ABSTRACT
In this paper, we provide evidence against the common idea that worked examples should be designed to convey problem categories and category-specific solution procedures. Instead we propose that instructional examples should be designed in a way that supports the understanding of relations between structural problem features and individual solution steps, i.e. relations that hold below the category level. We illustrate in the domain of probability word problems how category-avoiding instructional examples can be constructed. In two experiments we provide evidence that category-avoiding examples reduce cognitive load during learning and that they foster subsequent problem-solving performance.
