Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
This paper evaluates dialogue-based student performance in a controlled experiment using versions of a tutoring system with and without automatic adaptation to the student affective state of uncertainty. Our performance metrics include correctness, uncertainty, and learning impasse severities, which are measured in a “test” dialogue after the tutoring treatment. Although these metrics did not significantly differ across conditions when considering all student answers in our test dialogue, we found significant differences in specific types of student answers, and these differences suggest that our uncertainty adaptation does have a positive benefit on student performance.
Unable to display preview. Download preview PDF.
- 1.Workshop on Modeling and Scaffolding Affective Experiences to Impact Learning: Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education (AIED), Marina Del Ray, CA, Online proceedings (July 2007), http://www.informatics.sussex.ac.uk/users/gr20/aied07/index.html
- 2.Wang, N., Johnson, W., Rizzo, P., Shaw, E., Mayer, R.: Experimental evaluation of polite interaction tactics for pedagogical agents. In: Proceedings of Intelligent User Interface Conference (IUI), pp. 12–19 (2005)Google Scholar
- 3.Hall, L., Woods, S., Sobral, D., Paiva, A., Dautenhahn, K., Wolke, D., Newall, L.: Designing empathic agents: Adults vs. kids. In: Proceedings of the Intelligent Tutoring Systems Conference (ITS), Maceio, Brazil, pp. 604–613 (2004)Google Scholar
- 5.Pon-Barry, H., Schultz, K., Bratt, E.O., Clark, B., Peters, S.: Responding to student uncertainty in spoken tutorial dialogue systems. International Journal of Artificial Intelligence in Education 16, 171–194 (2006)Google Scholar
- 6.Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29(3), 241–250 (2004)Google Scholar
- 7.Bhatt, K., Evens, M., Argamon, S.: Hedged responses and expressions of affect in human/human and human/computer tutorial interactions. In: Proceedings of Cognitive Science (CogSci), Chicago, USA, pp. 114–119 (2004)Google Scholar
- 9.Kort, B., Reilly, R., Picard, R.: An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. In: Okamoto, T., Hartley, R., Kinshuk, J., Klus, P. (eds.) Proceedings IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges, Madison, WI, pp. 43–48 (2001)Google Scholar
- 10.Forbes-Riley, K., Litman, D., Silliman, S., Purandare, A.: Uncertainty corpus: Resource to study user affect in complex spoken dialogue systems. In: Proceedings 6th Language Resources and Evaluation Conference (LREC), Marrakech, Morocco (May 2008)Google Scholar
- 12.VanLehn, K., Jordan, P.W., Rosé, C.P., Bhembe, D., Böttner, M., Gaydos, A., Makatchev, M., Pappuswamy, U., Ringenberg, M., Roque, A., Siler, S., Srivastava, R., Wilson, R.: The architecture of Why2-Atlas: A coach for qualitative physics essay writing. In: Proceedings of Intelligent Tutoring Systems (2002)Google Scholar
- 14.Forbes-Riley, K., Litman, D.: Analyzing dependencies between student certainness states and tutor responses in a spoken dialogue corpus. In: Dybkjaer, L., Minker, W. (eds.) Recent Trends in Discourse and Dialogue, pp. 275–304. Springer (2008)Google Scholar
- 15.Burleson, W., Picard, R.: Affective agents: Sustaining motivation to learn through failure and a state of stuck. In: Social and Emotional Intelligence in Learning Environments Workshop at the Intelligent Tutoring Systems Conference (ITS), Maceio, Brazil (2004)Google Scholar