Qualitative simulation predicts the set of possible behaviors...

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This article describes learning with media as a complementary process within which representations are constructed and procedures performed, sometimes by the learner and sometimes by the medium. It reviews research on learning with books, television, computers, and multimedia environments. These media are distinguished by cognitively relevant characteristics of their technologies, symbol systems, and processing capabilities. Studies are examined that illustrate how these characteristics, and instructional designs that employ them, interact with learner and task characteristics to influence the structure of mental representations and cognitive processes. Of specific interest is the effect of media characteristics on the structure, formation, and modification of mental models. Implications for research and practice are discussed Do media influence learning? The research reviewed in this article suggests that capabilities of a particular medium, in conjunction with methods that take advantage of

This article addresses the position taken by Clark (1983) that media do not influence learning under any conditions. The article reframes the questions raised by Clark to explore the conditions under which media will influence learning. Specifically, it posits the need to consider the capabilities of media, and the methods that employ them, as they interact with the cognitive and social processes by which knowledge is constructed. This approach is examined within the context of two major media-based projects, one which uses computers and the other video. The article discusses the implications of this approach for media theory, research, and practice. Do media influence learning? Ten years ago, Richard Clark (1983) reviewed the results of comparative research on educational media and claimed that they provide consistent evidence "... for the generalization that there are no learning benefits to be gained from employing any specific medium to deliver instruction " (p. 445). According to Clark, the results of those studies that appear to favor one medium over another are due not to the medium but to the method or content that are introduced along with the

This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations

Work in philosophy and psychology has argued for a dissociation between perceptuallybased similarity and higher-level rules in conceptual thought. Although such a dissociation may be justified at times, our goal is to illustrate ways in which conceptual processing is grounded in perception, both for perceptual similarity and abstract rules. We discuss the advantages, power and influences of perceptually-based representations. First, many of the properties associated with amodal symbol systems can be achieved with perceptually-based systems as well (e.g. productivity). Second, relatively raw perceptual representations are powerful because they can implicitly represent properties in an analog fashion. Third, perception naturally provides impressions of overall similarity, exactly the type of similarity useful for establishing many common categories. Fourth, perceptual similarity is not static but becomes tuned over time to conceptual demands. Fifth, the original motivation or basis for sophisticated cognition is often less sophisticated perceptual similarity. Sixth, perceptual simulation occurs even in conceptual tasks that have no explicit perceptual demands. Parallels between perceptual and conceptual processes suggest that many mechanisms typically associated

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.

Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom in-struction and the broader educational research community. A frame-work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional de-sign principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and em-phasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each concep-tual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More-over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain examples. Worked examples are associated with early stages of skill develop-ment, but the design principles are relevant to constructivist research and teaching. The Historical Context In recent years, learning from "worked examples " has received a consider-able amount of attention from researchers (e.g., Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Ward & Sweller, 1990), particularly in such fields as mathematics, physics, and computer programming. Although there is no precise definition, worked examples share certain family resemblance (Wittgenstein, 1953). As instructional devices, they typically include a problem statement and a proce-dure for solving the problem; together, these are meant to show how other similar problems might be solved. In a sense, they provide an expert's problem-