Pickering’s chapter one is an introduction to how
scientists view and practice science. He spends a large amount of time
discussing science practice and studies in time. While I am not absolutely
certain what Pickering was trying to argue, it seems he is touching on the idea
that scientists will change their practice, and theories, throughout studies
and over longer periods of time. He says that “…there is no thread in the
present that we can hang onto which determines the outcome of cultural
extension (pg24).” This sounds a little like Nersessian’s paper, which discusses
how science practice includes times when hypotheses and models have to be
revised or improved upon. In the study, researchers were using computational
modeling to throughout a study on a system of neural cells, and then had to
change the models to incorporate new observations.
Pickering seems to be saying that science is
uncertain and unpredictable, which is why we have to revise and improve
machines and practices. In Netlogo, we have and will be creating models that
will have to be ‘tuned’ so they will be able to properly model systems, and be
predictive of future outputs. Unfortunately, Pickering does not talk much about
how to teach students science, but he seems to be in agreement with the other
authors we read about what science practice is and how it is done.
Pickering’s argument about agencies, human and
material, is what made the most sense to me in the whole article. Scientific
practice is sets of actions to study systems in action. The world is always
moving. It seems that Pickering tries, throughout the chapter, and apparently
the book, to describe how human agency and material agency work together. In
Netlogo, the programming is using ‘agents’ to act out systems representing
other systems. In the wolf and sheep program, the agents can be programmed to
hunt, eat, die, and propagate in order to represent population dynamics. I am
not sure if this is exactly what Pickering was imagining representation and
modeling could do, but he did mention how emergence and mangling could help
analysis of different aggregation levels (when he was discussing his argument
in chapter 7). Aggregation levels being taught through modeling was an
idea discussed by Wilensky, and it seems Pickering would agree with this method.
I was not absolutely sure what Pickering was
defining the ‘mangle’ as. Was it the interaction between human agency and
material agency in science practice? Computational modeling seems to do this
well. Would Pickering agree that computational modeling is a good way to teach
students science practice?
Science questions: As it is supposed to snow this
week, how are air currents and weather tracked, and how could they be modeled?
I also had a difficult time figuring out what "mangle" exactly meant to Pickering. We should definitely have a class discussion about this!
ReplyDeleteFunny you should ask that science question, Laura and I were just discussing that this weekend! Weather itself is not really modeled per se, rather data is collected by weather balloons released daily across the US at 102 sites. This data (air pressure, temperature, relative humidity, and wind direction and speed) is then entered into programs and various models synthesize that data for what we would consider weather forecasts/predictions. My point being, what we know about air currents and weather are actually the products of modeling.
Cool question about air currents. My understanding is that weather is super complicated and that's why we haven't really figured it out yet. To model it accurately takes huge computers that do tons of calculations and still can't really get it right because there are so many unknown factors. There is that famous piece about how a butterfly flapping its wings in asia can cause a hurricane in south america or something like that. It would be interesting to model the Butterfly effect (http://en.wikipedia.org/wiki/Butterfly_effect) in action to show how small changes in the start of the model can make huge changes in the later weather patterns.
ReplyDeleteThe models I forgot to put into my post:
ReplyDeleteI already talked some about the Wolf Sheep Predation model in my blog above. This model would be able to help students reach the following standards:
HS-LS2-1 Use mathematical to support and revise explanations based on evidence about factors affecting biodiversity and populations in ecosystems of different scales.
HS-LS2-6 Evaluate the claims, evidence and reasoning that the complex interactions in ecosystems maintain relatively consistent numbers and types of organisms in stable conditions, but changing conditions may result in a new ecosystem.
HS-LS2-8 Evaluate the evidence for the role of group behavior on individual and species’ chances to survive and reproduce.
This model lets students explore the idea of population dynamics and the interconnectivity between organisms and the environment that can lead to a stable or unstable ecosystem.
Another interesting model is the Mimicry model with the monarch butterflies and the viceroy moths. Over time, the viceroy moths mimic the color of the monarchs so they can avoid being eaten by birds. This model would touch on the following standards:
HS-LS4-2.4 Construct an explanation based on evidence that the process of evolution primarily results from four factors … the list includes genetic variation and potential to survive and reproduce in an environment.
HS-LS4-4 Construct an explanation based on evidence for how natural selection leads to adaptation of populations.
The model also includes how long birds remember that color can mean nasty tasting prey. The model then shows how genetic variation allows populations to adapt to environmental factors.