Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy

• Authors: Min Chi;Kurt Vanlehn
• Venue: Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
• Year: 2007

Explicit instruction in a problem-solving strategy accelerated learning not only in the domain where it was taught but also in a second domain where it was not taught. We present data from a study in which students learned two unrelated deductive domains: probability and physics. During the probability instruction, the Strategy group was trained with an Intelligent Tutoring System (ITS) that explicitly taught a domain-independent backward chaining problem-solving strategy while the No-strategy groups trained with another ITS without any explicit strategy instruction. During the subsequent physics instruction, both groups were trained with the same ITS, which did not explicitly teach any strategy. The Strategy group gained significantly more than the No-strategy group in both domains. Moreover, their gains were evident both on multiple-principle problems, where the strategy should make problem solving more efficient, and on single-principle ones, where the strategy should make no difference. This suggests that the strategy increased the students' learning of domain principles and concepts, because that is all they had to know in order to solve the single-principle problems.