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How can we help people develop insight in both engineering education and practice ?

Simulation, Exploration, and Understanding in Engineering G. W. Rubloff Materials Science & Engineering, and Institute for Systems Research University of Maryland rubloff@isr.umd.edu www.isr.umd.edu/~rubloff/. How can we help people develop insight in both engineering education and practice ?.

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How can we help people develop insight in both engineering education and practice ?

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  1. Simulation, Exploration, and Understanding in EngineeringG. W. RubloffMaterials Science & Engineering, and Institute for Systems ResearchUniversity of Marylandrubloff@isr.umd.eduwww.isr.umd.edu/~rubloff/ How can we help people develop insight in both engineering education and practice ? with special thanks to Anne Rose, HCIL Center for Engineered Learning Systems www.isr.umd.edu/CELS/ Institute for Systems Research Human-Computer Interaction Laboratory www.cs.umd.edu/hcil/ Institute for Advanced Computer Studies

  2. materials & processes transistors & chips Pick Station Stocker Depart Leave Arrive Developing Insight in Engineering Education and Practice • EXAMPLE: semiconductor chips CHALLENGES Domains are unfamiliar to the user Often no hands-on physical experience Unfamiliar length and time scales Principles are abstract Subtle until experienced Ultimately must be understood in mathematical terms Systems-level behavior enlarges complexity Multi-level metrics Heterogeneous, hierarchical models Dynamic & stochastic behavior Environments and tools for engineering insight are limited Education and training Broad engineering practice people equipment factory costs and operations/ logistics

  3. Developing Insight in Engineering Education and Practice • SOLUTIONS CHALLENGES Domains are unfamiliar to the user Often no hands-on physical experience Unfamiliar length and time scales Principles are abstract Subtle until experienced Ultimately must be understood in mathematical terms Systems-level behavior enlarges complexity Multi-level metrics Heterogeneous, hierarchical models Dynamic & stochastic behavior Environments and tools for engineering insight are limited Education and training Broad engineering practice Simulations of physical phenomena Desired attributes of simulation environments

  4. Monte Carlo materials & processes transistor devices finite element equipment circuits & chips dynamic continuous parameter factory operations & logistics static spreadsheet cost of ownership dynamic/stochastic discrete event Engineering Simulations • EXAMPLE: semiconductor chips

  5. Monte Carlo materials & processes transistor devices finite element equipment circuits & chips dynamic continuous parameter factory operations & logistics static spreadsheet cost of ownership dynamic/stochastic discrete event Engineering Simulations • EXAMPLE: semiconductor chips While valuable to specific technical experts, how beneficial are these for education and broader practice?

  6. Self-directed and guided hands-on experiences Tools to help the user develop understanding and insight Connectivity to underlying fundamentals Complexity management through Integrated, heterogeneous simulations Separable authoring and rapid module development Desired attributes of simulation environments Developing Insight in Engineering Education and Practice • SOLUTIONS CHALLENGES Domains are unfamiliar to the user Often no hands-on physical experience Unfamiliar length and time scales Principles are abstract Subtle until experienced Ultimately must be understood in mathematical terms Systems-level behavior enlarges complexity Multi-level metrics Heterogeneous, hierarchical, dynamic, stochastic behaviors Environments and tools for engineering insight are limited Education and training Engineering practice Simulations of physical phenomena

  7. Simulated Processes in a Learning Environment SimPLE control the simulation view dynamic results keep history timer operate system and see consequences in real time communicate save & document learning by DOING carry out experiments and annotate results access background and guidance materials, locally or from Internet Demos in HCIL

  8. Simulation control at system image Tightly-coupled guidance Condition watchdog Assigned exercises Change module Timer Lab notebook System design configurator Process recipes Learning historian Design of experiments E-mail tool Graphs & charts Guidance – local & Internet Visualization control learner Teacher kit Authoring in html teacher Separable authoring SimPLE framework Domain-specific simulation models and submodels Domain-specific Delphi objects author / developer Featuresin the SimPLE Framework

  9. Tightly-Coupled Guidance

  10. 1. Do a simulation 2. Record and save the simulation history 3. Replay the simulation history 4. Review, revise, & annotate the history 5. Share the history with peers & instructor Learning Historian History Simulation

  11. Guidance materials Configuration setups Simulation models Error messages Historian configuration GUI components System design parameters Teacher Kit Teacher may create specific setups to customize educational scaffolding

  12. Cluster tool scheduling Process recipe Sensitivity analysis Oxide thickness Oxide growth temperature Cluster tool configuration YIELD fail pass Factory simulation Capacitor area Capacitance fail SimPLE Applications TrafficSim transportation management SortSim computing algorithms NileSim hydrology & social science EquiPSim semiconductor manufacturing WaferMap multistep process optimization WaterSim environment & manufacturing HSE factory operations

  13. Messages Engineering insight through SimPLE environments Free and guided exploration through simulation Powerful tools for individual and collaborative learning Also: science, computer science, math, social science, … You can use this learning systems technology now Teachers – specific topical areas & development of new areas Developers – SimPLE platform & new features to come We invite your participation Collaborations, workshops, … www.isr.umd.edu/CELS/

  14. Research support Research partnership for tech training Simulation software platform Commercial applications & customization CEBSM Research partnership for semiconductor ESH Acknowledgements ENGINEERING L. Henn-Lecordier B. Levy P. Tarnoff G. B. Baecher B. Levine J. W. Herrmann COMP SCI & UMIACS A. Rose B. Shneiderman C. Plaisant G. Chipman EXTERNAL F. Shadman (U. Arizona CEBSM) M. Lesiecki (MATEC) S. Braxton (Bowie State)

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