Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (b) phases of skill acquisition, and (...) (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible. (shrink)

The current research investigates a combination of two instructional approaches, tutored problem solving and worked examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked‐out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer‐based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the (...) effectiveness of such machine‐adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example‐enhanced Cognitive Tutors, in which the fading of worked‐out examples occurred either in a fixed way or adaptively. Results indicate that the adaptive fading of worked‐out examples leads to higher transfer performance on delayed posttests than the other two methods. (shrink)

The goal of this study was to investigate interindividual differences in learning from worked-out examples with respect to the quality of self-explanations. Restrictions of former studies (e.g., lacking control of time-on-task) were avoided and additional research questions (e.g., reliability and dimensionality of self-explanation characteristics) were addressed. An investigation with 36 university freshmen of education working in individual sessions was conducted. The domain was probability calculus. As predictors of learning, prior knowledge and the quality of self-explanations (thinking aloud protocols) were assessed. (...) A post-test was employed to measure the learning gains as dependent variable. The following main results were obtained. Most self-explanation characteristics can be regarded as relatively stable person characteristics. The interindividual differences in the quality of self-explanations were, however, found to be multidimensional. Most importantly, even when controlling for time-on-task (quantitative aspect), learning gains could be substantially predicted by qualitative differences of self- explanation characteristics. Successful learners tended to employ more principle-based explanations, more anticipative reasoning and more explication of operator-goal combinations. (shrink)

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) “incompatible” with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)organizing-processes of prior knowledge and thus reduce unnecessary cognitive load during subsequent learning. Fifty-six student teachers participated in the experiment. A framework group underwent a pre-training intervention providing a generalized, (...) categorical schema for categorizing primary learning strategies and related but different strategies as a cognitive framework for (re-)organizing their prior knowledge. Our control group received comparable factual information but no framework. Afterwards, all participants learned about primary learning strategies. The framework group claimed to possess higher levels of interest and self-efficacy, achieved higher learning outcomes, and learned more efficiently. Hence, providing a categorical framework can help overcome the barrier of incorrect prior knowledge in pieces. (shrink)