Matching Model Representations to Task Demands

Abstract: In CSCL environments model representations can stimulate learners to explicate their reasoning and elicit and support knowledge construction. But literature on their effectiveness is not clear with data indicating that representations not adapted to task demands have counterproductive effects. By matching the representational guidance of a model representation to the demands of the task, learners can be supported during their collaborative inquiry process because they receive the information they need when they need it. Generally speaking, the goal of collaborative inquiry learning is to have learners create a well developed conceptual understanding of a subject such that they are able to solve problems concerning that subject (Jonassen, 2003). This conceptual understanding is considered to be well developed when it has achieved an integration of both qualitative and quantitative knowledge representations of the subject matter (White, & Frederiksen, 1990). Such an understanding enables learners: to understand the core concepts and principles of the subject matter and the interrelationships between them (qualitative problem representation), to make calculations according to these principles and to understand the outcome of these calculations (quantitative problem representation). However, research shows that learners encounter at least two difficulties when working with multiple representations, namely, (1) problems translating from and coordinating betwee