Goals and learning in microworlds

We explored the consequences for learning through interaction with an educational microworld called Electric Field Hockey (EFH). Like many microworlds, EFH is intended to help students develop a qualitative understanding of the target domain, in this case, the physics of electrical interactions. Through the development and use of a computer model that learns to play EFH, we analyzed the knowledge the model acquired as it applied the game-oriented strategies we observed physics students using. Through learning-by-doing on the standard sequence of tasks, the model substantially improved its EFH playing ability; however, it did so without acquiring any new qualitative physics knowledge. This surprising result led to an experiment that compared students’ use of EFH with standard-goal tasks against two alternative instructional conditions, specific-path and no-goal, each justified from a different learning theory. Students in the standard-goal condition learned less qualitative physics than did those in the two alternative conditions, which was consistent with the model. The implication for instructional practice is that careful selection and analysis of the tasks that frame microworld use is essential if these programs are to lead to the learning outcomes imagined for them. Theoretically, these results suggest a new interpretation for numerous empirical findings on the effectiveness of no-goal instructional tasks. The standing ‘‘reduced cognitive load’’ interpretation is contradicted by the success of the specific-path condition, and we offer an alternative knowledge-dependent interpretation.