Causal model progressions as a foundation for intelligent learning environments

White, Barbara Y.; Frederiksen, John R.

doi:10.1016/0004-3702(90)90095-h

Cited by 268 publications

(159 citation statements)

References 19 publications

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“…This type of support is in line with the model progression approach described by White and Frederiksen (1990). Model progression was found to lead to higher performance (Rieber & Parmley, 1995;Swaak, van Joolingen, & de Jong, 1998).…”

Section: Excerpt 1 (Low-level Novice)supporting

confidence: 73%

Finding Out How They Find It Out: An empirical analysis of inquiry learners’ need for support

Mulder

Lazonder

Jong

2009

International Journal of Science Education

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Section: Excerpt 1 (Low-level Novice)supporting

confidence: 73%

Finding Out How They Find It Out: An empirical analysis of inquiry learners’ need for support

Mulder

Lazonder

Jong

2009

International Journal of Science Education

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“…A potentially fruitful alternative might be to adapt task complexity to the students' increasing levels of domain understanding by structuring the task content according to a simple-tocomplex sequence. This type of learning support was first introduced by White and Frederiksen (1990), who termed it 'model progression'.…”

Section: Introductionmentioning

confidence: 99%

Validating and Optimizing the Effects of Model Progression in Simulation-Based Inquiry Learning

et al. 2011

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Model progression denotes the organization of the inquiry learning process in successive phases of increasing complexity. This study investigated the effectiveness of model progression in general, and explored the added value of either broadening or narrowing students' possibilities to change model progression phases. Results showed that high-school students in the 'standard' model progression condition (n = 19), who could enter subsequent phases at will, outperformed students from a control condition (n = 30) without model progression. The unrestricted condition (n = 22) had the additional option of returning to previous phases, whereas the restricted condition (n = 20) disallowed such downward progressions as well as upward progressions in case insufficient knowledge was acquired. Both variants were found to be more effective in terms of performance than the 'standard' form of model progression. However, as performance in all three model progression conditions was still rather weak, additional support is needed for students to reach full understanding of the learning content.

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“…On the other hand, most of the approaches trying to address "understanding" make use of a more or less formalized notion of "causality". For instance, White & Frederiksen (1987, 1988 exploit causal modeling to set up a learning environment that incorporates features of microworlds and of intelligent tutoring systems. The authors suggest possible progressions between models, in the context of a theory of model evolution.…”

Section: Education and Cognitive Sciencesmentioning

confidence: 99%

“…In the learner's theory, causality plays a crucial role [White & Frederiksen, 1987]. Taking into account the age of the learner (12-13 years), Aristotelian causalities are used as reference [Kuhn, 1971].…”

Section: From Education To Functional Modeling Of Physics Learningmentioning

confidence: 99%

Untitled

Neri

2000

Machine Learning

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Abstract. The goal of the reported research is the development of a computational approach that could help a cognitive scientist to interactively represent a learner's mental models, and to automatically validate their coherence with respect to the available experimental data. In a reported case-study, the student's mental models are inferred from questionnaires and interviews collected during a sequence of teaching sessions. These putative cognitive models are based on a theory of knowledge representation, derived from psychological results and educational studies, which accounts for the evolution of the student's knowledge over a learning period. The learning system WHY, able to handle (causal) domain knowledge, shows how to model the answers and the causal explanations given by the learner.

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Causal model progressions as a foundation for intelligent learning environments

Cited by 268 publications

References 19 publications

Finding Out How They Find It Out: An empirical analysis of inquiry learners’ need for support

Finding Out How They Find It Out: An empirical analysis of inquiry learners’ need for support

Validating and Optimizing the Effects of Model Progression in Simulation-Based Inquiry Learning

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