Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples. This study extends that finding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth-grade students were merely asked to self-explain after reading each line of a possage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self-explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated o large number of self-explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model onolysis of the self-explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding. Preparation of this article was supported in part by an Office of Educational Research and

This article provides one example of a method of analyzing qualitative data in an objective and quantifiable way. Although the application of the method is illustrated in the context of verbal data such as explanations, interviews, problem-solving protocols, and retrospective reports, in principle, the mechanics of the method can be adapted for coding other types of qualitative data such as gestures and videotapes. The mechanics of the method we outlined in 8 concrete step. Although verbal analyses can be used for many purposes, the main goal of the analyses discussed here is to formulate an understanding of the representation of the knowledge used in cognitive performances and how that representation changes with learning This can be contrasted with another method or analyzing verbal protocols, the goal of which is to validate the cognitive processes of human performance, often as embodied in a computational model

Coached problem solving is known to be effective for teaching cognitive skills. Simple forms of coached problem solving are used in many ITS. This paper first considers how university physics can be taught via coached problem solving. It then discusses how coached problem solving can be extended to support two other forms of learning: conceptual learning and meta learning.

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

This article offers a plausible domain-general explanation for why some concepts of processes are resistant to instructional remediation although other, apparently similar concepts are more easily understood. The explanation assumes that processes may differ in ontological ways: that some processes (such as the apparent flow in diffusion of dye in water) are emergent and other processes (such as the flow of blood in human circulation) are direct. Although precise definition of the two kinds of processes are probably impossible, attributes of direct and emergent processes are described that distinguish them in a domain-general way. Circulation and diffusion, which are used as examples of direct and emergent processes, are associated with different kinds of misconceptions. The claim is that stu-Do Not Copy dents ’ misconceptions for direct kinds of processes, such as blood circulation, are of the same ontological kind as the correct conception, suggesting that misconceptions of direct processes may be nonrobust. However, students ’ misconceptions of emergent processes are robust because they misinterpret emergent processes as a kind of commonsense direct processes. To correct such a misconception requires a re-representation or a conceptual shift across ontological kinds. Therefore, misconceptions of emergent processes are robust because such a shift requires that students know about the emergent kind and can overcome their (perhaps even innate) predisposition to conceive of all processes as a direct kind. Such a domain-general explanation suggests that teaching students the causal structure underlying emergent processes may enable them to recognize and understand a variety of emergent processes for which they have robust misconceptions, such as concepts of electricity, heat and temperature, and evolution. Correspondence and requests for reprints should be sent to Michelene T. H. Chi, Learning Research

In this article we describe and analyze the evaluation of the Conceptual Helper, an intelligent tutoring system that uses a unique cognitive approach to teaching qualitative physics. The results of the evaluation are encouraging and suggest that the proposed methodology can be effective in performing its task.

Abstract: We believe that identifying media by their cognitive roles (e.g., definition, explanation, pseudo-code, visualization) can improve comprehension and usability in hypermedia systems designed for learning. We refer to media links organized around their cognitive role as cognitive media types [Recker, Ram, Shikano, Li, & Stasko, 1995]. Our hypothesis is that the goals that students bring to the learning task will affect how they will use the hypermedia support system [Ram & Leake, 1995]. We explored student use of a hypermedia system based on cognitive media types where students performed different orienting tasks: undirected, browsing in order to answer specific questions, problem-solving, and problem-solving with prompted self-explanations. We found significant differences in use behavior between problem-solving and browsing students, though no learning differences.

The theoretical stance explicated in this chapter assumes that scientific discoveries often require that the problem solver (either the scientist or the inventor) re-conceptualizes the problem in a way that crosses ontological categories. Examples of the highest level of ontological categories are entities, processes, and mental states. Discoveries might be explained as the outcome of the process of switching the problem representation to a different ontological category. Examples from contemporary and the history of science will be presented to support this radical ontological change hypothesis.

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To understand deep cognitive change, we have to understand how learners can go beyond their own prior knowledge. We propose a displacement scenario in which a learner acquires a target idea in a different context and then transfers that idea into a target context. We used virtual reality technology to implement a displacement scenario for teaching 2nd grade children that the Earth is round. The rather large pre- to posttest improvement was stable over four months.