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Özet
The use of worked examples represents an alternative instructional method to that of problem-solving in highly structured domains such as physics and computer programming. The benefits of worked examples may be measured in posttest performance scores, and lower cognitive load in the form of less perceived mental effort. However, for worked examples to be effective, research into the range of different design components is crucial for creation of effective worked examples. Such components include intra-example features, such as the integration of source information with diagrammatic or auditory material, labeling or cueing of problem subgoals, and faded solution steps, and training in self-explanation strategies. Inter-example features such as the presentation of multiple examples, using example/problem pairs, and varying surface stories and problem types, are also important components of effective worked examples. A meta-analysis of multiple studies that employ various forms of worked examples instructional techniques was conducted. Mental effort ratings and posttest performance scores were used to calculate the standardized mean difference effect sizes for each study.
The findings of this study showed that worked examples indeed represent a benefit to learning, with certain features and/or environments producing greater benefits than others. When the general analysis was broken down into intra-example features and self-explanation training, the results showed that faded solution steps demonstrated the strongest effect in favor or worked example instruction, followed by self-explanation training. Conventional worked examples, integrations, and subgoals also represented moderate effects. Inter-example features such as presenting multiple examples per problem type and surface story variation produced better outcomes in terms of posttest scores compared to when these features were not present. Prior knowledge was also a factor in the efficacy of worked examples as students with little to no prior knowledge benefited more than those that had prior knowledge in the domain.
