Eliciting Adaptation Knowledge from On-Line Tutors to Increase Motivation
In the classroom, teachers know how to motivate their students and how to exploit this knowledge to adapt or optimize their instruction when a student shows signs of demotivation. In on-line learning environments it is much more difficult to assess a learner’s motivation and to have adaptive intervention strategies and rules of application to help prevent attrition or drop-out. In this paper, we present results from a survey of on-line tutors on how they motivate their learners. These results will inform the development of an adaptation engine by extracting and validating selection rules for strategies to increase motivation depending on the learner’s self-efficacy, goal orientation, locus of control and perceived task difficulty in adaptive Intelligent Tutoring Systems.
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- 1.Bandura, A.: Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Englewood Cliffs, NJ (1986)Google Scholar
- 2.Beal, C.R., 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, July 18-22, Amsterdam, Netherlands. Retrieved on 23 March 2006 ( 2005), from http://www.wayangoutpost.net/paper/Beal&LeeCRC.pdf
- 3.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 and Communication Technologies in Education m-ICTE ( 2003)Google Scholar
- 5.Pajares, F., Schunk, D.H.: Self-Beliefs and School Success: Self-Efficacy, Self-Concept, and School Achievement. In: Riding, R., Rayner, S. (eds.) Perception, pp. 239–266. Ablex Publishing, London (2001)Google Scholar
- 7.Pintrich, P.R., Garcia, T.: Student goal orientation and self-regulation in the college classroom. In: Maehr, M.L., Pintrich, P.R. (eds.) Advances in motivation and achievement: Goals and self-regulatory processes, vol. 7, pp. 371–402. JAI Press, Greenwich, CT (1991)Google Scholar
- 8.Pintrich, P.R., Schunk, D.H.: Motivation in education: Theory, research, and practice. Prentice Hall, Englewood Cliffs, NJ (1996)Google Scholar
- 9.Qu, L., Wang, N., Johnson, W.L.: Detecting the Learner’s Motivational States in an Interactive Learning Environment. In: Looi, C.-K., et al. (eds.) Artificial Intelligence in Education, pp. 547–554. IOS Press, Amsterdam, Trento, Italy (2005)Google Scholar
- 10.Rotter, J.B.: Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80(Whole No. 609) ( 1966)Google Scholar
- 11.Witten, I.H., Frank, E., Trigg, L.E., Hall, M., Holmes, G., Cunningham, S.J: Weka: Practical machine learning tools and techniques with Java implementations. In: Proc ICONIP/ ANZIIS/ANNES99 Future Directions for Intelligent Systems and Information Sciences, Dunedin, New Zealand, pp. 192–196 (November 1999)Google Scholar
- 12.Zhang, G., Cheng, Z., He, A., Huang, T.: A WWW-based Learner’s Learning Motivation Detecting System. In: Proceedings of International Workshop on Research Directions and Challenge Problems in Advanced Information Systems Engineering, Honjo City, Japan, (September 16–19, 2003), http://www.akita-pu.ac.jp/system/KEST2003/