NGSS describes several scientific and engineering practices that
every student should learn by twelfth grade in order to be literate in science.
Some of these were more or less prominent in the Nersessian paper. A couple of
these practices include the building of an experiment and collection and usage
of data and observations. Creating an experiment is the first step in a long
process for studying a scientific question, and a researcher needs to decide
what data is relevant or important to answer the question that is asked.
Nersessian described how the researchers in the case study used computational
modeling to create a simulation model of a neural system. This supports
diSessa’s idea that computational literacy should be something that anyone
should be competent in. Modeling, and computational modeling, is important
throughout the scientific and engineering processes.
According to NGSS, students should be able to create and use
models to help them analyzing and interpret data and observations. In the
Nersessian paper, the researchers used modeling, including computational
modeling to help them analyze their data. It was shown that modeling also helped
the researchers revise and improve their study. Computational modeling showed
the researchers how an event that they originally thought was something to
minimalize in their experiment was actually something that was important to
their study. Modeling information observed from data collection can lead to
findings that were not originally predicted, leading to adjustment and revision
of the concepts or hypotheses. The computational modeling helped them
understand what they were observing, which is what diSessa would want to see as
a computational literacy skill. If the researchers were not computational
literate, they would not have been able to improve their study, or their
hypothesis.
According to NGSS, scientists use models and representations
to explain, and defend, findings or design solutions, which is what the researchers
in the Nersessian paper had to do throughout and after their experiment. DiSessa
argues students can use computational models to create and organize information
for themselves to see and make sense of concepts and that the computational
models can act as an explanation for concepts. The researchers in the case
study used the models they created for their data to explain, and defend, their
findings to the scientific community. Nersessian and diSessa want students to
be science and computational literate, so they are able to find understanding
and create their own ideas.
To your point that diSessa sees computational models as a way to organize information and make sense of concepts, I would argue that he sees modeling as a fundamental method in learning new concepts. You do mention that students can use models to explore concepts, and I think that is more to the point. I believe that diSessa is saying the the models allow students to learn concepts that would otherwise require mathematical training beyond their years, and that is the real power of computational modeling. While the argument sounds reasonable, I wonder how the need for computational literacy would affect the implementation of the practice.
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