Raising Confidence Levels Using Motivational Contingency Design Techniques

  • Declan Kelly
  • Stephan Weibelzahl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

Motivation plays a key role in learning and teaching, in particular in technology enhanced learning environments. According to motivational theories, proper contingency design is an important prerequisite to motivate learners. In this paper, we demonstrate how confidence levels in an adaptive educational system can be raised using a contingency design technique. Learners that saw parts of a complete picture depending on their performance were more confident to solve the next task than learners who did not. Results suggest that it is possible to raise confidence levels of learners through appropriate contingency design and thus to automatically adapt to their motivational states.

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References

  1. 1.
    Ames, C.: Classroom goals, structures, and student motivation. Journal of Educational Psychology 84(3), 261–271 (1992)CrossRefGoogle Scholar
  2. 2.
    Bandura, A.: Self-efficacy: The exercise of control. W.H. Freeman, New York (1997)Google Scholar
  3. 3.
    Beck, J.E.: Using response times to model student disengagement. Proceedings of Workshop on Social and Emotional Intelligence in Learning Environments. Held at International Conference on Intelligent Tutoring Systems, ITS 2004, Maceio, Brazil, August 31 (2004)Google Scholar
  4. 4.
    Beffa-Negrini, P.A., Cohen, N.L., Miller, B.: Strategies to motivate students in online learning environments. Journal of Nutrition Education and Behavior 34, 334–340 (2002)CrossRefGoogle Scholar
  5. 5.
    Carroll, K.: Sing a Song of Science. Zephyr Press, Chicago (1999)Google Scholar
  6. 6.
    Corno, L.: Conative individual differences in learning. In: Collis, J.M., Messick, S. (eds.) Intelligence and Personality: Bridging the gap in theory and measurement, pp. 121–138. Lawrence Erlbaum, Mahwah (2001)Google Scholar
  7. 7.
    Freud, S.: Beyond the pleasure principle. W. W. Norton & Company, New York (1990)Google Scholar
  8. 8.
    Gardner, H.: Frames of Mind: The theory of multiple intelligences. Basic Books, New York (1983)Google Scholar
  9. 9.
    Georgouli, K.: The Design of a ‘Motivating’ Intelligent Assessment System. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 811–820. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Jones, K.: Running, or stumbling through, simulations. Simulation/Games for Learning 19(4), 160–167 (1989)Google Scholar
  11. 11.
    Keller, J.M.: Motivation and instructional design: A theoretical perspective. Journal of Instructional Development 2(4), 26–34 (1979)CrossRefGoogle Scholar
  12. 12.
    Keller, J.M.: Motivational design of instruction. In: Reigeluth, C.M. (ed.) Instructional Theories and Models: An Overview of Their Current Status, pp. 383–434. Lawrence Erlbaum, New York (1983)Google Scholar
  13. 13.
    Keller, J.M.: Using the ARCS Motivational Process in Computer-Based Instruction and Distance Education. In: Theall, M. (ed.) Motivation in Teaching and Learning: New Directions for Teaching and Learning. Jossey-Bass, San Francisco (1999)Google Scholar
  14. 14.
    Kelly, D.: A Framework for using Multiple Intelligences in an ITS. In: Proceedings of EDMedia 2003, World Conference on Educational Multimedia, Hypermedia & Telecommunications, Honolulu, HI (2003)Google Scholar
  15. 15.
    Kelly, D., Tangney, B.: Predicting Learning Characteristics in a Multiple Intelligence Based Tutoring System. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 678–688. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Kelly, D., Tangney, B.: Matching and Mismatching Learning Characteristics with Multiple Intelligence Based Content. In: The Twelveth International Conference on Artificial Intelligence in Education, AIED 2005, Amsterdam, Netherlands, pp. 354–361. Reigeluth, C.M (2005); A new paradigm of ISD? Educational Technology, 36(3) (1996)Google Scholar
  17. 17.
    Kleinginna, P., Kleinginna, A.: A categorized list of motivation definitions, with suggestions for a consensual definition. Motivation and Emotion 5, 263–291 (1981)CrossRefGoogle Scholar
  18. 18.
    Lazaer, D.: Eight Ways of Teaching: The Artistry of Teaching with Multiple Intelligences, SkyLight (1999)Google Scholar
  19. 19.
    Lepper, M.R., Hodell, M.: Intrinsic motivation in the classroom. In: Ames, C., Ames, R. (eds.) Research on motivation in education, vol. 3, pp. 73–105. Academic Press, San Diego (1989)Google Scholar
  20. 20.
    Malone, F.: Toward a theory of intrinsically motivation instruction. Cognitive Science 5(4), 333–369 (1980)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Martens, R., Gulikers, J., Bastaens, T.: The impact of intrinsic motivation on e-learning in authentic computer tasks. Journal of Computer Assisted Learning 20(5), 368–376 (2004)CrossRefGoogle Scholar
  22. 22.
    Maslow, A.: Motivation and personality. Harper, New York (1954)Google Scholar
  23. 23.
    Militiadou, M., Savenye, W.: Applying social cognitive constructs of motivation to enhance student success in online distance education. AACE Journal 11(1), 78–95 (2003)Google Scholar
  24. 24.
    Qu, L., Johnson, W.L.: Detecting the Learner’s Motivational States in an Interactive Learning Environment. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED 2005, pp. 547–554. IOS Press, Amsterdam (2005)Google Scholar
  25. 25.
    Skinner, B.F.: Contingencies of reinforcement: A theoretical analysis. Appleton-Century-Crofts, New York (1969)Google Scholar
  26. 26.
    Stipek, D.J.: Motivation to learn: From Theory to practice. Allyn and Bacon, Needham Heights (1993)Google Scholar
  27. 27.
    del Soldato, T.: Detecting and Reacting to the Learner’s Motivational State. In: Frasson, C., McCalla, G.I., Gauthier, G. (eds.) ITS 1992. LNCS, vol. 608, pp. 567–574. Springer, Heidelberg (1992)Google Scholar
  28. 28.
    Song, S.H., Keller, J.M.: Effectiveness of motivationally-adaptive CAI. Educational Technology Research & Development 49(2), 5–22 (2001)CrossRefGoogle Scholar
  29. 29.
    Terrell, S., Rendulic, P.: Using computer-managed instructional software to increase motivation and achievement in elementary school children. Journal of Research on Computing in Education 28(3), 403–414 (1996)Google Scholar
  30. 30.
    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)CrossRefGoogle Scholar
  31. 31.
    de Vicente, A., Pain, H.: Validating the Detection of a Student’s Motivational State. In: Mendez Vilas, A., Mesa Gonzalez, J.A., Mesa Gonzalez, J. (eds.) Proceedings of the Second International Conference on Multimedia Information & Communication Technologies in Education (m-ICTE2003), pp. 2004–2008 (2003)Google Scholar
  32. 32.
    Vroom, V.: Work and motivation. Wiley, New York (1964)Google Scholar
  33. 33.
    Waugh, R.: Creating a scale to measure motivation to achieve academically: Linking attitudes and behavior using Rasch measurement. British Journal of Educational Psychology 72, 65–86 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Declan Kelly
    • 1
  • Stephan Weibelzahl
    • 1
  1. 1.National College of IrelandIreland

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