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Professors’ Instructional Approaches And Students’ Perceptions Of Nanohub Simulations As Learning Tools

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2008 Annual Conference & Exposition


Pittsburgh, Pennsylvania

Publication Date

June 22, 2008

Start Date

June 22, 2008

End Date

June 25, 2008



Conference Session

Embedded Computing

Tagged Division

Computers in Education

Page Count


Page Numbers

13.1005.1 - 13.1005.10



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Paper Authors


Alejandra J. Magana

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Alejandra Magana is a Ph.D. student in Engineering Education at Purdue University. She holds a M.S. Ed. in Educational Technology from Purdue University and a M.S. in E-commerce from ITESM in Mexico City. She is currently working for the Network for Computational Nanotechnology at Purdue University as a Research Assistant and as an Instructional Designer.

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Sean Brophy Purdue University

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Sean Brophy is an Assistant Professor in Engineering Education at Purdue University. He holds a Ph.D. in Education and Human Development (Technology in Education) from Vanderbilt University and a M.S. in Computer Science (Artificial Intelligence) from DePaul University.

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George Bodner Purdue University, West Lafayette

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George M. Bodner is the Arthur E. Kelly Professor of Chemistry, Education and Engineering at Purdue University, where he was head of the Division of Chemical Education in the Department of Chemistry and a member of the faculty of the School of Engineering Education.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Professors’ Instructional Approaches and Students’ Perceptions of nanoHUB Simulations as Learning Tools.


Simulations can provide a critical element of learning experiences. Simulations are also becoming a critical part of computational science, which is being described as the third- 1 leg in this century’s methodologies of science (Sabelli, et. al, 2005) . Opportunities exist to use the same simulation as both a tool for experts and a learning environment of novices. What needs to be done to accomplish this duality of a simulation resource?

The Network for Computational Nanotechnology (NCN) has developed an infrastructure network to help transform nanoscience to nanotechnology through online simulation and training. Called, the web portal delivers high-end, research quality, online simulations and tutorials to over 25,000 users annually (personal communication Gerhard Klimeck). These users include researchers, experimentalists, professors and students who, as a community of practice, collaborate and learn by sharing ideas, finding solutions, and building innovations in nanotechnology. The was initially focused on pioneering the development of nanotechnology from science to manufacturing through innovative theory, exploratory simulation, and novel cyber infrastructure. Recently, it has also become an outstanding educational source in nanotechnology-related concepts and theory. Our ultimate goal is to analyze how experts use these tools for research activities, so that we can better inform individuals developing materials to facilitate their use for educational purposes.

Review of the Literature

Experts use visual imagery such as models, as well as graphs, symbols and other representational systems, to help them represent and understand problems and facilitate solutions (Lehrer & Schauble, 2000 2 ; Nersessian, 1992 3 ). Because of the strong relationship between models and simulations, Mayer (1992) 4 defined a model as a representation that involves visualizing the principle-based mechanism between interacting components that represent the functionality or operation of a portion of the natural world. This visualization can concretize phenomena that are not directly observable.

In contrast, Nersessian (1992) 3 , argued that experts use models and simulations to construct mental representations and simulations that can be used to comprehend the system Operating on these mental representation involve the construction of analogical models and inferring through analogical reasoning. Nersessian also suggested that “these techniques involve a process of abstracting from phenomena or existing representations and creating a schematic or idealized model to reason with and quantify” (p. 65, 1992) 3 . Sabelli (2006) 5 noted that the addition of computer visualization to the simulation of complex phenomena allows for a visual exploration of the phenomena and overcomes the limits of models. As noted previously she described simulations as the third-leg in this

Magana, A. J., & Brophy, S., & Bodner, G. (2008, June), Professors’ Instructional Approaches And Students’ Perceptions Of Nanohub Simulations As Learning Tools Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--4186

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