MaterialSim: A Constructionist Agent-Based Modeling Approach to Engineering Education

Chapter

Abstract

This chapter reports on a model-based, constructionist learning environment for engineering education in the field of materials sciences. “MaterialSim” is a set of activities, computer models, and support materials in which students’ main task is to conduct a scientific investigation by programming and testing their own agent-based models of a materials science phenomenon. In this paper, we investigate: (a) the cognition of students engaging in scientific inquiry through interacting with computer models; (b) the cognitive gains of students programming their own computer models of scientific phenomena; (c) the characteristics, advantages, and trajectories of scientific content knowledge that is articulated in epistemic forms and representational infrastructures unique to agent-based modeling, in comparison with aggregate, equational representations, and (d) the principles which govern the design of agent-based, constructionist learning environments in general and for materials science in particular.

We describe and analyze a series of studies, consisting of both design research and empirical evaluation. Data sources for the evaluation of these studies include classroom observations, interviews with students, videotaped sessions of model-building, questionnaires, and analysis of artifacts. Results suggest that (a) agent-based modeling seems to provide phenomenally isomorphic representations that can lead to deep conceptual insights about the content, particularly when students construct their own computer models, and (b) learning about the fundamental atomistic micromechanisms governing natural phenomena, given the availability of a computational medium to manipulate, represent, combine, and analyze them, enabled students to capture fundamental causality structures underlying complex behaviors in materials science.

Keywords Constructionism Engineering education Agent-based modeling Multiagent modeling Project-based learning Complexity theory NetLogo 

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Software and Model References

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Learning Sciences and Technology Design Program, School of Education and (by courtesy) Department of Computer ScienceSchool of Engineering, Stanford UniversityStanfordUSA
  2. 2.Center for Connected Learning and Computer-Based Modeling, Department of Learning Sciences, School of Education and Social Policy and Department of Computer ScienceMcCormick School of Engineering, Northwestern Institute on Complex Systems, Northwestern UniversityEvanstonUSA

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