Self versus teacher judgments of learner emotions during a tutoring session with autotutor

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5091 LNCS, Issue , 2008, Pages 9-18

  D'Mello, Sidney ^a   Taylor, Roger ^b   Davidson, Kelly ^a   Graesser, Art ^a  

^a UNIVERSITY OF MEMPHIS   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED INSTRUCTION; EDUCATION COMPUTING; STUDENTS;

AFFECTIVE STATE; AUTOTUTOR; COLLEGE STUDENTS; CONVERGENT VALIDITY; EXPERT TUTORING; INTELLIGENT TUTORING SYSTEM; LEARNING SESSIONS;

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 70349849798     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69132-7_6     Document Type: Conference Paper

References (22)

1. Craig, S.D., Graesser, A.C., Sullins, J., Gholson, B.: Affect and learning: An exploratory look into the role of affect in learning. Journal of Educational Media 29, 241-250 (2004)
2. Goleman, D.: Emotional intelligence. Bantam Books, New York (1995)
3. Graesser, A.C., Chipman, P., King, B., McDaniel, B., D'Mello, S.: Emotions and Learning with AutoTutor. In: Luckin, R., et al. (eds.) 13th International Conference on Artificial Intelligence in Education (AIED 2007), pp. 569-571. IOS Press (2007)
4. Snow, R., Corno, L., Jackson, D.: Individual differences in affective and cognitive functions. In: Berliner, D.C., Calfee, R.C. (eds.) Handbook of educational psychology, pp. 243-310. Macmillan, New York (1996)
5. Meyer, D.K., Turner, J.C.: Reconceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review 18, 377-390 (2006)
6. Graesser, A.C., Jackson, G.T., McDaniel, B.: AutoTutor holds conversations with learners that are responsive to their cognitive and emotional states. Educational Technology 47, 19-22 (2007)
7. Mandler, G.: Mind and emotion. Wiley, New York (1976)
8. Kim, Y.: Empathetic Virtual Peers Enhanced Learner Interest and Self-Efficacy. In: Workshop on Motivation and Affect in Educational Software at the 12th International Conference on Artificial Intelligentce in Education, Amsterdam, The Netherlands (2005)
9. Linnenbrink, E.A., Pintrich, P.R.: The role of motivational beliefs in conceptual change. In: Limon, M., Mason, L. (eds.) Reconsidering conceptual change: Issues in theory and practice, pp. 115-135. Kluwer Academic Publishers, Dordretch (2002)
10. Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper-Row, New York (1990)
11. D'Mello, S.K., Picard, R., Graesser, A.C.: Towards an Affect Sensitive AutoTutor. IEEE Intelligent Systems 22(4), 53-61 (2007)
12. Lepper., M.R., Chabay, R.W.: Socializing the intelligent tutor: Bringing empathy to computer tutors. In: Mandl, H., Lesgold, A. (eds.) Learning Issues for Intelligent Tutoring Systems, pp. 242-257. Erlbaum, Hillsdale (1988)
13. Lepper, M.R., Woolverton, M.: The wisdom of practice: Lessons learned from the study of highly effective tutors. In: Aronson, J. (ed.) Improving academic achievement: Impact of psychological factors on education, pp. 135-158. Academic Press, Orlando (2002)
14. Graesser, A.C., McDaniel, B., Chipman, P., Witherspoon, A., D'Mello, S., Gholson, B.: Detection Of Emotions During Learning With Autotutor. In: Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 285-290. Erlbaum, Mahwah (2006)
15. Ang, J., Dhillon, R., Krupski, A., Shriberg, E., Stolcke, A.: Prosody-Based Automatic Detection Of Annoyance And Frustration In Human-Computer Dialog. In: Proceedings of the International Conference on Spoken Language Processing, Denver, pp. 2037-2039 (2002)
16. Grimm, M., Mower, E., Kroschel, K., Narayan, S.: Combining Categorical and Primitives- Based Emotion Recognition. In: 14th European Signal Processing Conference (EUSIPCO), Florence, Italy (2006)
17. Litman, D.J., Forbes-Riley, K.: Predicting Student Emotions In Computer-Human Tutoring Dialogues. In: Proceedings Of The 42nd Annual Meeting Of The Association For Computational Linguistics. Association for Computational Linguistics, East Stroudsburg, PA, pp. 352-359 (2004)
18. Shafran, I., Riley, M., Mohri, M.: Voice signatures. In: Proceedings IEEE Automatic Speech Recognition and Understanding Workshop, pp. 31-36. IEEE, Piscataway (2003)
19. Robson, C.: Real word research: A resource for social scientist and practitioner researchers. Blackwell, Oxford (1993)
20. D'Mello, S.K., Craig, S.D., Sullins, J., Graesser, A.C.: Predicting Affective States Through An Emote-Aloud Procedure From Autotutor's Mixed-Initiative Dialogue. International Journal Of Artificial Intelligence In Education 16, 3-28 (2006)
21. McDaniel, B.T., D'Mello, S.K., King, B.G., Chipman, P., Tapp, K., Graesser, A.C.: Facial Features for Affective State Detection in Learning Environments. In: McNamara, D.S., Trafton, J.G. (eds.) Proceedings of the 29th Annual Cognitive Science Society, pp. 467- 472. Cognitive Science Society, Austin (2007)
22. Ekman, P., Friesen, W.V.: The facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto (1978)