An Evaluation of Self-explanation in a Programming Tutor
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.
KeywordsSelf-explanation Programming tutor Evaluation Self-efficacy
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- 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
- 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.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.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.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.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
- 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