I think Wilensky and Reisman hit
the nail on the head when they commented, “In school settings, typical
instruction emphasizes the memorization of classification schemas and
established theories” (pg. 172). I know
I can remember classes where I had to memorize parts of a cell, or memorize a
certain pathway/cycle. Not only was this
type of instruction monotonous at times, but it was also not very effective as
I have forgotten many of the pathways.
Engaging in computational models where students get to explore and
create their own theories and hypotheses appears to be much more exciting and
effective. The processes described also
fit very nicely into the eight practices the NGSS framework recommends. Students were able to ask questions about a
phenomena, develop a model, plan and carry out an investigation, analyze and
interpret the results, use computational thinking, construct explanations for
results, engage in argumentation from evidence (other research papers comparing
accuracy of model results) and obtain and communicate information to
others. Although Wilensky and Resnick
focus more on thinking in different levels, their examples imply very similar
ideas. I really liked the Biology
examples (Predator-Prey models, Fireflies, and Slime Mold).
I used a modeling program called
Populus (I think), and we were able to mess around with the Lotka-Volterra
model by changing the values of the variables in the equations. Although I could see the effects over time by
the graphs the program produced, it was more math oriented and less fun to
interact with than the NetLogo model.
Perhaps the more mathematical model would be more appropriate for
college students with some knowledge of calculus and the NetLogo model would be
more appropriate for students with less background knowledge in calculus? One of the affordances of the NetLogo model
is its ability to explore complicated ideas with less intense math attached.
I found
myself thinking as I was reading about the flashing fireflies that I would
probably start by using a more deterministic centralized mindset (where the
leader gives deterministic orders to his or her followers), but this would have
been the wrong approach. I can’t help
but wonder what the result would be if a student (or myself) took the time to
create this model and work out all of the bugs, only to read later in a
research article that their mechanism was completely wrong. I would no doubt be frustrated and feel my
time was wasted. However, many revisions
are usually needed in any model built from scratch and that is part of
science.
A few
questions I have going off of that thought is: At what point should students be
able to look up/research information about the phenomena they are looking into? Would having that information before creating
the model take away from the inquiry process?
Or would having relevant background information aid in student thinking
and help give some direction to the model they will build?
It would be
interesting to try to model how the shapes and characteristics of different
trees leaves effect how the trees respond to different climates. (sharp pointy
needles vs. broad flat leaves, deciduous vs. non-deciduous, nutrient
availability, tropics vs. temperate forest)
Reading your questions makes me think of the Buehl practice, "Front loading for reading." I would think that after students have created and revised models, they would have developed an interest in the topic. Research that followed engaging in models would seem more relevant and practical to a student, as they would have an experience to create mental models and relate their research to. Student's research could be part of the revision process. Maybe following an initial model fabrication then research, students would be able to revise their models with an opportunity to apply what they have learned elsewhere. As for modeling different characteristics of tree's leaves, that is an interesting question. Many different variables would have to be considered if all leaves were available in a particular model. Perhaps a set of models would show how different leaves could be selected for depending on a niche created by a student. This could model why trees have the leaves that they do. Just one possibility of many!
ReplyDeleteI agree with David that an initial attempt before research can help guide and inspire students' independent research. However, I think that some scaffolding will be required so the student can set out on the right track, most specifically I think it will be important to help the student figure out the assumptions they is making with their initial hypothesis and model. I think this is a valuable skill because it does not depend on your prior knowledge in any particular field, but because it is metacognitive it may not come naturally to students and will I think require some guidance. I also like your tree question! Im thinking of two factors in particular - humidity and light availability - that I think have the greatest impact on leaf shape and may be relatively easy to model!
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