1/26 Jenna - Modeling as a New Literacy

Nersessian primarily discusses how models are used to reason about solutions to a problem. She specifically talks about  “bootstrapping” conceptual understanding with “hybrid models” in order to explore the target problem and analogical contexts, and how this sort of problem solving leads to the development of novel concepts. In the article, she presents an ethnographic study of a lab attempting to understand neural communication in order to build a system of neurons that could learn. In their labs, they observed a peculiar phenomenon ("bursting") in their physical simulation of the dish-model, and they constructed a computational simulation in order to study the phenomenon in greater depth. The two models they used made information (that was otherwise not visible) accessible for study and inquiry.

The excerpt from diSessa's book includes a discussion about literacy and a proposal for a new form of literacy, which she calls “computational literacy." In diSessa's view, literacy exists upon three "pillars": literacy is material (in that it requires external representations), mental (in that internal activity is necessary for the external representations to have meaning), and social (in that literacy is part of a historical trajectory and is embedded in communities). Literacy therefore frames how we are able to think about and interact with the world – through our memory, communication, reasoning, and representations. diSessa goes on to discuss programming and how this practice addresses various components of literacy she described earlier. Programming is interesting to diSessa because it brings a unity between experience and analysis (through the construction of computational models) that is absent from other forms of literacy, and that this unity broadens the interactions we can have with the world.

In her article, Nersessian hints at the practice of asking questions and defining problems, since she is studying a set of scientists who were searching for a way to build a learning neural network. She also touches upon the idea of constructing explanations and designing solutions when she describes how the scientists were synthesizing work from a number of disciplines to build their models; the integration of domains here was foundational to their work, enabling them access their problem space.

However, the most prominent practices in this piece – and in diSessa’s chapters too – are about modeling and carrying out investigations. In Nersessian’s article, the development of both physical and computational models determines what the scientists can reason about and how they think about the problem. In this way, the models drive their inquiry forward. In diSessa’s piece, modeling through programming is explored as it is a new literacy that expands the ways in which an individual can interact with the world. What I find most interesting about this concept is that diSessa frames programming as part of a literacy trajectory. He describes the texts and proofs of Galileo’s theorems, the advent of their algebraic notation and later their definitions from calculus, and poses programming as a way of moving these other forms of representation into an experiential territory.