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)

Human cognition is striking in its brilliance and its adaptability. How do we get that way? How do we move from the nearly helpless state of infants to the cognitive proficiency that characterizes adults? In this paper I argue, first, that analogical ability is the key factor in our prodigious capacity, and, second, that possession of a symbol system is crucial to the full expression of analogical ability.

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We examine the use of analogy in human thinking from the perspective of a multiconstraint theory, which postulates three basic types of constraints: similarity, structure and purpose. The operation of these constraints is apparent in both laboratory experiments on analogy and in naturalistic settings, including politics, psychotherapy, and scientific research. We sketch how the multiconstraint theory can be implemented in detailed computational simulations of the analogical human mind.

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (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 goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods—one‐on‐one human tutoring, observing tutoring individually, collaborating without observing, and studying alone—the results showed that students learned to solve physics problems just as effectively from observing tutoring (...) collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge. (shrink)

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the (...) analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. (shrink)

It was investigated to what extent example variability and the elicitation of sophisticated self-explanations foster the acquisition of applicable and transferable knowledge by learning from worked-out examples. To this end, we had 56 apprentices from a bank learn calculation of compound interest and real interest. The subjects were randomly assigned to the four conditions of a 2´2-factorial design (factor 1: uniform vs. multiple examples; factor 2: spontaneous vs. Elicited self-explanations). The learning results were measured by a post-test comprising application problems (...) and transfer problems. It was found that the acquisition of applicable and transferable knowledge can be supported by eliciting self-explanations. This underlines the "causal" relevance of the quality of self-explanations for knowledge acquisition by learning from worked-out examples. The assumption that multiple examples foster transfer performance, at least when sophisticated self-explanations are elicited, is not unequivocally supported. Without learner support in form of an elicitation procedure, multiple examples even seem to be detrimental. (shrink)

In attempting to fit a model of analogical problem solving to protocol data of students solving physics problems, several unexpected observations were made. Analogies between examples and exercises (a form of case‐based reasoning) consisted of two distinct types of events. During an initialization event, the solver retrieved an example, set up a mapping between it and the problem, and decided whether the example was useful. During a transfer event, the solver inferred something about the problem's solution. Many different types of (...) initialization and transfer events were observed. Poor solvers tended to follow the example verbatim, copying each solution line over to the problem. Good solvers tried to solve the problem themselves, but referred to the example when they got stuck, or wanted to check a step, or wanted to avoid a detailed calculation. Rather than learn from analogies, both Good and Poor solvers tended to repeat analogies at subsequent similar situations. A revised version of the model is proposed (but not yet implemented) that appears to be consistent with all the findings observed in this and other studies of the same subjects. (shrink)

The re-analysis aimed at extending earlier findings on example-based learning and to draw consequences for further research and instructional practice. Based on an earlier experimental study on learning with worked-out examples in the domain of accounting (n=56 students of a vocational school), we re-analysed the effects of an intervention means (elaboration training) on learning behaviour (aspects of example elaboration). In a further step, different ways of dealing with worked-out examples (elaboration pro-files) were identified and related to the subsequent learning outcomes (...) and to the learn-ers' mental effort. We explained the formation of different elaboration profiles by various learner characteristics (prior knowledge, interest and tolerance of ambiguity). It was shown that the elaboration training had a positive effect on the quality of example elaboration. Two ways of learning-effective example elaboration were identified. Subgroups of learners with different elaboration profiles differed in mental effort and in tolerance of ambiguity, but not with respect to prior knowledge and interest. High tolerance of ambiguity concurred with high mental effort and resulted in effective, meta-cognitively accentuated example elaboration. By integrating cognitive and motiva-tional characteristics in the analysis of elaboration patterns, new insights concerning example-based learning and the role mental effort plays in this context could be won. Consequences for research and instructional practice were drawn. (shrink)

The “testing effect” refers to the finding that after an initial study opportunity, testing is more effective for long‐term retention than restudying. The testing effect seems robust and is a finding from the field of cognitive science that has important implications for education. However, it is unclear whether this effect also applies to the acquisition of problem‐solving skills, which is important to establish given the key role problem solving plays in, for instance, math and science education. Worked examples are an (...) effective and efficient way of acquiring problem‐solving skills. Forty students either only studied worked examples (SSSS) or engaged in testing after studying an example by solving an isomorphic problem (STST). Surprisingly, results showed equal performance in both conditions on an immediate retention test after 5 min, but the SSSS condition outperformed the STST condition on a delayed retention test after 1 week. These findings suggest the testing effect might not apply to acquiring problem‐solving skills from worked examples. (shrink)