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A simulation-based learning environment assisting scientific activities based on the classification of 'surprisingness'
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, Kobe University, Japan ; , Hiroshima University, Japan

EdMedia + Innovate Learning, in Lugano, Switzerland ISBN 978-1-880094-53-2 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

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Abstract

In this paper, we propose a general framework of the learning environment in which the system can explicitly assist a learner's scientific activity. A learner can improve her hypothesis and model of the domain developmentally with the guidance of the tutoring system. First, we present the framework for generally representing the models of the domain and their relations. Then, we classify the differences between the erroneous and correct modelsÕ behaviors of physical systems. This classification is conducted by a process ontology of physical systems, and used as the guideline for representing the knowledge of model modifications. The example of how our method works in an elementary mechanics problem is also presented.

Citation

Horiguchi, T. & Hirashima, T. (2004). A simulation-based learning environment assisting scientific activities based on the classification of 'surprisingness'. In L. Cantoni & C. McLoughlin (Eds.), Proceedings of ED-MEDIA 2004--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 497-504). Lugano, Switzerland: Association for the Advancement of Computing in Education (AACE). Retrieved August 9, 2024 from .

© 2004 Association for the Advancement of Computing in Education (AACE)

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References

View References & Citations Map
  1. Addanki (1989). Addanki, S., Cremonini, R. And Penberthy, J.S.: Reasoning about assumptions in graph of models, Proceedings of IJCAI-89, pp.1432-1438.
  2. Addanki (1991). Addanki, S., Cremonini, R. And Penberthy, J.S.: Graph of models, Artificial Intelligence, 51, pp.145-177.
  3. Perkinson (1984). Perkinson, H.J.: Learning From Our Mistakes: Reinterpretation of Twentieth Century Educational Theory, Greenwood Press.
  4. Weld (1989). Weld, D.S. And de Kleer, J.: Readings in Qualitative Reasoning about Physical Systems, Morgan Kaufmann.
  5. Wenger (1990). Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge, Morgan Kaufmann.
  6. White (1990). White, B.Y. And Frederiksen, J.R.: Causal Model Progressions as a Foundation for Intelligent Learning Environments, Artificial Intelligence, 42, pp.99-157.
  7. White (1993). White, B.Y.: ThinkerTools: Causal Models, Conceptual Change, and Science Education, Cognition

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Cited By

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  • Strategies for using simulations as a vehicle to manage cognitive load

    Les Lunce, Indiana State University, United States; Debra Runshe, Indiana University Purdue University Indianapolis, United States; E-Ling Hsiao & Xiaoxia Huang, Indiana State University, United States

    Society for Information Technology & Teacher Education International Conference 2009 (Mar 02, 2009) pp. 1492–1495

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