Learning from worked-out examples: Fostering the integration of multiple representations

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Learning from worked-out examples plays an important role in cognitive skill acquisition in well-structured domains such as mathematics. In addition, research has provided many guidelines how to structure example-based learning. However, there is hardly any research on worked-out examples in which multiple representations are employed with the instructional intention to foster deep understanding and flexible knowledge. Against this backgrond, worked-out examples with solutions in multiple representational formats (domain: stochastics/combinatorics) are employed in this project. The overall research question refers to the following issues: Can the understanding of solution procedures be fostered by the employment of multiple representations that are combined with instructional means that aim to foster the integration of different representations? Such instructional means are a) self-explanation prompts that direct the learners to identify the relations between different representations and b) corresponding instructional explanations. These issues are addressed by two experimental studies.