Generating Tailored Examples to Support Learning via Self-explanation

We describe a framework that helps students learn from examples by generating example problem solutions whose level of detail is tailored to the students' domain knowledge. The framework uses natural language generation techniques and a probabilistic student model to selectively introduce gaps in the example solution, so that the student can practice applying rules learned from previous examples in problem solving episodes of difficulty adequate to her knowledge. Filling in solution gaps is part of the meta-cognitive skill known as selfexplanation (generate explanations to oneself to clarify an example solution), which is crucial to effectively learn from examples. In this paper, we describe how examples with tailored solution gaps are generated and how they are used to support students in learning through gap-filling self-explanation.

• Type: Conference
• Year: 2001
• Where: IJCAI
• Authors: Cristina Conati, Giuseppe Carenini
• Keywords: Example Solution, IJCAI 2001, IJCAI 2007, Solution Gaps, Tailored Solution Gaps