An Evaluation of Self-explanation in a Programming Tutor

  • Amruth N. Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


A controlled study was conducted in-natura to evaluate the effectiveness of presenting passive self-explanation questions in a problem-solving tutor on code-tracing. Data was collected from multiple institutions over three semesters using a tutor on selection statements: fall 2012-fall 2013. ANOVA and ANCOVA were used to analyze the collected data. After accounting for the additional time provided to test group students to answer self-explanation questions, test group was found to fare no better than control group on the number of concepts practiced, the pre-post change in score or the number of practice problems solved per practiced concept. It is speculated that this lack of difference might be attributable to self-efficacy issues, and that the features of tutors found to be effective in-vivo might need self-efficacy supports to also be effective in-natura.


Self-explanation Programming tutor Evaluation Self-efficacy 


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  1. 1.
    Aleven, V., Ogan, A., Popescu, O., Torrey, C., Koedinger, K.: Evaluating the effectiveness of a tutorial dialogue system for self-explanation. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 443–454. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Aleven, V.A., Koedinger, K.R.: An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science 26(2), 147–179 (2002)CrossRefGoogle Scholar
  3. 3.
    Atkinson, R.K., Derry, S.J., Renkl, A., Wortham, D.: Learning From Examples: Instructional Principles from the Worked Examples Research. Review of Educational Research 70, 181–214 (2000)CrossRefGoogle Scholar
  4. 4.
    Bandura, A.: Self-efficacy: Towards a unifying theiry of behavioral change. Psychological Review 84(2), 191–215 (1977)CrossRefGoogle Scholar
  5. 5.
    Butcher, K.R.: Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology 98(1), 182–197 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chi, M.T., De Leeuw, N., Chiu, M.H., LaVancher, C.: Eliciting self-explanations improves understanding. Cognitive Science 18(3), 439–477 (1994)Google Scholar
  7. 7.
    Chi, M.T., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R.: Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science 13(2), 145–182 (1989)CrossRefGoogle Scholar
  8. 8.
    Davis, R.: Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence 24, 347–410 (1984)CrossRefGoogle Scholar
  9. 9.
    Hausmann, R.G.M., VanLehn, K.: The Effect of Self-Explaining on Robust Learning. International Journal of Artificial Intelligence in Education 20(4), 303–332 (2011)Google Scholar
  10. 10.
    Hausmann, R.G., Nokes, T.J., VanLehn, K., van de Sande, B.: Collaborative dialog while studying worked-out examples. In: Proceedings of the Artificial Intelligence in Education 2009 Conference (July 2009)Google Scholar
  11. 11.
    Kumar, A.N.: Promoting Reflection and its Effect on Learning in a Programming Tutor. In: Proceedings of 22nd International FLAIRS Conference on Artificial Intelligence (FLAIRS 2009) Special Track on Intelligent Tutoring Systems, Sanibel Island, FL, May 19-21, pp. 454–459 (2009)Google Scholar
  12. 12.
    Lehman, B., Mills, C., D’Mello, S., Graesser, A.: Automatic evaluation of learner self-explanations and erroneous responses for dialogue-based ITSs. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 541–550. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    McNamara, D.S., Boonthum, C., Kurby, C.A., Magliano, J., Pillarisetti, S., Bellissens, C.: Interactive paraphrase training: The development and testing of an iSTART module. In: Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modeling, pp. 181–188. IOS Press (July 2009)Google Scholar
  14. 14.
    McNamara, D.S., Levinstein, I.B., Boonthum, C.: iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers 36, 222–233 (2004)CrossRefGoogle Scholar
  15. 15.
    Rau, M.A., Aleven, V., Rummel, N.: Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. In: Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, pp. 441–448. IOS Press (July 2009)Google Scholar
  16. 16.
    Webb, N.M.: Peer interaction and learning in small groups. International Journal of Educational Research 13(1), 21–39 (1989)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amruth N. Kumar
    • 1
  1. 1.Ramapo College of New JerseyMahwahUSA

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