (2009) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/978-1-60750-028-5-17 · Cited by 382 · see citing works

Affect-aware tutors: recognising and responding to student affect

Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009) · International Journal of Learning Technology · doi:10.1504/ijlt.2009.028804 · Cited by 371 · see citing works

Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Levi, G., & Hassner, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830587 · Cited by 336 · see citing works

The Measurement of Learners’ Self-Regulated Cognitive and Metacognitive Processes While Using Computer-Based Learning Environments

GREENE, J. A., & AZEVEDO, R. (2010) · Educational Psychologist · doi:10.1080/00461520.2010.515935 · Cited by 267 · see citing works

Deep learning for human affect recognition: Insights and new developments

Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works

Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms

Holstein, K., McLaren, B. M., & Aleven, V. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-23204-7_14 · Cited by 189 · see citing works

Self-Regulated Learning in Learning Environments With Pedagogical Agents That Interact in Natural Language

GRAESSER, A., & McNAMARA, D. (2010) · Educational Psychologist · doi:10.1080/00461520.2010.515933 · Cited by 153 · see citing works

Designing emotionally sentient agents

McDuff, D., & Czerwinski, M. (2018) · Communications of the ACM · doi:10.1145/3186591 · Cited by 132 · see citing works

Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., & Zhou, R. (2021) · Asia Pacific Education Review · doi:10.1007/s12564-021-09697-7 · Cited by 124 · see citing works

EMMA: an emotion-aware wellbeing chatbot

Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019) · 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2019.8925455 · Cited by 126 · see citing works

Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them

Scholten, M. R., Kelders, S. M., & Van Gemert-Pijnen, J. E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7351 · Cited by 97 · see citing works

Social support agents for older adults: longitudinal affective computing in the home

Ring, L., Shi, L., Totzke, K., & Bickmore, T. (2015) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-014-0157-0 · Cited by 97 · see citing works

Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in virtual learning environment

Ouherrou, N., Elhammoumi, O., Benmarrakchi, F., & El Kafi, J. (2019) · Education and Information Technologies · doi:10.1007/s10639-018-09852-5 · Cited by 83 · see citing works

Delivering hints in a dialogue-based intelligent tutoring system

Zhou, Y., Freedman, R., Glass, M., MICHAEL, J. A., Rovick, A. A., & Evens, M. W. (1999) · Cited by 52 · see citing works

FITS: a fraction intelligent tutoring system

Nwana, H. S. (1991) · National Conference on Artificial Intelligence · Cited by 3 · see citing works

Building a new student model to support adaptive tutoring in a natural language dialogue system

Evens, M. W., & Zhou, Y. (2000) · Cited by 5 · see citing works

To Tutor the Tutor: Adaptive Domain Support for Peer Tutoring

Walker, E., Rummel, N., & Koedinger, K. R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_65 · Cited by 21 · see citing works

Computational Intelligence Approaches for Student/Tutor Modelling: A Review

Sani, S. M., & Aris, T. N. M. (2014) · International Conference on Intelligent Systems, Modelling and Simulation · doi:10.1109/isms.2014.21 · Cited by 25 · see citing works