2/2 Jenna - The Individual and the Tool

In her article, Nersessian describes how neural engineers used multiple models to understand neural networks in order to build a neural system that could learn. Modeling is at the heart of her argument, and it is by far the most prominent NGSS practice in the piece. However, because she is looking at a community of scientists, we also see many of the other NGSS practices at work. We read of their questions and the process of defining their problem space. We read how the scientists synthesizing work from a number of disciplines to construct explanations and design solutions. This integration of domain enabled them to access their problem space and define the parameters of their models. Finally, by carrying out investigations with the models, the scientists were able to generate novel concepts about their field.

Nersessian's account of modeling states that "novel scientific concepts arise from the interplay of attempts to solve specific problems, use of conceptual, material, and analytical resources provided by the problem situation, and often through model-based reasoning processes" (2012, p. 1). This is consistent with diSessa's ideas about computational literacy, because he views this as an essential tool for scientific practices. Computational literacy (modeling through programming) requires conceptual analysis, but supports discovery through lower levels of abstraction and the ability to view the system moment by moment. Through programming, the learner builds a system from the inside out, and sees how the underlying principles fit together like puzzle pieces to produce the observed phenomenon. The versatility of this literacy empowers the individual to participate in many tasks (beyond modeling) within the scientific community. Furthermore, the tasks do not have to produce novel concepts for the entire community; the learner is equipped to inquire about the world and construct concepts that are interesting and novel to him- or herself.