Abstract
The relationship between emotions and learning was investigated by tracking the affective states that college students experiencedwhile interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately identified. After a learning session with AutoTutor, the affective states of the learner were classified by the learner and two accomplished teachers. The classification of the teachers was not very reliable and did not match the learners self reports. This result suggests that accomplished teachers may be limited in detecting the affective states of learners. This paper discusses the implications of our findings for theories of expert tutoring and for alternate methodologies for establishing convergent validity of affect measurement.
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References
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)
Goleman, D.: Emotional intelligence. Bantam Books, New York (1995)
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)
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)
Meyer, D.K., Turner, J.C.: Reconceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review 18, 377–390 (2006)
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)
Mandler, G.: Mind and emotion. Wiley, New York (1976)
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)
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)
Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper-Row, New York (1990)
D’Mello, S.K., Picard, R., Graesser, A.C.: Towards an Affect Sensitive AutoTutor. IEEE Intelligent Systems 22(4), 53–61 (2007)
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)
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)
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)
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)
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)
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)
Shafran, I., Riley, M., Mohri, M.: Voice signatures. In: Proceedings IEEE Automatic Speech Recognition and Understanding Workshop, pp. 31–36. IEEE, Piscataway (2003)
Robson, C.: Real word research: A resource for social scientist and practitioner researchers. Blackwell, Oxford (1993)
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)
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)
Ekman, P., Friesen, W.V.: The facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto (1978)
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D’Mello, S., Taylor, R., Davidson, K., Graesser, A. (2008). Self Versus Teacher Judgments of Learner Emotions During a Tutoring Session with AutoTutor. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds) Intelligent Tutoring Systems. ITS 2008. Lecture Notes in Computer Science, vol 5091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69132-7_6
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DOI: https://doi.org/10.1007/978-3-540-69132-7_6
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