Recent research on learning individual monologs and collaborative problem solving suggests that students learn best when they are required to be active participants in interactive dialogs. However, some interactive dialogs are more conducive to learning than others. Two dialog patterns that seem to be effective in producing successful problem solving and deep learning are elaborative and critical interactions. The goal of the present study is to evaluate the relative impact of each dialog on learning and problem solving by experimentally manipulating the types of conversations in which dyads engage.Undergraduate participants were randomly assigned to one of four conditions: a singleton control, a dyadic control, an elaborative dyad, or a critical dyad. The domain chosen for the experiment was a bridge optimization task in which individuals or dyads modified a simulated bridge, with the goal of making it as inexpensive as possible.Both problem solving and learning from the simulation were assessed. Performance on the task included a combination of two factors: the quality of the design and the price. Overall learning was measured by the gain from pre- to posttest on isomorphic evaluations, and was further decomposed into text-explicit and inferential knowledge. The results suggest elaboration is easier to train and led to stronger problem solving and learning than the control condition, whereas the critical interactions were more difficult to instruct and led to problem solving and learning equal to the control condition
