Company Name

PeopleGas

Participatory simulations stand to scaffold student understanding in a wide range of contexts from abstractions like points on a graph to the very small like molecules in a gas. To help learners develop insight and understanding related to the distribution of speeds in an ideal gas, we have explored connecting motion detectors (TI's Calculator Based Rangers connected to calculators) to individual students and having the students then move around the classroom while the motion detectors record their speeds. The motion detectors collect data for each "molecule" (student) in this "people-gas"(collection of students). Individual histograms of |velocity| (or velocity^2) are be generated and then these can be combined to get a distribution of velocities for this people-gas. This distribution &endash; and its micro-logic (e.g. people won't move over as wide a range of speeds and they have lots of rules about social space etc.) &endash; is contrasted with distributions like the Maxwell-Boltzman distribution and its micro-logic (elastic collisions in an ideal gas). Shown below are two histograms

Histogram of "molecule" speeds for two trials of

the People-Gas activity.

from two trials of this people-gas activity. Among the issues discussed, learners did noticed that (unlike a Maxwell-Boltzman distribution) there were a fairly large number of "molecules" with relatively slow speeds. They connected this to a difference in the micro-logic of the people gas by suggesting that people will slow down before colliding in a way that molecules won't and that this accounts for there being more slow "molecules" in the people-gas. The contrasts as much as the similarities between the participatory simulation results and the behavior of an ideal gas deepen student understanding of this form of representation (histograms) and the use of the representation in significant scientific settings. In future versions the network will facilitate the real-time collection and aggregating of data.

Uri Wilensky
Northwestern University
Evanston, IL

Walter M. Stroup
The University of Texas
Austin, Texas


PeopleGas

Participatory simulations stand to scaffold student understanding in a wide range of contexts from abstractions like points on a graph to the very small like molecules in a gas. To help learners develop insight and understanding related to the distribution of speeds in an ideal gas, we have explored connecting motion detectors (TI's Calculator Based Rangers connected to calculators) to individual students and having the students then move around the classroom while the motion detectors record their speeds. The motion detectors collect data for each "molecule" (student) in this "people-gas"(collection of students). Individual histograms of \|velocity\| (or velocity^2) are be generated and then these can be combined to get a distribution of velocities for this people-gas. This distribution &endash; and its micro-logic (e.g. people won't move over as wide a range of speeds and they have lots of rules about social space etc.) &endash; is contrasted with distributions like the Maxwell-Boltzman distribution and its micro-logic (elastic collisions in an ideal gas). Shown below are two histograms

Histogram of "molecule" speeds for two trials of the People-Gas activity.
from two trials of this people-gas activity. Among the issues discussed, learners did noticed that (unlike a Maxwell-Boltzman distribution) there were a fairly large number of "molecules" with relatively slow speeds. They connected this to a difference in the micro-logic of the people gas by suggesting that people will slow down before colliding in a way that molecules won't and that this accounts for there being more slow "molecules" in the people-gas. The contrasts as much as the similarities between the participatory simulation results and the behavior of an ideal gas deepen student understanding of this form of representation (histograms) and the use of the representation in significant scientific settings. In future versions the network will facilitate the real-time collection and aggregating of data.


Uri Wilensky Northwestern University Evanston, IL		Walter M. Stroup The University of Texas Austin, Texas