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Affective Tutors: Automatic Detection of and Response to Student Emotion

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Advances in Intelligent Tutoring Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 308))

Abstract

This chapter describes the automatic recognition of and response to human emotion within intelligent tutors. Tutors can recognize student emotion with more than 80%accuracy compared to student self-reports, using wireless sensors that provide data about posture, movement, grip tension, facially expressed mental states and arousal. Pedagogical agents have been used that provide emotional or motivational feedback. Students using such agents increased their math value, self-concept and mastery orientation, with females reporting more confidence and less frustration. Low-achieving students—one third of whom have learning disabilities—report higher affective needs than their higher-achieving peers. After interacting with affective pedagogical agents, low-achieving students improved their affective outcomes and reported reduced frustration and anxiety.

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References

  • Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion Sensors Go To School. In: International Conference on Artificial Intelligence and Education, pp. 17–24. IOS Press, Amsterdam (2009)

    Google Scholar 

  • Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Mehranian, H., Fisher, D., Barto, A., Mahadevan, S., Woolf, B.: Repairing Disengagement With Non Invasive Interventions. In: International Conference on Artificial Intelligence in Education. IOS Press, Marina del Rey (2007)

    Google Scholar 

  • Arroyo, I., Woolf, B., Royer, J.M., Tai, M.: Affective Gendered Learning Companions. In: Inter. Conference on Artificial Intelligence in Education. IOS Press, England (2009)

    Google Scholar 

  • Arroyo, I., Woolf, B.P.: Inferring learning and attitudes from a Bayesian Network of log file data, pp. 33–40. IOS Press AIED, Amsterdam (2005)

    Google Scholar 

  • Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-Task Behavior in the Cognitive Tutor Classroom: When Students Game The System. In: Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383–390 (2004)

    Google Scholar 

  • Bandura, A.: Self-efficacy: The Exercise of Control. Freeman, New York (1997)

    Google Scholar 

  • Baylor, A.: The Impact of Pedagogical Agent Image on Affective Outcomes. In: Proceedings of Workshop on Affective Interactions: Computers in the Affective Loop, San Diego (2005)

    Google Scholar 

  • Beal, C.: Boys and girls: The development of gender roles. McGraw-Hill, New York (1994)

    Google Scholar 

  • Bickmore, T., Picard, R.W.: Establishing and Maintaining Long-Term Human-Computer Relationships. Transactions on Computer-Human Interaction 12, 293–327 (2004)

    Article  Google Scholar 

  • Brand, S., Reimer, T., Opwis, K.: How do we learn in a negative mood? Effect of a negative mood on transfer and learning. Learning and Instruction 17, 1–16 (2007)

    Article  Google Scholar 

  • Burleson, W.: Affective Learning Companions: Strategies for Empathetic Agents with Real-Time Multimodal Affective Sensing to Foster Meta-Cognitive Approaches to Learning, Motivation, and Perseverance. MIT PhD thesis (2006), http://affect.media.mit.edu/pdfs/06.burleson-phd.pdf (Acesses May 14, 2010)

  • Burleson, W., Picard, R.W.: Gender-specific approaches to developing emotionally intelligent learning companions. IEEE Intelligent Systems 22, 62–69 (2007)

    Google Scholar 

  • Carr, M., Jessup, D.: Gender Differences in First-Grade Mathematics Strategy Use: Social and Metacognitive Influences. Journal of Educational Psychology 89(2), 318–328 (1997)

    Article  Google Scholar 

  • Chan, T.W., Baskin, A.: Learning companion system. In: Frasson, C., Gauthier, G. (eds.) Intelligent Tutoring Systems. Ablex Publishing, Norwood (1990)

    Google Scholar 

  • Conati, C., Mclaren, H.: Evaluating A Probabilistic Model of Student Affect. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 55–66. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  • Cooper, D.G., Arroyo, I., Woolf, B.P., Muldner, K., Burleson, W., Christopherson, R.: Sensors Model Student Self Concept in the Classroom. In: International Conference on User Modeling and Adaptive Presentation, pp. 30–41 (2009)

    Google Scholar 

  • D’Mello, S.K., Picard, R.W., Graesser, A.C.: Towards an Affect-Sensitive AutoTutor. Special issue on Intelligent Educational Systems IEEE Intelligent Systems 22(4), 53–61 (2007)

    Google Scholar 

  • D’Mello, S.K., Graesser, A.: Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments. In: Frontiers in Artificial Intelligence and Applications Conference (2007)

    Google Scholar 

  • Dennerlein, J., Becker, T., Johnson, P., Reynolds, C., Picard, R.W.: Frustrating computer users increases exposure to physical factors. In: Proceedings of International Ergonomics Association, Seoul, Korea, pp. 24–29 (2003)

    Google Scholar 

  • Dweck, C.: Messages that motivate: How praise molds students’ beliefs, motivation, and performance (In Surprising Ways). In: Aronson, J. (ed.) Improving academic achievement. Academic Press, New York (2002)

    Google Scholar 

  • Efklides, A., Petkakim, C.: Effects of Mood on students’ metacogntiive experience. Learning and Instruction 15, 415–431 (2005)

    Article  Google Scholar 

  • El Kaliouby, R.: Mind-reading Machines: the automated inference of complex mental states from video. PhD thesis, University of Cambridge (2005)

    Google Scholar 

  • Fennema, E., Carpenter, T., Jacobs, V., Franke, M., Levi, L.: A Longitudinal Study of Gender Differences in Young Children’s Mathematical Thinking. Educational Researcher 27(5), 6–11 (1998)

    Google Scholar 

  • Fletcher, J., Lyon, G., Fuchs, L., Barnes, M.: Learning disabilities: From identification to intervention. NY (2007)

    Google Scholar 

  • Geary, D., Hoard, M., Byrd-Craven, J., Nugent, L., Numtee, C.: Cognitive Mechanisms Underlying Achievement Deficits in Children with Mathematical Learning Disability. Child Development 78(4), 1343–1359 (2007)

    Article  Google Scholar 

  • Geary, D., Hoard, M., Hamson, C.: Numerical and arithmeticalcognition: Patterns of functions and deficits in children at risk for a mathematicaldisability. Journal of Experimental ChildPsychology 74, 213–239 (1999)

    Google Scholar 

  • Goleman, D.: Emotional Intelligence. Bantam Books, New York (1995)

    Google Scholar 

  • Graesser, A., Chipman, P., King, B., McDaniel, B., D’Mello, S.: Emotions and Learning with AutoTutor. In: Luckin, R., Koedinger, K., Greer, J. (eds.) 13th International Conference on Artificial Intelligence in Education. IOS Press, Amsterdam (2007)

    Google Scholar 

  • Graham, S., Weiner, B.: Theories and principles of motivation. In: Berliner, D., Calfee, R. (eds.) Handbook of Educational Psychology. Macmillan, New York (1996)

    Google Scholar 

  • Kapoor, A., Burleson, W., Picard, R.: Automatic prediction of frustration. International Journal of Human-Computer Studies 65(8), 724–736 (2007)

    Article  Google Scholar 

  • Klein, J., Moon, Y., Picard, R.: This Computer Responds to User Frustration: Theory, Design, Results, and Implications. Interacting with Computers 14(2), 119–140 (2002)

    Google Scholar 

  • Lepper, M., Woolverton, M., Mumme, D., Gurtner, J.: Motivational techniques of expert human tutors: lessons for the design of computer-based tutors. In: Lajoie, S., Derry, S. (eds.) Computers as Cognitive Tools. Erlbaum, Mahwah (1993)

    Google Scholar 

  • McQuiggan, S., Lester, J.: Diagnosing Self-Efficacy in Intelligent Tutoring Systems: An Empirical Study. In: Ikeda, M., Ashley, K., Chan, T. (eds.) ITS 2006. LNCS, vol. 4053, pp. 565–574. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  • McQuiggan, S., Mott, B., Lester, J.: Modeling self efficacy in intelligent tutoring systems: An inductive approach. User Modeling and User-Adapted Interaction 18(1), 81–123 (2008)

    Article  Google Scholar 

  • Mota, S., Picard, R.: Automated posture analysis for detecting learner’s interest level. In: Computer Vision and Pattern Recognition Workshop (2003)

    Google Scholar 

  • NCES, National Center for Educational Statistics, Digest of Educational Statistics, ch. 2 (2009), http://nces.ed.gov/fastfacts/display.asp?id=64

  • Picard, R., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective Learning–A Manifesto. BT Journal 2(4), 253–269 (2004)

    Article  Google Scholar 

  • Picard, R., Scheirer, J.: The galvactivator: A glove that senses and communicates skin conductivity. In: 9th International Conference on Human-Computer Interaction, pp. 1538–1542 (2001)

    Google Scholar 

  • Prendinger, H., Ishizuka, M.: The Empathic Companion: A Character-Based Interface that Addresses Users’ Affective States. Applied Artificial Intelligence 19(3-4), 267–285 (2005)

    Article  Google Scholar 

  • Qi, Y., Picard, R.: Context-sensitive bayesian classifiers and application to mouse pressure pattern classification. Pattern Recognition 3, 448–451 (2002)

    Google Scholar 

  • Reeves, B., Nass, C.: The media equation: How people treat computers, television and new media like real people and places. CSLI, New York (1998)

    Google Scholar 

  • Robertson, J.: Is Attribution Training a Worthwhile Classroom Intervention For K–12 Students with Learning Difficulties? Education Psychology Review 12(1), 111–134 (2000)

    Article  Google Scholar 

  • Royer, J., Walles, R.: Influences of gender, motivation and socioeconomic status on mathematics performance. In: Berch, D., Mazzocco, M. (eds.) Why is math so hard for some children. Paul H. Brookes Publishing Co., MD (2007)

    Google Scholar 

  • Sax, L.: Why Gender Matters: What Parents and Teachers Need to Know about the Emerging Science of Sex Differences. Doubleday (2005)

    Google Scholar 

  • Snow, R., Corno, L., Jackson, D.: Individual differences in affective and cognitive functions. In: Berliner, D., Calfee, R. (eds.) Handbook of Educational Psychology. McMillan, New York (1996)

    Google Scholar 

  • Strauss, M., Reynolds, C., Hughes, S., Park, K., McDarby, G., Picard, R.: The handwave bluetooth skin conductance sensor. Affective Computing and Intelligent Interaction 2005, 699–706 (2005)

    Google Scholar 

  • Weiner, B.: An attributional theory of motivation and emotion. Springer, New York (1986)

    Google Scholar 

  • Bernard, W.: Attribution Theory, Achievement Motivation, and the Educational. Review of Educational Research 42(2), 203–215 (1972)

    MathSciNet  Google Scholar 

  • Wentzel, K., Asher, S.: Academic lives of neglected, rejected, popular, and controversial children. Child Development 66, 754–763 (1995)

    Article  Google Scholar 

  • Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Picard, R.: Emotional Intelligence for Computer Tutors. In: el Kaliouby, R., Craig, S. (eds.) Special Issue on Modeling and Scaffolding Affective Experiences to Impact Learning, International Journal of Learning Technology (2009)

    Google Scholar 

  • Woolf, B., Arroyo, I., Muldner, K., Burleson, W., Cooper, D., Dolan, R., Christopherson, R.: The Effect of Motivational Learning Companions on Low-Achieving Students and Students with Learning Disabilities. In: International Conference on Intelligent Tutoring Systems, Pittsburgh (2010)

    Google Scholar 

  • Zimmerman, B.: Self-Efficacy: An Essential Motive to Learn. Contemporary Educational Psychology 25, 82–91 (2000)

    Article  Google Scholar 

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Woolf, B.P., Arroyo, I., Cooper, D., Burleson, W., Muldner, K. (2010). Affective Tutors: Automatic Detection of and Response to Student Emotion. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds) Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14363-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-14363-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

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