Key Questions for 1/26

Please post your group's questions as comments -- Thanks! Doug

ps. Please also:

1) Post your week 1 blogs and posts into bloodspot.
2) Adjust your titles to use the format



1/26 Doug: Catchy Descriptive Phrase

1/26 Laura Dynamics of Discovery

          This weeks’ readings delved more philosophically into the concepts of modeling and literacy.  Nersessian’s article intended to uncover the process through which a concept is discovered.  Following a neuroscience lab over the course of 5 years, Nersessian and her lab used ethnographic observation and interview strategies to understand the dynamics of social and cognitive modeling which ultimately lead to discovery when “the information was always there.” (20) 

            Andrea diSessa’s chapter seeks to define and understand the process and defining elements of literacy, ultimately as it pertains to informing the creation of a new computational literacy.  diSessa highlights three pillars of literacy- material, mental, and social- and analyzes both their history and their future potential optimization.  diSessa uses Galileo’s classic theory that distance is proportional to rate and time as a parable for obtaining new literacy, highlighting the critical difference between the theoretical form of its original presentation and the abstracted algebraic equation we know today, d=rt, arguing that once “new principles become fundamental and old ones become obvious. Entire new terrain becomes accessible.” (22) He concludes by addressing the cultural implications of literacy and the influence culture will have on future literacy.

            Across both pieces, I was especially struck by the emphasis on community and dynamic building of concepts as critical to the discovery and comprehension processes.  I think that history all too often highlights discovery as both individual and revelatory, when in reality it is more often a realization given many other pieces of knowledge and contribution.  This idea is supported by Vygotsky’s Zone of Proximal Development in which the individual’s potential is enhanced by others.  diSessa looks to extend this principle to the material realm as well (we can achieve more with proper materials), making an exciting argument for the potential role of computers in enhancing our thought processes and literacy.  Both pieces also spoke to a certain clarity once the discovery had been made, which I think is a really beautiful testament to the humbling pursuit of science as explanatory—the information was always there, the phenomenon has always existed, but we are just now coming to understand it.    

            In reading these two works, a few questions persist for me:

- How much can we really derive from anecdotal analyses like Nersessian’s?  Are these experiences necessarily universal to the creation of new concepts?

- What will computational literacy look like for our students and how do we incorporate it into the science classroom?  Especially considering we may not be as literate or intuitive as our students who have been raised on technology?

- Whose values are at play in our current definition of scientific literacy?  Whose values will be represented in computational literacy?  Is there any way to achieve a definition of literacy without confounding biases? (socio-economic, etc)           

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. 

1/26 David Modeling and Literacy

            Nersessian begins with describing a concept as an active relationship between person and theory, specifically through attempts to solve a problem and “model-based reasoning processes.” Then, a scientific experiment is observed and recorded in which different types of models are used. During the experiment, different scientists create physical and computational models to represent neurons and neural pathways. The experiment concludes that the relationship between creating and revising models leads to the construction of concepts. Concepts are developed through the specificity of models, both physical and computational, and allows for learners to construct a mental analogy between model and concept.

            diSessa begins with describing literacy and how it is used in our lives between personal, professional and educational uses. Then, it is described how computers are changing literacy and education. diSessa places literacy into three categories, or pillars. First, material literacy may range from the use of paper and pencil to spreadsheets and electronically processed images. Then, the second type of literacy, mental, is described as a relationship between physical tools and cognitive thought. diSessa describes a book as a, “poor stepping stool,” to someone who may not be able to read. The last type is described as social literacy. The ability to comprehend what others have written is critical to literacy, as shown by the work of Newton. diSessa then shows how people build upon another’s ideas through literacy. Finally, literacy is compared socially to environments and niches.

            Both articles sought to suggest that modeling and literacy both be communicated and revised. Through communication and revision, modeling and literacy become active between participants. The researchers in the Nersessian article used models to make predictions about what would happen next; these models were communicated and revised in their community. Also, diSessa discusses Galileo’s theorems and shows how they were communicated and revised between people so now that learners in high school may understand them. Literacy must be the, “driving force,” in education as diSessa says. Proper literacy will create knowledgeable and competent people who may effectively communicate and revise ideas.

1/26 Caitlin Scientific practices

Nersessian and diSessa are two researchers who are interested in how computational modeling and representations are used. Nersessian’s study focused on how scientists use models as they build a new hypothesis and create conclusions. DiSessa focused on how students can learn mathematical and scientific concepts through computational modeling.

There are several practices that are important to science and engineering work, mentioned by the NGSS, but that were not particularly prominent in the Nersessian and diSessa papers. A couple of these practices include the building of an experiment and collection and usage of data and observations. Creating an experiment is the first step in a long process for studying a scientific question, and a researcher needs to decide what data is relevant or important to answer the question that is asked. Nersessian described how the researchers in the case study decided on how the experiment should be set up and what they needed to observe. Computational modeling was used to set up the experiment.

NGSS discusses analyzing and interpreting data and observations as an important practice that students should be able to perform. NGSS argues that that organizing the data into a form (i.e. creating a model or representation) is a necessary skill. For the Nersessian and diSessa papers, this is where the practice of computational modeling comes in. It is the focus of the Nersessian paper, and diSessa discusses how students can use computational models to create and organize information for themselves to see and make sense of concepts.

Another practice, discussed in the NGSS, that was prominent in the readings was revision of hypotheses and the experiment. It was not the focus of the diSessa paper, but it was a large practice in the Nersessian paper. For example, the computational modeling the case study in the paper showed the researchers how an event that they originally thought was something to minimalize was actually something that was important to their study. Modeling information observed from data collection can lead to findings that were not originally predicted, leading to adjustment and revision of the concepts or hypotheses.

After information or observations are analyzed or tested, revisions are made, and an answer to a question or problem is found, scientists will then use models and representations to explain, and defend, findings or design solutions. In the diSessa paper, it is argued that the computational models act as an explanation for concepts. The researchers in the case study will use the models they created for their data to explain, and possibly defend, their findings to the scientific community. Then hopefully, other scientists will found their own studies on those models. 

1/26 - Kim - Innovation is Key

Nersessian

Conceptual innovation and change are essential to understanding the creativity of the scientific process.  He discusses the different view points on how philosophers believe this conceptual change can occur.  He conducts research about what knowledge is needed to build an epistemology and what methods other than logical and conceptual analysis can we use to achieve this, going into detail about cognitive approaches.  Then he discusses how models figure in and facilitate reasoning about target phenomena.

            A theme I noticed is the importance of problem solving to the scientific process but more so the importance of model based reasoning to enhance one’s problem solving processes.

DiSessa

            DiSessa discusses literacy and how it is an infrastructural aspect to education and to life.  With that, the introduction of computers has completely transformed our society and scientific practices.  He stresses the need for “material intelligence.”  He talks about the three pillars of literacy: material, mental/cognitive, and social.

A theme for DiSessa is most obviously, the important of literacy for not only science, but also for life in general.  Literacy in the sciences will help to enrich one’s understanding of complex processes or phenomena, as literacy in general will help to enrich one’s life.

This innovative aspect of science is so important for students to understand, and I think to aid in this understanding teachers should elect to include historic science pieces in their curriculum.  Not only would this help to show students the reasoning behind how some common concepts, like gravity or space-time, first came about to being understood or to show how someone reasons their way through something new.  It also shows, as Nersessian points out, how a lot of science is continuous and cumulative.  Most new concepts are logical extensions of previous notions.

I think that the modeling and problem solving practices talked about in Nersessian can be vastly enhanced through use of computers, as discussed in DiSessa and Schwartz.  Currently, there are great programs for computational thinking out there, and it will be very important for teachers and students to get regular access to using computers in the classroom.  Both authors discuss the cumulative aspect of science and emphasize a “social pillar” of literacy that people cannot ignore because it has become infrastructural.  I found it interesting that both authors utilized historical literacy in order to delve into their own cognitive views.

1/26 Dan: Computational Literacy

Summaries:

The Nersessian article was a report on a study that attempted to discover how the brain changed when learning occurred.  Specifically, the researches focused on the connections between synapses in the brain and how their communication functioned and changed.  Nersessian focused her attention on how a model of the brain was able to enhance the teams work by making measurable points of the brain much more accessible.  The article highlighted the process of improving the model by starting with a simplified version and then refining it by tuning its behavior to more closely resemble the actual synapses in vitro.  

The diSessa article explores the idea she terms “computational literacy” by exploring what different forms of literacy exist, and how they have changed over time to become infrastructural.  She breaks the term literacy into three pillars: material, mental, and social.  She also cites several examples of how widely accepted literacies, like mathematical literacy, have come to enhance our knowledge.  She uses an example of Galileo’s six theorems of uniform motion to explain how mathematical literacy has enhanced our understanding of this topic, showing modern algebraic expressions have made his complex reasoning widely accessible to students as young as middle school.  Her point was that when a literacy is so critical to a society, it is not just a result or an aim of education, but a key component of the educational process.  Her view (in the late ’90’s) is that computational literacy will become just as critical as reading, writing, or mathematics.

Connections to NGSS:

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.

The diSessa article focused mostly on Practice 8: Obtaining, evaluation, and communication information.  Much the articles discusses how we have chosen to communicate our mathematical knowledge has had a profound impact on the access to it.  In addition to the Galileo example, where she shows how the equation d = rt essentially explains Galileo’s six theorems , diSessa also discusses how the notation of calculus developed and how that has impacted its ability to be taught throughout high schools and universities. The notation that Newton developed seemed arbitrary and was not self-explanatory.  The current notation, developed primarily by Leibnitz, makes the function of the notation clear.  This change makes the topic of calculus more accessible to a wider range of the general population.  diSessa is clear from theses examples how the ability to communicate our knowledge clearly has a profound impact on its effectiveness and accessibility.