Expert models and modeling processes associated with a computer-modeling tool An 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
Zhang, BaoHui; Liu, Xiufeng; Krajcik, Joseph S.
2006-07
Citation
Zhang, BaoHui; Liu, Xiufeng; Krajcik, Joseph S. (2006). "Expert models and modeling processes associated with a computer-modeling tool An 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 ." Science Education 90(4): 579-604. <http://hdl.handle.net/2027.42/55226>
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 Ed 90 :579–2604, 2006Publisher
Wiley Subscription Services, Inc., A Wiley Company
ISSN
0036-8326 1098-237X
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