mushi-life presenter richard joiner designer : chris quintana
TRANSCRIPT
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MUSHI-Life
Presenter Richard Joiner
Designer : Chris Quintana
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AERA – San Francisco 2006 2
MUSHI-Life
• MUSHI-Life is a multi-user simulation with integrated handheld devices
• Groups of students assume roles as environmental entomologists.
• A tablet computer shows a simulation
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AERA – San Francisco 2006 3
MUSHI-Life
• The simulation contains different insect-like “bugs” with different physical characteristics
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AERA – San Francisco 2006 4
MUSHI-Life
• A set of rules describes how the bugs reproduce, feed, and interact with other bugs in the environment.
• The survival ability of a given bug is governed – its phenotype, – different characteristics of the environment, – characteristics of other bugs it may encounter
in the simulation.
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AERA – San Francisco 2006 5
MUSHI-Life
• Students can view the overall simulation on the tablet computer
• Use individual PDAs to "capture" and "release" individual bugs
• Use them to view magnified, detailed portions of the global environment, such as the interaction of a given set of bugs or the characteristics of a given bug.
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AERA – San Francisco 2006 8
MUSHI-Life
• MUSHI-Life is designed to support students' explorations of questions surrounding natural selection and adaptability.
• It may be used in an observational investigation to identify behavioral patterns related to survival within native contexts.
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AERA – San Francisco 2006 9
MUSHI-Life
• It can be used to directly observe the effects of moving a bug to a non-native environment
• Users can explicitly manipulate bug characteristics to experimentally determine their relationship to adaptation and survival.
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AERA – San Francisco 2006 10
MUSHI-Life
• MUSHI-Life provides a framework to give learners multiple linked representations of a simulation
• They can explore and manipulate a scientific simulation
• See different aspects of the simulation at different levels of granularity.
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AERA – San Francisco 2006 11
MUSHI-Life
• See that there are different levels to understanding in complex simulations
• Understand how local interactions can impact the global behavior of the simulation.
• Engage in more reflective thinking
• Engage in the types of social interactions that can positively impact learning.
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AERA – San Francisco 2006 12
MUSHI-Life
• The first prototype of MUSHI-Life was completed in June 2005.
• Initial focus group testing with students ranging from sixth to eight grade will begin in the late 2005,
• classroom-based research studies scheduled for early 2006.
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AERA – San Francisco 2006 13
MUSHI-Lenses
• Representation of phenomena
• MUSHI use multiple and linked representations.
• Bug eyed representation through the hand held computer
• Overall view with the tablet computer.
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AERA – San Francisco 2006 14
MUSHI-Lenses
• Design of activity structure for investigating these phenomena
• The learners engage in systematic observation for the purpose of discovery and or problem solving.
• The activity is an inquiry learning activity
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AERA – San Francisco 2006 15
MUSHI-Lenses
1. students assigned a bug and asked to record the preferred food sources
2. survey the food sources
3. survey for a second time but the sources had been changed
4. predict which bugs would survive in the new conditions
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AERA – San Francisco 2006 16
MUSHI-Lenses
• Incorporate instructional scaffolds to support learning
• MUSHI scaffolds the learner in a number of ways
• Roy Pea’s (2004) framework
• What and Why of Scaffolding– Fading
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AERA – San Francisco 2006 17
MUSHI-Lenses
• How of Scaffolding• Channelling
– Recruitment – Getting the students interest– Reduction in the degrees of freedom – This involves
simplifying the task– Direction maintenance – Keeping them in pursuit of a
particular objective– Marking critical features – marking certain features of
the task that are relevant– Frustration control – Making the activity less stressful
• Modelling solutions of a task
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AERA – San Francisco 2006 20
MUSHI-Lenses
• How of Scaffolding• Channelling
– Recruitment – Getting the students interest– Reduction in the degrees of freedom – This involves
simplifying the task– Direction maintenance – Keeping them in pursuit of a
particular objective– Marking critical features – marking certain features of
the task that are relevant– Frustration control – Making the activity less stressful
• Modelling solutions of a task