Abstract
Is learning by solving problems better than learning from worked-out examples? Using a machine-learning program that learns cognitive skills from examples, we have conducted a study to compare three learning strategies: learning by solving problems with feedback and hints from a tutor, learning by generalizing worked-out examples exhaustively, and learning by generalizing worked-out examples only for the skills that need to be generalized. The results showed that learning by tutored problem solving outperformed other learning strategies. The advantage of tutored problem solving was mostly due to the error detection and correction that was available only when skills were applied incorrectly. The current study also suggested that learning certain kinds of conditions to apply rules only for appropriate situations is quite difficult.
The research presented in this paper is supported by the National Science Foundation Award No. REC-0537198. This work was also supported in part by the Pittsburgh Science of Learning Center, which is funded by the National Science Foundation Award No. SBE-0354420.
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Matsuda, N., Cohen, W.W., Sewall, J., Lacerda, G., Koedinger, K.R. (2008). Why Tutored Problem Solving May be Better Than Example Study: Theoretical Implications from a Simulated-Student Study. 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. https://doi.org/10.1007/978-3-540-69132-7_16
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DOI: https://doi.org/10.1007/978-3-540-69132-7_16
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