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Trying to Reduce Bottom-Out Hinting: Will Telling Student How Many Hints They Have Left Help?

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Intelligent Tutoring Systems (ITS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5091))

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Many model-tracing intelligent tutoring systems give, upon demand, a series of hints until they reach the bottom-out hint that tells them exactly what to do (exact answer of the question). Since students don’t know the number of hints available for a given question, some students might be surprised to, all of a sudden, be told the final answer; letting them know when the bottom out hint is getting close should help cut down on the incidence of bottom-out hinting. We were interested in creating an intervention that would reduce the chance that a student would ask for the bottom out hint. Our intervention was straightforward; we simply told them the number of hints they had not yet seen so that they could see they were getting close to the bottom out hint. We conducted a randomized controlled experiment where we randomly assigned classrooms to conditions. Contrary to what we expected, our intervention led to more, not less, bottom out hinting. We conclude that the many intelligent tutoring systems that give hints in this manner should not consider this intuitively appealing idea.

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  1. VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence in Education 15(3), 1–47 (2005)

    Google Scholar 

  2. Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8, 30–43 (1997)

    Google Scholar 

  3. Baker, R.: Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model. In: Workshop on Data Mining for User Modeling, Educational Data Mining Track, at User Modeling, pp. 76–80 (2007)

    Google Scholar 

  4. Walonoski, J., Heffernan, N.T.: Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In: Ikeda, Ashley, Chan (eds.) Proceedings of the Eight International Conference on Intelligent Tutoring Systems, pp. 382–391 (2006a)

    Google Scholar 

  5. Lloyd, N., Heffernan, N.T., Ruiz, C.: Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. In: The Educational Data Mining Workshop held at the 13th Conference on Artificial Intelligence in Education, pp. 40–49 (2007)

    Google Scholar 

  6. Feng, M., Beck, J., Heffernan, N.T., Koedinger, K.R.: Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? In: The 1st International Annual Conference on Education Data Mining, Montreal (DRAFT) (submitted, 2008)

    Google Scholar 

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Beverley P. Woolf Esma Aïmeur Roger Nkambou Susanne Lajoie

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© 2008 Springer-Verlag Berlin Heidelberg

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Guo, Y., Beck, J.E., Heffernan, N.T. (2008). Trying to Reduce Bottom-Out Hinting: Will Telling Student How Many Hints They Have Left Help?. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds) Intelligent Tutoring Systems. ITS 2008. Lecture Notes in Computer Science, vol 5091. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69130-3

  • Online ISBN: 978-3-540-69132-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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