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Characterization of Student Modeling in an Industrially Situated Virtual Laboratory

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2012 ASEE Annual Conference & Exposition


San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012



Conference Session

Epistemic Research

Tagged Division

Educational Research and Methods

Page Count


Page Numbers

25.304.1 - 25.304.20



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Paper Authors


Erick Jacob Nefcy Oregon State University

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Erick Nefcy is a doctoral student in the School of Chemical, Biological, and Environmental Engineering at Oregon State University. Through his undergraduate studies, he has held multiple internships at Intel Corporation. He is currently studying the growth of self-forming barrier layers in copper thin films, as well as investigating the student teams' use of models during completion of the Chemical Vapor Deposition Virtual Laboratory project.

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Edith Stanley Gummer Education Northwest


Milo Koretsky Oregon State University

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Milo Koretsky is a professor of chemical engineering at Oregon State University. He currently has research activity in areas related to thin film materials processing and engineering education. He is interested in integrating technology into effective educational practices and in promoting the use of higher level cognitive skills in engineering problem solving. Koretsky is a six-time Intel Faculty Fellow and has won awards for his work in engineering education at the university and national levels.

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Characterization of Student Modeling in an Industrially Situated Virtual LaboratoryModeling has been identified as a critical element of engineering practice. Theories regardingmodeling by STEM professionals in practice contend that models are constructed from priorknowledge and newly gathered information and that they are refined in an iterative cycle ofcreation, use, evaluation, and revision. However, authentic modeling practices are difficult toreplicate in the school environment.In order to develop these iterative modeling skills in students we have developed a learningsystem based on virtual laboratories. In these learning systems, student teams take on the role ofprocess development engineers and are tasked with finding suitable input parameters so theprocess can be released to high volume manufacturing. They complete the task thoughexperimentation, but the experiments are completed virtually. When students interact with thevirtual equipment, there is lower cognitive demand required to perform experiments. This aspectaffords students the opportunity to build a rich experimental design based on interpretation anditeration. While not instructed to do so, most student teams inevitably resort to modeling as a toolto progress towards a solution.Previously, we have reported a method that has been developed, termed model development andusage maps (model maps). These maps provide a graphical representation of student teams’model development as they proceed through the process development task. Classifications of themodel development include: type of model component (quantitative, qualitative, graphical,empirical, statistical), utility of the model (operationalized, abandoned, not engaged),experimental action based on the model component (directing the input variables for a future run,quantifying model parameters, using a run to qualitatively verify a model, etc), correctness of themodel component, and emotional responses to model verification or mismatch.The result of the previous study showed a significant difference in the modeling characteristics inthe virtual laboratory learning system when compared to two physical laboratories. The virtuallaboratory learning system showed significant increases both in the number of models studentsused and the variety of those models from team to team. However, the analysis was based on theteams’ work products, primarily on their design notebooks. The study reported in this paper usesthe same analysis technique, but at a finer grain size. Using protocol analysis, we audio recordedtwo teams while they completed the task, and then created model maps based on the transcripts.When compared to model maps for the same teams using the coarser analysis (work products),the protocol data show more detail in the modeling, but the essential “modeling path” isconsistent between the two methods. Moreover, the finer grain size of protocol analysis allowsthe observation of iterative cycle of creation, use, evaluation, and revision that could not beresolved at the coarser grain size.

Nefcy, E. J., & Gummer, E. S., & Koretsky, M. (2012, June), Characterization of Student Modeling in an Industrially Situated Virtual Laboratory Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21062

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