1/26 Steve modeling and literacies

Steve Slovenski

Summaries:

            DiSessa begins by exploring how important literacy is to our modern lives and how we take it for granted.  He says that there are three pillars of literacy: material (such as written language, dependent on technology), cognitive (our intelligence and ability to interpret things like language) and social (in the sense that all literacies are communally agreed upon).  He uses an example of Galileo trying to explain something without algebra to show how important literacy is, and then proposes a definition for literacy: “Literacy is a socially widespread patterned deployment of skills and capabilities in a context of material support (that is, it is an exercise of material intelligence) to achieve valued intellectual ends.”  Finally, he argues that we need to think about how new literacies should be promoted in the education system.

            Nercessian’s paper details the benefits of modeling to a scientific experiment.  The experiment is a very complicated one which takes several years.  Over the course of the experiment, several doctoral students develop their own models of the neural network that is the focus of the experiment.  As the experiment progresses, the students refine and improve their several different models.  Finally, the models converge and a productive combination of the models is created that leads to important scientific progress.  The article shows that modeling is a continuously evolving process that not only helps communicate scientific ideas to others but also helps individuals understand the topic they are modeling better.

Relevant themes & questions:  

·         It is very difficult to imagine what future literacies are supposed to look like.  Does that mean people understand code languages?  Or is it something more general about technology.

·         One thing working to society’s advantage with future literacies is that technology makers have an incentive to help people become literate in their product’s language.  For example, facebook provides lots of templates and help for people trying to make new facebook apps.

·         Modeling is clearly a powerful tool for both communicating and learning.  Computer models particularly are fascinating in that you can put in rules that the simulation must follow and then sit back and watch without having to do all of the calculations yourself.  This provides an incredible opportunity to model all kinds of things that used to be too complex to model.

NGSS practices in diSessa and Narcessian

            The diSessa piece focuses on 5, 6, and 8 from the NGSS practices.  ‘Using mathematical and computational thinking’ is discussed in the context of the material pillar of literacy.  diSessa says that if we don’t have mathematical literacy, we will have trouble thinking mathematically because math literacies are designed to make understanding math concepts easier.  diSessa mentions the ‘constructing explanations’ standard when he uses Galileo’s explanation of some kinematics to show how literacies make explaining things in science much easier.  If we want to be able to construct explanations for increasingly complex things in technology, we need to continue to develop new literacies to make it possible.  ‘Obtaining, evaluating, and communicating information’ is discussed when diSessa details the history of calculus notation and how important the formation of a usable and clear language for calculus was.  If we had gone with the more complicated system of notation for calculus, communicating calculus concepts to other people would have been much more difficult. 

            The Narcessian paper addresses ‘asking questions and defining problems’ when it discusses how the bursts were initially considered noise but turned out to be meaningful.  This was a case of an improperly defined problem which modeling helped resolve.  The scientists thought that bursts were the problem, but they were actually part of the solution.  The practice most stressed by the Narcessian paper is ‘developing and using models’.  This is the whole focus of the paper.  The scientists used several different models and combined them in the end after making numerous iterations that improved each time.  The models not only communicated certain aspects of the network to others, but they helped the scientists understand what was actually going on in the network.  The paper addressed ‘analyzing and interpreting data’ by showing how the models continuously evolved based on new data coming from the neural network.  As information flowed from the network, it was used to improve the models.  ‘Computational thinking’ was stressed when Narcessian discussed how the computer model was made by one student who preferred to think in a more mathematical way about the network.  This model ended up being extremely useful in benefiting from the study.  ‘Constructing explanations’ was a skill used by the scientists throughout.  They were continuously bombarded with new information both from the models and the network itself, and they had to keep trying to find explanations that were consistent with all of the available data.  ‘Communicating information’ was discussed in the context of sharing models.  When the scientists came together to combine the benefits of each individual model, they had to carefully communicate to each other all of the aspects of their model.  Working as a team collaborating together was an important aspect of the study’s success.