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From Example Studying to Problem Solving via Tailored Computer-Based Meta-Cognitive Scaffolding: Hypotheses and Design
Cristina Conati, Kasia Muldner and Giuseppe Carenini

We present an intelligent tutoring framework designed to help students acquire problem-solving skills from pedagogical activities involving worked-out example solutions. Because of individual differences in their meta-cognitive skills, there is great variance in how students learn from examples. Our framework takes into account these individual differences and provides tailored support for the application of two key meta-cognitive skills: self-explanation (i.e., generating explanations to oneself to clarify studied material) and min-analogy (i.e., not relying too heavily on examples during problem solving). We describe the framework’s two components. One component explicitly scaffolds self-explanation during example studying with menu-based tools and direct tailored tutorial interventions, including the automatic generation of example solutions at varying degrees of detail. The other component supports both self-explanation and min-analogy during analogical problem solving by relying on subtler scaffolding, including a highly innovative example selection mechanism. We conclude by reporting results from an empirical evaluation of the former component, showing that it facilitates cognitive skill acquisition when students access it at the appropriate learning stage.

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