Learning from worked examples is a very well established method when students should acquire cognitive skills. This method supports students in the beginning of skill acquisition when the major goal is to achieve an understanding of the underlying domain principles and their application. Once students understand the domain principles, worked examples can help them solve problems in a meaningful way and to fine-tune and optimize their skills.
As worked examples lose their effectiveness in the course of cognitive skill acquisition, the worked steps should be gradually faded out (fading principle). Another strategy to help students move from studying worked steps to problem-solving is to instruct the learners to first read and then imagine a solution (imagery principle). Because many students do not deeply process worked examples, self-explanations should be fostered (self-explanation principle). In this context, it is important that students are
supported to integrate different information sources, which are often provided in worked examples, to lower working memory demands; otherwise self-explaining is blocked by cognitive overload (easy- mapping principle). When students, particularly those with low prior knowledge, have difficulties in providing good self-explanations, instructional explanations can be used as a back-up help (explanation- help principles).
When later applying the skills learned from worked examples, typical errors can be reduced by providing examples illustrating these "traps" (studying errors principle). Sometimes sets of examples to be
compared can also help to avoid errors in later problem solving, in particular when the example sets are designed in ways that the crucial point to be attended stands out (example-set principle). Many students tend to learn worked solution procedures as fixed chains of steps that can only be applied to the very same problem type in the future. To prevent this lack of flexibility and to foster transfer, the single solution steps should be marked as meaningful building blocks (meaningful-building blocks principle). When worked examples have the form of models, it is recommended to have a model person similar to the learners (model-observer similarity principle). Such models often use complex exemplifying domains to demonstrate the skill (e.g., stem-cell research for argumentation skills). In these cases, learners' attention should be primarily directed - for example, by corresponding prompts - on the learning domain (e.g., argumentation) (focus-on-learning domain principle).
Some of these principles for learning from worked examples "add up" in their effects. For example, self- explanation prompts and fading independently contribute to better learning (e.g., Atkinson et al., 2003). However, there are not only the-more-the-better relations between the instructional principles. Some principles depend inherently on each other (e.g., example sets and self-explanations with respect to example comparisons): Learners cannot compare a good and a poor writing model without providing
two such models (Braaksma et al., 2002). In other cases, neglecting a principle such as "easy mapping" might countermand the effect of self-explanation prompts, because learners are cognitively overloaded (e.g., Sweller, 2006).
When implementing worked examples in their own classrooms, teachers should adopt a careful and theoretically informed (hopefully by this chapter) view as to whether this method might work in a specific context. Such a caveat is necessary, although the effectiveness of well-designed learning from worked examples is empirically very well established. Note that also a doctor when prescribing some tried-and-tested medication has to carefully check whether the expected effects emerge or whether there are small effects or even severe negative side effects in a specific case (see Renkl, in 2013). I hope, dear reader, that this chapter has not only motivated you to use learning from worked examples but also provided you with enough basic knowledge to check its effects and to further optimize it for your specific context. Good luck if you try out learning from worked examples!
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