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Diagnosing Self-efficacy in Intelligent Tutoring Systems: An Empirical Study

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Book cover Intelligent Tutoring Systems (ITS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 4053))

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Abstract

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a students’ level of self-efficacy. This paper investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. In an empirical study, two families of self-efficacy models were induced: a static model, learned solely from pre-test (non-intrusively collected) data, and a dynamic model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. The resulting static model is able to predict students’ real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate.

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References

  1. André, E., Mueller, M.: Learning affective behavior. In: Proceedings of the 10th International Conference on Human-Computer Interaction, pp. 512–516. Lawrence Erlbaum, Mahwah (2003)

    Google Scholar 

  2. Bandura, A.: Exercise of personal and collective efficacy in changing societies. In: Bandura, A. (ed.) Self-efficacy in changing societies, pp. 1–45. Cambridge University Press, New York (1995)

    Chapter  Google Scholar 

  3. Bandura, A.: Self-efficacy: The exercise of control. Freeman, New York (1997)

    Google Scholar 

  4. Bandura, A.: Guide for constructing self-efficacy scales. Unpublished manuscript (2005)

    Google Scholar 

  5. Baylor, A.L., Kim, Y.: Pedagogical agent design: The impact of agent realism, gender, ethnicity, and instructional role. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 592–603. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Beal, C., Lee, H.: Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction. In: Workshop on Motivation and Affect in Educational Software, in conjunction with the 12th International Conference on Artificial Intelligence in Education (2005)

    Google Scholar 

  7. Burleson, W., Picard, R.: Affective agents: Sustaining motivation to learn through failure and a state of stuck. In: Workshop of Social and Emotional Intelligence in Learning Environments, in conjunction with the 7th International Conference on Intelligent Tutoring Systems (2004)

    Google Scholar 

  8. Conati, C., Maclaren, H.: Data-driven refinement of a probabilistic model of user affect. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS, vol. 3538, pp. 40–49. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Delcourt, M., Kinzie, M.: Computer technologies in teacher education: the measurement of attitudes and self-efficacy. Journal of Research and Development in Education 27(1), 35–41 (1993)

    Google Scholar 

  10. Graham, S., Weiner, B.: Principles and theories of motivation. In: Berliner, D., Calfee, R. (eds.) Handbook of educational psychology, pp. 63–84. MacMillan Publishing, New York (1996)

    Google Scholar 

  11. Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Journal of Cognitive Systems Research 5(4), 269–306 (2004)

    Article  Google Scholar 

  12. Hanley, J., McNeil, B.: The meaning and use of the area under the Receiver Operating Characteristic (ROC) curve. Radiology 143, 29–36 (1982)

    Google Scholar 

  13. Johnson, W.L., Rizzo, P.: Politeness in tutoring dialogs: “Run the factory, that’s what i’d do”. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 67–76. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Kim, Y.: Empathetic virtual peers enhanced learner interest and self-efficacy. In: Workshop on Motivation and Affect in Educational Software, in conjunction with the 12th International Conference on Artificial Intelligence in Education (2005)

    Google Scholar 

  15. 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, pp. 75–105. Erlbaum, Hillsdale (1993)

    Google Scholar 

  16. Lester, J., Towns, S., FitzGerald, P.: Achieving affective impact: Visual emotive communication in lifelike pedagogical agents. The International Journal of Artificial Intelligence in Education 10(3-4), 278–291 (1999)

    Google Scholar 

  17. Padilla, M., Miaoulis, I., Cyr, M.: Science Explorer: Cells and Heredity. Teacher’s Edition. Prentice Hall, Upper Saddle River (2000)

    Google Scholar 

  18. Paiva, A., Dias, J., Sobral, D., Aylett, R., Woods, S., Hall, L., Zoll, C.: Learning by feeling: Evoking empathy with synthetic characters. Applied Artificial Intelligence 19, 235–266 (2005)

    Article  Google Scholar 

  19. Pajares, F., Kranzler, J.: Self-Efficacy beliefs and general mental ability in mathematical problem solving. Contemporary Educational Psychology 20, 426–443 (1995)

    Article  Google Scholar 

  20. Porayska-Pomsta, K., Pain, H.: Providing Cognitive and Affective Scaffolding Through Teaching Strategies: Applying Linguistic Politeness to the Educational Context. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 77–86. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  21. Prendinger, H., Ishizuka, M.: The empathic companion: A character-based interface that addresses users affective states. Applied Artificial Intelligence 19, 267–285 (2005)

    Article  Google Scholar 

  22. Schunk, D., Pajares, F.: The development of academic self-efficacy. In: Wigfield, A., Eccles, J. (eds.) Development of achievement motivation, pp. 15–31. Academic Press, San Diego (2002)

    Chapter  Google Scholar 

  23. Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufman, San Francisco (2005)

    MATH  Google Scholar 

  24. de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: An empirical study. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 933–943. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  25. Zimmerman, B.: Self-efficacy: an essential motive to learn. Contemporary Educational Psychology 25, 82–91 (2000)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, North Carolina State University, Raleigh, NC, 27695, USA

    Scott W. McQuiggan & James C. Lester

Authors
  1. Scott W. McQuiggan
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  2. James C. Lester
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Editor information

Editors and Affiliations

  1. JAIST, 1-1, Asahi-dai, Nomi, 923-1292, Ishikawa, Japan

    Mitsuru Ikeda

  2. Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA

    Kevin D. Ashley

  3. Graduate Institute of Network Learning Technology, National Central University, 300, Jhongda Rd., 32001, Jhongli City,Taoyuan County, Taiwan

    Tak-Wai Chan

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© 2006 Springer-Verlag Berlin Heidelberg

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McQuiggan, S.W., Lester, J.C. (2006). Diagnosing Self-efficacy in Intelligent Tutoring Systems: An Empirical Study. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_56

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  • DOI: https://doi.org/10.1007/11774303_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35159-7

  • Online ISBN: 978-3-540-35160-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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