Goals and learning in microworlds

We explored the consequences for learning through interaction with an educa tional microworld called Electric Field Hockey (EFH). Like many microworlds , EFH is intended to help students develop a qualitative understanding of t he target domain, in this case, the physics of electrical interactions. Thr ough the development and use of a computer model that learns to ploy EFH, w e analyzed the knowledge the model acquired as it applied the game-oriented strategies we observed physics students using. Through learning-by-doing o n the standard sequence of tasks, the model substantially improved its EFH playing ability; however, it did so without acquiring any new qualitative p hysics knowledge. This surprising result led to on experiment that compared students' use of EFH with standard-goal tasks against two alternative inst ructional conditions, specific-path and no-goal, each justified from a diff erent learning theory. Students in the standard-goal condition learned less qualitative physics than did those in the two alternative conditions, whic h 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 ore to lead to the learning outcomes ima gined for them. Theoretically, these results suggest a new interpretation f or numerous empirical findings on the effectiveness of no-goal instructiona l tasks. The standing "reduced cognitive load" interpretation is contradict ed by the success of the specific-path condition, and we offer an alternati ve knowledge-dependent interpretation.