In the Neressian article, all 8 practices, from asking
questions to constructing explanations, were used. This is so mainly because it was a novel
study that was conducted and there was a lot of room for investigation,
explanation, and revision. First, they
started out with a question regarding the communication between neurons when
learning takes place. Then they
developed the dish-model system and carried out a four year investigation. Throughout those four years data was
continually collected and additional computational models were created. This was evident with the “bursting”
component of their study, which prevented the detection of learning, which was
critical for data to be collected. Thus,
the scientists had to come up with a way to prevent bursting (designing
solutions and constructing explanations) and included one researcher creating a
computational model of the system to promote progress and understanding of the
neurons and their interaction. Throughout
the whole process, positive and productive argumentation took place from the
obstacles they hit to the data that was collected. While this study was new in its field, the
information collected from this study has made a huge innovative step towards
further understanding neurons and their interaction when learning.
In Chapter 2, diSessa focuses on computational models as ways
for the average student to grasp and understand concepts that “geniuses like
Galileo” even struggled to understand (34). This aligns itself with Neressian and
her article, where the researchers continually made computational models to
further understand what was going on in their experiment, especially when no
study like their’s has been done before.
Both authors point towards the importance of computational models as a
means to observe phenomena that could not otherwise or more easily be observed. This was evident in the diSessa article where
the students could manipulate the program by changing certain variables and see
how, as a result, the motion was affected.
Through the use of this program, students slowly began to build upon
their own models of motion and acceleration, just like Neressian and her fellow
researchers did during their own study, where they continually changed and
added to their model as they came across certain obstacles.
I like how diSessa mentions how models and the history of science (of which we looked at in our science literacy class last semester) can be seen side by side. As people learned and discovered new ideas, people would find ways to describe those ideas in easier and more efficient models. Computational modeling is another way to explain ideas. As you mentioned, in the Nersessian paper, the researchers had to efficiently describe phenomenon as they progressed throughout the experiment.
ReplyDeleteElizabeth,
ReplyDeleteI also thought that was interesting how scientists made more programming in order to better understand highly complex processes and phenomena. I can see how that relates to us being better able to do calculus now that we have calculators that can perform what were once lengthy calculations in less than a second. The advances technology has made can be mind boggling; however, once again we come to the point of how might a teacher be able to keep up with everything.