The NGSS laid out 8 practices for K-12 science classrooms. While arguments could be made that both articles addressed all of these topics, some were more prominently featured than others. The Nersessian article focused mostly on Practice 2: Developing and using models. A key point in her article was how the use of a computational model of the synapses allowed the researches to measure and control critical variables with a much more precision. It also focused on the process of developing a model, highlighting that it is a continual process that can always be perfected and refined. Practice’s 4: Analyzing and interpreting data, and 5: Using mathematics and computational thinking, were also addressed in some detail. The team developed new ways of displaying their results, creating a visual of the network that could map they type of bursts and where they occurred over time. Using vectors, they were essentially able to plot the center of activity trajectory (CAT). These achievements required them to analyze the data they were collecting and use mathematical processes to communicate their results.
diSessa’s chapter’s focus on how computational literacy can enhance students ability to grapple with difficult and sometimes abstract concepts. He shows how a study of motion can be inaccessible to students without a strong foundation in algebra and calculus, but through computational modeling and programming, students can engage in discovery about motion at a much younger age. I see this idea aligning with Nersessian’s article that focuses on how we can use modeling, specifically computational modeling to learn about previously unreachable topics, in his case, the brain. While diSessa’s article made an argument for computational modeling and programming as an instructional practice and Nersessian was more detailed in outlining the steps involved in developing a model, both were able to show how these practices provide unique advantages. For example, diSessa highlights the way in which students can see how the up and down processes of a ball in flight are really the same thing through the s and a vector sliders. Nersessian explores a similar example in that the researchers were able to see how the bursts were not just random synapses firing, but that the could in fact track the movement of activity through the CAT. Both authors would agree that computational modeling and programming can offer unique insights that traditional mathematical methods cannot easily access.
I agree that both authors would say that computational modeling and programming can offer unique insights that traditional mathematical methods cannot easily access. I would agree myself based on personal experience with vectors. In diSessa's article he describes how a group of sixth graders might be able to interact with his tick model computer program and grasp some of the ideas about uniform motion at a young age. I remember my first experience with vectors in physics and it was freshman year in high school. There were no computer models to show this idea and I remember struggling at first. The idea that a tick model could be altered to show vectors and represent a ball's trajectory in mid-air is amazing. What is more amazing is that this phenomena can be understood and explored by sixth graders! I took a physics course in undergrad and still did not have a program that offered such opportunity for exploration. Granted I still found a way to understand the material, I honestly think that I would have been able to make more connections and have a better understanding if I could have explored a program like the one diSessa describes.
ReplyDeleteTo a certain degree, I agree with you and Joey that computational modeling and programming offer insights that traditional mathematical methods cannot, but I motion to highlight 'traditional' and not establish this as an inherent flaw of mathematical modeling. Mathematical and computational literacies are very similar in their emphasis on abstraction and logic driven rule application/expansion, differing most essentially in potential processing power (brain v computer). The problems diSessa describes students facing with mathematics I think stem from concepts being presented as abstractions first, without grounding in reality. This grounding, provided by the vector visual in diSessa's computational model, could just as easily be presented to students mathematically but often gets left out- a main reason I think many students abandon mathematics at an early age. My hope for solving this issue thus has two parts: 1) that education in mathematics focuses on abstraction and reality/rationale and 2) that education in computation doesn't fall into the same abstraction trap that math did
ReplyDeleteDan I agree with you that both authors focused on the aspect of simplicity when discussing computational modeling. However, in order to reach that simplicity or make it easier for students to learn, they must first learn how to use the program, which can be intimidating itself. How can you make that a fun process? Addititionally, how would students be tested on the topics? Using the computational model or standard testing methods?
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