B. Y. White and J. R. Frederiksen. Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42:99--157, 1990. 12  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... Indeed, some of the earliest work in the field was directly aimed at instructional problems (e.g. 9,25] Over the last decade there have been several important efforts aimed at using qualitative physics to help teach diagnosis, troubleshooting, and operation of complex physical systems (e.g. [29,35,44]) Unfortunately, little effort has been focused on using qualitative physics in classroom settings, to help undergraduates learn principles of a domain (one rare exception is [40] In this paper, we describe CyclePad, an articulate virtual laboratory for engineering thermodynamics that is ....

B. White, J. Frederiksen, Causal model progressions as a foundation for intelligent learning environments, Artificial Intelligence 42 (1990) 99--157.

....them reason about related concepts such as parallel and series circuits, alternating versus direct current, and so forth. One manner of dealing with the difficulties that various causal models pose for learners is to limit the types of models students must deal with while learning new concepts. White and Frederiksen (1990; 1995; White, 1993) exposed students to models that were causally consistent, for instance, in which electrical force was the causal agent in both qualitative and quantitative models when learning the behavior of electrical circuits. They argued that, to the extent that there was causal ....

....the causal agent in both qualitative and quantitative models when learning the behavior of electrical circuits. They argued that, to the extent that there was causal generality in the concepts and laws embedded in the models, students should be able to apply their understanding to other domains (White Frederiksen, 1990). The present research asks a slightly different question. It asks how we can engage students in thinking about the nature of causality itself in an effort to build their ability to handle a variety of complex causal models in order to help them achieve the scientific understandings. We conducted ....

White, B. & Frederiksen, J. (1990). Causal model progression as a foundation for intelligent learning environments. Artificial Intelligence, 24, 99-157.

.... Existing systems for generating explanations of device behavior typically depend on models built explicitly for the explanation or tutoring task [8,21] When explanations are generated from more general behavior models, the explanations typically follow from hard coded labeling of causal influence [24] or component function [14] DME model libraries are designed primarily for describing behavior for engineering analysis. Textual annotations used for explanation are optional and have no behavioral semantics. Integrating model formulation and explanation in DME is analogous to the approach used ....

B. White & J. Frederiksen. Causal model progressions as a foundation for Intelligent learning. Artificial Intelligence, 42(1):99-155, 1990.

....concepts, should increase their sensitivity to possible causal patterns in play. This in turn should enable deeper understanding and a more systemic view of the concepts. Others have called for adequate, accessible causal models to help learners achieve complex scientific understandings (e.g. White Frederiksen, 1990, 1995) and especially those that build on intuitive notions of causality and mechanism (White, 1993, p. 182) Helping students and teachers address their assumptions and learn to recognize new, more complex forms of causality may be a promising avenue towards inducing conceptual change and ....

White, B. & Frederiksen, J. (1990). Causal model progression as a foundation for intelligent learning environments. Artificial Intelligence, 24, 99-157.

.... systems for generating explanations of device behavior typically depend on models built explicitly for the explanation or tutoring task [10,19] When explanations are generated from more general behavior models, the explanations typically follow from hard coded labeling of causal influence [21] or component function [14] Much of the work in explanation has concentrated on the generation of high quality presentations in natural language based on discourse planning and user modeling [7,17,18,19] These presentation techniques are independent of the modeling method or technique for ....

....of perturbations ( if X goes up, Y goes down ) This system is limited by the modeling representation: the confluence equations can only predict the sign of the first derivative and do not scale. Qualitative models have been used to generate explanations in tutoring and training systems [10,21]. DME s explanation system can also generate explanations on such models (using QSIM [15] for simulation) Qualitative models have known limitations of scale. Work on the SIMGEN systems [5,9] was the first to achieve the effect of qualitative explanation using numerical simulation models. SIMGEN ....

B. White & J. Frederiksen. Causal model progressions as a foundation for Intelligent learning. Artificial Intelligence , 42(1):99-155, 1990.

....of numerical simulation. Therefore, causal qualitative explanations have been applied to many intelligent tutoring systems which assist students in understanding how physical mechanisms act. White has developed an intelligent learning environment based on qualitative models of electrical circuit (White Frederiksen 1990). The models enable the system to simulate circuit behavior and to generate causal explanations, and serve as target mental models for learners in order to lead to more sophisticated model in a problem sequence. Cawsey has proposed detailed analysis of structure of causal explanations of simple ....

White, B.Y. & Frederiksen, J.R. 1990. Causal Model Progressions as a Foundation for Intelligent Learning Environments.

....the consideration for the overall architecture for an ITS. Wenger in [13] has characterized ITSs as consisting of either model based or curriculum based architectures. A modelbased ITS emphasizes the model view of the domain expertise. Some example ITSs in this class are: Lisp Tutor [1] QUEST [14], CIRCSIM Tutor [8] The curriculumbased ITSs, on the other hand, emphasize the curriculum view of the domain expertise, example ITSs in this class are: BIP [2] WUSOR [6] MHO [9] In Wenger s [13] term the curriculum based architectures emphasize the notion of lesson rather than that of model ....

White, B. Y. & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. In Clancey, W. J. & Soloway, E. (Eds.). Artificial intelligence and learning environment (pp. 7-49). Cambridge, MA: The MIT Press.

....of cdr recursion, or vice versa. While the actual structures may turn out to be quite similar, their purposes are very different. Here we are concerned only with the aim of enabling imprecise student modelling. The progression of causal models from qualitative to quantitative developed by White and Frederiksen (1990) appears similar to Greer and McCalla s abstraction hierarchy, but the former s aim is to eliminate most student modelling problems by building systems which enable students to build their own models. Student modelling then becomes a matter of the 32 system identifying which of the ....

.... identifying which of the pre specified sequence of causal models the student has acquired, and thus is a version of overlay modelling the students are assumed to have the current model when they can correctly solve problems that the current model can solve but the previous model could not (White and Frederiksen, 1990, p150) 5.3.2 Viewpoints Specifying relationships between elements of a belief set is useful when the student modelling component needs to adjust its focus on the student: partitioning the elements into subsets is useful when a different pair of spectacles is needed altogether. For example, ....

White, B.Y. and Frederiksen, J.R. (1990). Causal model progressions as a foundation for intelligent learning environments, Artificial Intelligence, 42, 99-157.

.... Indeed, some of the earliest work in the field was directly aimed at instructional problems (e.g. 1 ,2 ] Over the last decade there have been several important efforts aimed at using qualitative physics to help teach diagnosis, troubleshooting, and operation of complex physical systems (e.g. [3 ,4 ,5 ,6 ]) but little effort has been focused on using qualitative physics in classroom settings, to help undergraduates learn principles of a domain (a rare exception is [7 ] In this paper we describe a system, called CyclePad, that has been built to help engineering undergraduates appreciate and ....

White, B. & Frederiksen, J. Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99-157.

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B. Y. White and J. R. Frederiksen. Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42:99--157, 1990. 12

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White, B. & Frederiksen, J. 1990. Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99-157.

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White, B.Y., & Frederiksen, J.R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, pp. 99--157.

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B.Y. White and J.R. Frederiksen, Causal model progressions as a foundation for intelligent learning environments, Artificial Intelligence 42 (1990), 99--157.

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B. Y. White and J. R. Frederiksen. Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42:99--157, 1990. 12

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B.Y. White and J.R. Frederiksen, Causal model progressions as a foundation for intelligent learning environments, Artificial Intelligence 42 (1990), 99--157.

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B.Y. White, J.R. Frederiksen, Causal model progressions as a foundation for intelligent learning environments, Artificial Intelligence 42 (1990) 99--157.

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Frederiksen, J., & White, B. (1990). Causal Model Progressions as a Foundation for Intelligent Learning Environments.Artificial Intelligence, 42. 99-157.

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Science, 19(4/5):361--376. White, B.Y. and Frederiksen, J.R. (1990). Causal model progressions as a foundation for intelligent learning environments. In Clancey, W.J. and Soloway, E., (eds.), Artificial Intelligence and Learning Environments, pages 99--157. Cambridge MA: MIT Press.

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White, B. Y., and Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99-157.

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