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Expert models and modeling processes associated with a computer-modeling toolAn earlier version of the work was presented at NARST 2002 conference This article is based upon the work done at the University of Michigan Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

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Title: Expert models and modeling processes associated with a computer-modeling toolAn earlier version of the work was presented at NARST 2002 conference This article is based upon the work done at the University of Michigan Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
Authors: Zhang, BaoHui, Liu, Xiufeng, Krajcik, Joseph S.
Source: Science Education. Jul2006, Vol. 90 Issue 4, p579-604. 26p. 4 Color Photographs, 5 Diagrams, 4 Charts, 1 Graph.
Subjects: WATER quality, VIDEOS, ENVIRONMENTAL engineering, WATER analysis, ENVIRONMENTAL protection, EXAMPLE, MODEL theory, COMPUTER simulation, METAPATTERN (Information modeling), MODLER (Computer program language), GEOCHEMICAL modeling
Abstract: Holding the premise that the development of expertise is a continuous process, this study concerns expert models and modeling processes associated with a modeling tool called Model-It. Five advanced Ph.D. students in environmental engineering and public health used Model-It to create and test models of water quality. Using “think aloud” technique and video recording, we captured their computer screen modeling activities and thinking processes. We also interviewed them the day following their modeling sessions to further probe the rationale of their modeling practices. We analyzed both the audio–video transcripts and the experts' models. We found the experts' modeling processes followed the linear sequence built in the modeling program with few instances of moving back and forth. They specified their goals up front and spent a long time thinking through an entire model before acting. They specified relationships with accurate and convincing evidence. Factors (i.e., variables) in expert models were clustered, and represented by specialized technical terms. Based on the above findings, we made suggestions for improving model-based science teaching and learning using Model-It. © 2006 Wiley Periodicals, Inc. Sci Ed90:579–2604, 2006 [ABSTRACT FROM AUTHOR]
Copyright of Science Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Expert models and modeling processes associated with a computer-modeling tool<FNR></FNR><FN>An earlier version of the work was presented at NARST 2002 conference </FN><FNR></FNR><FN>This article is based upon the work done at the University of Michigan </FN><FNR></FNR><FN>Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation </FN>
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+BaoHui%22">Zhang, BaoHui</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiufeng%22">Liu, Xiufeng</searchLink><br /><searchLink fieldCode="AR" term="%22Krajcik%2C+Joseph+S%2E%22">Krajcik, Joseph S.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Science+Education%22">Science Education</searchLink>. Jul2006, Vol. 90 Issue 4, p579-604. 26p. 4 Color Photographs, 5 Diagrams, 4 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22WATER+quality%22">WATER quality</searchLink><br /><searchLink fieldCode="DE" term="%22VIDEOS%22">VIDEOS</searchLink><br /><searchLink fieldCode="DE" term="%22ENVIRONMENTAL+engineering%22">ENVIRONMENTAL engineering</searchLink><br /><searchLink fieldCode="DE" term="%22WATER+analysis%22">WATER analysis</searchLink><br /><searchLink fieldCode="DE" term="%22ENVIRONMENTAL+protection%22">ENVIRONMENTAL protection</searchLink><br /><searchLink fieldCode="DE" term="%22EXAMPLE%22">EXAMPLE</searchLink><br /><searchLink fieldCode="DE" term="%22MODEL+theory%22">MODEL theory</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+simulation%22">COMPUTER simulation</searchLink><br /><searchLink fieldCode="DE" term="%22METAPATTERN+%28Information+modeling%29%22">METAPATTERN (Information modeling)</searchLink><br /><searchLink fieldCode="DE" term="%22MODLER+%28Computer+program+language%29%22">MODLER (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22GEOCHEMICAL+modeling%22">GEOCHEMICAL modeling</searchLink>
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  Data: Holding the premise that the development of expertise is a continuous process, this study concerns expert models and modeling processes associated with a modeling tool called Model-It. Five advanced Ph.D. students in environmental engineering and public health used Model-It to create and test models of water quality. Using “think aloud” technique and video recording, we captured their computer screen modeling activities and thinking processes. We also interviewed them the day following their modeling sessions to further probe the rationale of their modeling practices. We analyzed both the audio–video transcripts and the experts' models. We found the experts' modeling processes followed the linear sequence built in the modeling program with few instances of moving back and forth. They specified their goals up front and spent a long time thinking through an entire model before acting. They specified relationships with accurate and convincing evidence. Factors (i.e., variables) in expert models were clustered, and represented by specialized technical terms. Based on the above findings, we made suggestions for improving model-based science teaching and learning using Model-It. © 2006 Wiley Periodicals, Inc. Sci Ed90:579–2604, 2006 [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/sce.20129
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        Text: English
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        PageCount: 26
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      – SubjectFull: WATER quality
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      – SubjectFull: VIDEOS
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      – SubjectFull: ENVIRONMENTAL engineering
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      – SubjectFull: WATER analysis
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      – TitleFull: Expert models and modeling processes associated with a computer-modeling tool<FNR></FNR><FN>An earlier version of the work was presented at NARST 2002 conference </FN><FNR></FNR><FN>This article is based upon the work done at the University of Michigan </FN><FNR></FNR><FN>Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation </FN>
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