ABSTRACT
Learning from worked-out examples is a very effective method for learning in well-structured domains. In this study, a new feature of example-based learning was introduced: learning with multiple solution methods. It was assumed that learning from multiple solutions fosters the understanding of solution methods, at least when the learning is supported by self-explanation prompts or by the provision of instructional explanations which compare different solutions. The participants (n = 170) learned to solve combinatorics problems under six conditions that constituted a 2x3-factorial design (Factor 1: “multiple solutions”: multiple vs. uniform; Factor 2: “instructional support”: no support vs. prompting written self-explanations vs. textual instructional explanations). The learning results were assessed through a post-test that required the flexible application of solution methods and the explication of the advantages and disadvantages of different methods. It was found that learning with multiple solutions was very effective. No positive effect was found for instructional support.
