Learning from examples is most effective in stages 1 and 2 of the four-stage ACT-R cognitive framework:
- learners solve problems by analogy
- learners develop abstract declarative “rules” to guide problem solving (some generalization from step 1)
- learners no longer need to consciously invoke the “rules script” to solve problems
- learners have practiced many types of problems, so can instantly “retrieve a solution template”
Throughout the survey, “A is more effective than B” is generally measured by pre/post testing to measure transfer in controlled experiments. In some cases a hypothesis is proposed to explain the result in terms of one or another theoretical cognitive framework; in other cases no interpretation of result is offered.
A key finding is that students who engage in “self explanation” [Chi et al., many many cites], in which a learner pauses while inspecting an example to construct the omitted rationale for a particular step, outperform those who don’t. Here are several ways to stimulate this behavior (*) along with other best practices for creating and using worked examples:
- * Identify subgoals within the task.
- * Several partially-worked examples of varying complexity and illustrating various strategies/approaches, with enough “missing” to stimulate some self-explanation, are more effective than fewer but more-thoroughly-worked examples.
- * Don’t mix formats in one example, eg, use either a labeled diagram showing some concepts or a textual explanation of those concepts, but not both: the “split attention” cost actually retards learning.
- Don’t assigning an “explainer” role to stimulate self-explanation: it actually hinders learning, possibly because of increased stress and reduced intrinsic motivation for the learners.
- Visuals accompanied or immediately followed by aural comments are more effective than either visuals or comments alone.
- Alternate worked examples with practice problems, rather than showing N examples followed by N problems.
- Novices tend to overfocus on problem context rather than underlying conceptual structure; to compensate, use the same context/background for a set of different problem types.