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

Analogy is often viewed as a partial similarity match between domains. But not all partial similarities qualify as analogy: There must be some selection of which commonalities count. Three experiments tested o particular selection constraint in anological mapping, namely, systemoticity. That is, we tested whether a given predicate is more likely to figure in the interpretation of and prediction from on analogy if the predicate participates in a common system of relations. In Experiment 1, subjects judged two matches to be included in on analogy: an isolated match, and a match embedded in. a larger matching system. Subjects preferred the embedded match. In Experiments 2 and 3, subjects mode analogical predictions about a target domain. Subjects predicted information that followed from a causal system that matched the base domain, rather than information that was equally plausible, but that created an isolated match with the base. Results support Gentner's (1983, 1989) structure. mopping theory in that anological mopping concerns systems and not individual predicates, and that attention to shored systematic structure constrains the selection of information to include in an analogy.

Despite huge investments by vendors and users, CAD productivity remains disappointing. Our analysis of real-world CAD usage shows that even after many years of experience, users tend to use suboptimal strategies to perform complex CAD tasks. Additionally, some of these strategies have a marked resemblance to manual drafting techniques. Although this phenomenon has been previously reported, this paper explores explanations for its causes and persistence. We argue that the strategic knowledge to use CAD effectively is neither defined nor explicitly taught. In the absence of a well-formed strategy, users often develop a synthetic mental model of CAD containing a mixture of manual and CAD methods. As these suboptimal strategies do not necessarily prevent users from producing clean, accurate drawings, the inefficiencies tend to remain unrecognized and users have little motivation to develop better strategies. To reverse this situation we recommend that the strategic knowledge to use CAD effectively should be made explicit and provided early in training. We use our analysis to begin the process of making this strategic knowledge explicit. We conclude by discussing the ramifications of this research in training as well as in the development of future computer aids for drawing and design.

Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. This task is difficult because written descriptions often do not specify exactly how referenced events fit together. This article (1) characterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions," or collections of changes expressible in everydaylanguage, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity.PATHFINDER works byidentifying partial matches between the representations of events and using these matches to form causal chains, fill causal gaps, and merge overlapping accounts of activity. By applying transformations to events prior to matching, PATHFINDER is also able to handle a range of discontinuities arising from a writer's use of analogy or abstraction.

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Several earlier investigations found that teaching standard textbook physics causes only moderate change in qualitative understanding. Manyinvestigations have tried to explain why teaching textbook physics results in so little learning of qualitativephysics. In contrast, we examined cases where learning did occur and tried to understand them, hoping that this might help us to understand how to support such learning. We developed computerized simulation models of both qualitative, conceptual problem solving and quantitative problem solving and used them to assess changes in students' qualitative knowledge as they learned textbook physics. In many cases, qualitative knowledge has been acquired on the basis of information explicitly presented in the textbook. However, we also found cases where learning of qualitativephysics took place on the basis of information only implicitly addressed in the instruction. Even more important, in various cases this newly acquired qualitative knowledge led to a less frequent use of incorrect qualitative pre-knowledge. This suggests that successfull students did not only learn what has been explicitly presented in the instruction. Rather, they did also learn by deriving and constructing information left implicit in the instruction, relating this information to their pre-knowledge and possibly re#ning and modifying their pre-knowledge in those cases where con#icts became aware.

implicit knowledge about motion

To evaluate how top-down and bottom-up processes contribute to learning from animated displays, we conducted four experiments that varied either in the design of animations or the prior knowledge of the learners. Experiments 1–3 examined whether adding interactivity and signaling to an animation benefits learners in developing a mental model of a mechanical system. Although learners utilized interactive controls and signaling devices, their comprehension of the system was no better than that of learners who saw animations without these design features. Furthermore, the majority of participants developed a mental model of the system that was incorrect and inconsistent with information displayed in the animation. Experiment 4 tested effects of domain knowledge and found, surprisingly, that even some learners with high domain knowledge initially constructed the incorrect mental model. After multiple exposures to the materials, the high knowledge learners revised their mental models to the correct one, while the low-knowledge learners maintained their erroneous models. These results suggest that learning from animations involves a complex interplay between top-down and bottom-up processes and that more emphasis should be placed on how prior knowledge is applied to interpreting animations.

In this paper, we present QUALEX, a system and algorithm for generating first-order qualitative causal graphs for tutorial purposes based on de Kleer and Brown's qualitative modeling theory. QUALEX is embedded in a constructive simulation environment called Heart Works which teaches hydraulics principles about the cardio-vascular system to first year college biology students. 1

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