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

在数学等结构良好的领域中，从设计好的例子中学习对于认知技能的获得起着重要的作用。此外，研究还提供了许多如何构建基于实例的学习的指导方针。然而，很少有研究将多重表征用于培养深度理解和灵活知识的习得范例。在此背景下，在这个项目中使用了具有多种表示格式(域：随机/组合)的解决方案的制定好的例子。整个研究问题涉及以下问题：能否通过使用多种表征与旨在促进不同表征整合的教学手段相结合来促进对解决程序的理解？这样的教学手段是a)自我解释提示，引导学习者识别不同表征之间的关系；b)相应的教学解释。通过两项实验研究解决了这些问题。

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.