The present paper analyzes the self-generated explanations (from talk-aloud protocols) that "Good" and "Poor" students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that "Good" students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self-explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example-independent knowledge and in a better understanding of the principles presented in the text. "Poor" students do not generate sufficient self-explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self-explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation-based learning to account for these psychological findings.
ASJC Scopus subject areas
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence