When Minimal Guidance Does and Does Not Work: Drill and Kill Makes Discovery Learning a Success

Two experiments were performed contrasting discovery learning with a variety of different instructional conditions. Students learned to solve data-flow isomorphs of the standard algebra problems. In experiment 1, where students practiced each new operation extensively, they performed best in a Discovery condition. The Discovery condition forced participants to develop correct semantic characterizations of the algebraic transformations. In Experiment 2, where students practiced each operation minimally, they performed worst in the Discovery condition and most of them failed to complete the curriculum. With less practice students' attempts to discover transformations became less constrained and more random. This search for transformations became so extended that students were unable to remember how they achieved transformations and so failed to learn. These interpretations of the advantages and disadvantages of discovery learning were confirmed with a simulation model that was subjected to the various learning conditions. Discovery learning can lead to better learning outcomes only when the challenge posed by the demand of discovery does not overcome the student's resources. 3 There has been a long history of advocacy of discovery learning including such intellectual giants as Rousseau, Dewey & Piaget. Bruner (1961) is frequently credited as the source for the modern research on discovery learning in the last 50 years. While discovery learning continues to have its advocates (e. two of the responses to the Kirschner et al. criticisms of minimally guided learning, the authors (Hmselo-Silver et al., 2007 and Schmidt et al, 2007) did not question the claim that minimally guided learning was bad. Rather they questioned whether Kirschner et al had it right in classifying problem-based inquiry as minimally guided. The conclusion of the research to be reported here is that one cannot make blanket claims about the superiority or inferiority of discovery learning. Rather one must assess carefully the information-processing consequences of each learning situation. A careful reading of the Kirschner et al paper finds such a nuanced perspective and they note cases where discovery learning can lead to superior results. We will try to develop an understanding of the information-processing consequences of the learning conditions we are studying by developing computer simulation models that reproduce the basic effects of our experiments. Many domains have a sufficiently rich combinatorial structure that it is not possible to provide students with direct instruction on all possible cases. They have to generalize what they learn on specific cases to new cases. For instance, in …