Evaluating a General Model of Adaptive Tutorial Dialogues

  • Amali Weerasinghe
  • Antonija Mitrovic
  • David Thomson
  • Pavle Mogin
  • Brent Martin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)

Abstract

Tutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively.

Keywords

adaptive tutorial dialogues constraint-based tutors Ill-defined tasks well-defined tasks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., Rose, C.P.: When are tutorial dialogues more effective than reading? Cognitive Science 31(1), 3–52 (2007)CrossRefGoogle Scholar
  2. 2.
    Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H.H., Ventura, M., Olney, A., et al.: AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments and Computers 36, 180–193 (2004)CrossRefGoogle Scholar
  3. 3.
    Evens, M., Michael, J.: One-on-One Tutoring By Humans and Computers. Lawrence Erlbaum Associates, Mahwah (2006)Google Scholar
  4. 4.
    Aleven, V., Ogan, A., Popescu, O., Torrey, C., Koedinger, K.: Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation. In: Lester, J., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 443–454. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Weerasinghe, A., Mitrovic, A.: Facilitating Deep Learning through Self-Explanation in an Open-ended Domain. Knowledge-based and Intelligent Tutoring Systems 10(1), 3–19 (2006)CrossRefGoogle Scholar
  6. 6.
    Weerasinghe, A., Mitrovic, A., Martin, B.: Towards Individualized Dialogue Support for Ill-Defined Domains IJAIED. Special Issue on Ill-Defined Domains 19(4), 357–379 (2009)Google Scholar
  7. 7.
    Mitrovic, A., Weerasinghe, A.: Revisiting the Ill-Definedness and Consequences for ITSs. In: Dimitrova, V., et al. (eds.) Proc. Artificial Intelligence in Education, Frontiers in Artificial Intelligence and Applications, vol. 200, pp. 375–382 (2009)Google Scholar
  8. 8.
    Mitrovic, A., Martin, B., Suraweera, P.: Intelligent Tutors for All: Constraint-based Modeling Methodology, Systems and Authoring. IEEE Intelligent Systems 22(4), 38–45 (2007)CrossRefGoogle Scholar
  9. 9.
    Elmasri, R., Navathe, S.: Fundamentals of Database Systems, 5th edn. Addison-Wesley, Boston (2007)MATHGoogle Scholar
  10. 10.
    Milik, N., Marshall, M., Mitrovic, A.: Teaching logical database design in ERM-Tutor. In: Ikeda, M., Ashley, K. (eds.) Proc. of ITS 2006, pp. 707–709 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amali Weerasinghe
    • 1
  • Antonija Mitrovic
    • 1
  • David Thomson
    • 1
  • Pavle Mogin
    • 2
  • Brent Martin
    • 1
  1. 1.Intelligent Computer Tutoring GroupUniversity of CanterburyNew Zealand
  2. 2.Victoria University of WellingtonWellingtonNew Zealand

Personalised recommendations