2/16 Caitlin Mangling models

Pickering’s chapter one is an introduction to how scientists view and practice science. He spends a large amount of time discussing science practice and studies in time. While I am not absolutely certain what Pickering was trying to argue, it seems he is touching on the idea that scientists will change their practice, and theories, throughout studies and over longer periods of time. He says that “…there is no thread in the present that we can hang onto which determines the outcome of cultural extension (pg24).” This sounds a little like Nersessian’s paper, which discusses how science practice includes times when hypotheses and models have to be revised or improved upon. In the study, researchers were using computational modeling to throughout a study on a system of neural cells, and then had to change the models to incorporate new observations.

Pickering seems to be saying that science is uncertain and unpredictable, which is why we have to revise and improve machines and practices. In Netlogo, we have and will be creating models that will have to be ‘tuned’ so they will be able to properly model systems, and be predictive of future outputs. Unfortunately, Pickering does not talk much about how to teach students science, but he seems to be in agreement with the other authors we read about what science practice is and how it is done.

Pickering’s argument about agencies, human and material, is what made the most sense to me in the whole article. Scientific practice is sets of actions to study systems in action. The world is always moving. It seems that Pickering tries, throughout the chapter, and apparently the book, to describe how human agency and material agency work together. In Netlogo, the programming is using ‘agents’ to act out systems representing other systems. In the wolf and sheep program, the agents can be programmed to hunt, eat, die, and propagate in order to represent population dynamics. I am not sure if this is exactly what Pickering was imagining representation and modeling could do, but he did mention how emergence and mangling could help analysis of different aggregation levels (when he was discussing his argument in chapter 7). Aggregation levels being taught through modeling was an idea discussed by Wilensky, and it seems Pickering would agree with this method.

I was not absolutely sure what Pickering was defining the ‘mangle’ as. Was it the interaction between human agency and material agency in science practice? Computational modeling seems to do this well. Would Pickering agree that computational modeling is a good way to teach students science practice?

Science questions: As it is supposed to snow this week, how are air currents and weather tracked, and how could they be modeled?

2/16 David Bergsmith Mangling of Practices

Pickering begins with describing science as a practice and a culture. Then, he discusses how people act as agents and they are always doing things, or interacting with the environment. Pickering says that people have goals and work in the present to reach a point somewhere in the future, while the environment and machines act currently and do not manifest goals. Scientists develop goals and plans and act accordingly. These practices of forming goals and plans should be practiced and developed within the science classroom. Engaging in acting upon goals and plans creates inquiry thinking for students.
Pickering discussed that humans are agents interacting with their environment. This is a concept we have touched on previously. Engaging students in the practice of science is the way to develop young minds to think about science. Science is and has been shifting away from the theory of a body of knowledge to the sociology of science knowledge, as described by Pickering. Science should be taught as a practice of engaged students. These students will act as agents within their classroom as a model for how they will and do interact with their environment.

Many of the models found in the NetLogo library will serve as engagement or enrichment activities for my students. Students may use NetLogo to discover ideas such as titrations or natural selection then apply what they discovered to theoretical work and classroom discussions. Within practice problems such as scenarios and word problems, my students will be able to use their experiences with NetLogo to reference interactions between themselves and the model, such as the embodiment of themselves or another, or the agents within the model and scaffold their knowledge. NetLogo models may then be revisited after some classroom work or possibly laboratory exercises. Through the revision of models, students will be able to construct their knowledge with many personal experiences to reference.

My most emerging question is how to predict what misconceptions will arise in my classroom when making connections between models and practices. Students will use and possibly create models using NetLogo, but at what points will these models create incorrect knowledge for my students? Will these misconceptions be found during class discussion or will the engagement of practices create a false knowledge that my students rely on? NetLogo is quite useful to myself to practice and engage in concepts that I have or have not studied in a few years, although I also studied different fields of science as an undergraduate. However, will these models ever hinder a student’s knowledge by creating false information who have yet to study science as much as I have? Most importantly, how I will as an instructor be able to identify these misconceptions and scaffold a student to create the correct knowledge?

2/16 - Kim - Mangled Models

The machine can only perform as its human agent intended it to perform.  I can apply  this thought of Pickering’s to my teaching through emphasis that although computing and programs are helpful tools, they are only as accurate as humans have programmed/coded them to be.  One can easily see this with the NetLogo tutorial #3 we had to complete; making a mistake in coding or not defining a process led to an error message or the program not completing what you wanted it to do.  I think this would be an important takeaway for students working with NetLogo because it teaches them how to thoroughly understand the code they are writing (in that they need to know what line does what) and also it teaches them about the processes of revision in science.  All things scientific are always up for revision or editing or adding to that process or model in some way.  I think I disagree with Pickering’s thoughts about ‘mangle’ in that, “we have no idea what precise collection of parts will constitute a working machine, nor do we have any idea of what its precise powers will be.”  Don’t we know which programs would work better for certain tasks or is he just generalizing?

The NetLogo model for Climate Change (under Earth Science) would be a great model for the NGSS, ESS2.D Weather and Climate. 

The standard includes:

·      The foundation for Earth’s global climate systems is the electromagnetic radiation from the sun, as well as its reflection, absorption, storage, and redistribution among the atmosphere, ocean, and land systems, and this energy’s re-radiation into space. (HS-ESS2-4)

·      Changes in the atmosphere due to human activity have increased carbon dioxide concentrations and thus affect climate. (HS-ESS2-6),(HS-ESS2-4)

This model shows the flow of energy (particularly heat energy) as it enters the Earth’s atmosphere and reflects off the ground/clouds/CO2 particles.   I like how this model allows the user to adjust albedo levels (I assume of the clouds and the ground) because that is a very important factor to heating and cooling on Earth.  I also like that the greenhouse effect is somewhat represented in this model, however I wish that was more detailed.  I don’t think this model is appropriately named because Climate Change is so much more complex than this model, and I would worry what my students might misappropriate from using this. 

2/16 Steve: Managing the mangle

1.       Article

a.       Science is not scientists observing the world from a place of isolation.  Science is about the interaction between people and the environment.  Science happens when humans have goals and they use machines and tools to try to achieve those goals. Humans make their moves by playing certain tools and setting up certain scenarios, and nature makes its move by reacting to the scenario according to the rules that govern its behavior. Science classrooms should reflect this messy interplay between humans and nature both by providing examples of past interactions and by teaching students how to manage their own interactions.

b.      The practical implications of this idea are many. I think the most important is to impress on students that the real world doesn’t function by equations.  It functions by tons of individuals with agency acting out their roles, and the job of science is to figure out what roles different individuals play and why.  Science shouldn’t just be what we know it should include how we got that information and what is still left unexplained. 

c.       The idea of mangle that Pickering discusses is closely related to what we have discussed about the ever-changing nature of our models.  When we model we must model and then test, model and then test, never stopping but always evaluating our model against the truth.  In science, Pickering claims, the same thing happens.  We design an experiment, then nature shows us what it does in response.  Then we use that information to redesign the experiment until we have pulled the information we want out of nature.  But it is a constantly evolving process where the path is not always direct and we are often surprised by what nature does.

d.      Pickering’s article seems to relate mostly to Nercessian’s.  Pickering mentions how his second through 5^th chapters will explore examples of the interplay between human agency and machine/nature agency, which is basically the story of Nercessian’s lab group.  They constantly made models, watched what happened, and remade their models, and then poked the neurons in new ways to see if their model (which represents human agency) accurately predicted what the neurons did (nature’s agency).

e.       The Pickering chapter related to my own modeling experience by elucidating the messiness of science and how difficult it really is to know what nature will do.  Even though our wolf-sheep models are not exactly nature, they are nature in a way in that there is some randomness involved and they are in the end the result of physical processes like electrons traveling through circuits.  I found that changing the variables of the model resulted in outcomes that I would never have predicted.  It was very difficult to get what I wanted out of the model just by thinking about where I should put the sliders.  This is the mangle of science incarnate; I had to just set up a situation, watch what happened, and adjust accordingly.  It was not a textbook science situation where I added two chemicals together and they had some predictable reaction.  It was complex and richly relational.

2.      Questions

a.       Can the agency model be used to predict other less sciency things like how many people decide to vote, how many Lyft/Uber drivers there will be in a city etc?

b.      What are some of the coolest remaining mysteries of science that are accessible to high school students that we can use to show how messy and mangled science research is?

3.      Modeling ideas: how people decide whether or not to vote based on which way people around them are going to vote, the acceleration of a bungee jumper

4.      How Pickering Chapter 1 relates to what you might do with Netlogo in your classes. How does it not? Netlogo relates to Pickering because it shows how messy science is. Because there is some randomness built into most of the models, the results are not always going to be the same.  This unpredictability is welcomed by Pickering, who would contrast it with experiments designed for the classroom where the results will be the same for everyone and there is little chance of any interesting results.  In the Netlogo system, students feel more ownership because they are controlling the sliders and settings.  They also feel more like real scientists because the result is not known beforehand, just like in real science when it is pushing the edge of knowledge.  It doesn’t relate in that Netlogo is still just a program, and not a real-world experiment, so it is slightly less legitimate than a real messy experiment in nature.  But like all models, though not right it is useful to show students how messy science can be.  Qs: How can we convince students that NetLogo represents a useful model of the situation?  What other examples of Netlogo models advancing science are there other than the monkey head turning angle example?
5.   I would consider using the N-Bodies model in a physics class.  I remember writing a similar program in Java in a computer science class in college and thinking it was very cool.  This model would help students with the content objective Use mathematical or computational representations to predict the motion of orbiting objects in the solar system.  This model would fit so well because it allows students to play around with the way that gravity works in a variety of systems.  Students can tweak initial velocities, number of planets, etc to really see how gravity works not just see that the planets go around the sun in circular orbits. 

 

2/9 Jenna - Leveling Up (Or Down, It's Flexible)

The Wilensky & Reisman and the Wilensky & Resnick articles of this week both investigate how computational models can help students resolve confusions that arise when they attempt to understand complex systems through the system's "levels." In short, models are well-suited for understanding how the behaviors of individual agents in the system (the micro level) lead to the aggregate behavior of the system (the macro level); Wilensky calls this conception of a complex system an "emergent view" of levels.

In "Thinking in Levels," Wilensky & Resnick specifically examine how students come to understand levels in systems and how the notion of levels helps us understand how misconceptions develop. The issues they discuss are ontological ("when is something a 'thing?'" and the role of individual randomness in aggregate patterns) and they can only be reconciled when students take on a new, "level headed" mindset. This new perspective means that learners can more fluidly flow from considering one level to another. 

As I read this piece, I couldn't help but think of code-switching (the linguistic idea of switching between languages or styles of language). The flexibility of mind that results in code-switching is very similar to what is needed for level-switching:

• What is the purpose of each code/level? 
• What interpretation is best suited for each code/level? 
• What does a particular code/level make possible that another cannot?

"Thinking Like a Wolf, a Sheep, or a Firefly" does not explore the meta-knowledge of levels described in the other article. Instead, it describes how the emergent view of levels promotes the learners' embodiment. In the case studies described by Wilensky & Reisman, students put themselves in the perspective of the agents they were modeling to generate hypotheses about individual behaviors. While students sometimes needed to make initial assumptions in order to constrain the imaginative process to a workable size, students were able to revise these assumptions and research them further to ensure they were sound foundations. This embodiment through imagination helped students develop a "level headed" mindset because they could see how sensory information at the individual level led to noticeable patterns at the macro level.

I think something that these articles do really well is explicitly address an educational assumption that I feel most of us hold, which is that there is a "right" answer that students need to come to know and that our job as science teachers is to make sure students have that correct idea. This assumption is much bigger than us - for example, it pervades the standardized testing movement and the idea that student test scores are a reliable measure of a teacher's ability. The articles for this week make it clear that we need to let go of this "ideal" and let go of our students so that they can engage in meaningful inquiry. However, I don't think the authors put forward a strong enough case that the process is more important than the result in order to convince teachers in practice and policy makers. I think the stakes need to be lower for teachers before they'll buy into teaching science as a practice in classrooms, and alternative measures of student aptitude (or teacher quality) will need to be proposed to win over policy makers.

2/9 Laura: "Level-Headed" Modeling

           I really enjoyed both the Wilensky and Reisman and the Wilensky and Resnick articles this week as I think they outlined more clearly the ways in which computational modeling can be incorporated into the biology classroom in a way that supports both computational and biological literacy.  I definitely see the potential of Netlogo as a strategy that is visual, interdisciplinary, revisable, authentic, and predictive, all with lower prior knowledge requirements.  I see a conflict, however in the way the two articles discuss both student perspective and the value of a plurality of model types. 

            While both articles clearly value the individual-orientation of embodied modeling, it seemed Wilensky and Reisman championed the viewpoint in isolation, like it was an inherently better way for students to visualize population dynamics, where Wilensky and Resnick were more aware that both the global and individual points of view are necessary to comprehension of the complex system.  Similarly, in Wilensky and Reisman, embodied modeling is presented as an alternative to classical modeling that is better for certain types of problems, while Wilensky and Resnick I think aim for a more integrated approach, “What is needed is a more pluralistic approach, recognizing that there are many different approaches to modeling.”     

            I think this difference in approach is critical in the ultimate application of both computational and classical modeling in the classroom.  I agree with Wilensky and Resnick, that optimally the methods would be used in concert as each has its own strengths and weaknesses that would, hopefully, balance the students’ experience and create a more complete understanding.  For example, in the population dynamics experiments referenced by both articles, both the embodied and classical methods make assumptions that limit their application.  The classical method assumes carrying capacity of the environment, making it difficult to repeat in a lab and requiring explanation as the model is presented.  The embodied model assumes that individual behaviors are governed by a small set of rules, which is more relatable to students but is not true in the wild.  Therefore, population dynamics necessarily requires students to see individual behaviors alongside population behavior, to see that individuals are not driven by the same rules/desires as a population and that the net result of individual action is not always intuitive.  I think Wilensky and Resnick said it best, in that “the whole is more (or, at least, different) than the sum of the parts,” so in order to understand a concept you need to understand both the micro and macro mechanisms, and the ways in which they interact. 

            I’m interested to hear if people had similar reactions, as I am definitely biased by my background in population ecology.  Additionally, I’m interested to hear people’s thoughts on the following questions:

- Are population dynamics necessarily abstract, or is it a problem with their traditional presentation, similar to our discussion of algebra last week?

- Is there danger in allowing students to anthropomorphize scientific concepts? Or is the only way we can possibly understand something (i.e. our only frame of reference)?

- Is there danger in an individually-oriented view of a system? I see potential for existential crisis down the road as students grapple with the disconnect between an individual’s course and larger driving forces.  

- Two weeks ago we discussed decision process behind choosing rules, how do we help students avoid curve fitting as they choose what rules to apply?

- How do we help students maintain awareness of their assumptions when modeling?

Science question this week:

What nutrients do we necessarily need for survival?  What are the possible the effects of eating a minimally diverse diet? (for example, a friend of mine is eating 30 bananas a day for 30 days... ala http://thebananagirl.com/my-trip-to-banana-island.php)

Also- What happens to candle wax when you burn a candle?

2/9 Dan - Wilensky & Wilensky

Readings:
a. One of the things that struck me in both readings was how these seemingly complex phenomena could be modeled with just a few simple rules. In a classroom, I think that could be a powerful way to show how powerful the rules are the constrain the world around us, both in their simplicity and the way that they afford for so many different variations and iterations to occur.
b. I would be interested to find out what other phenomena in physics, besides waves, could be modeled in levels. It’s not a way that I was ever taught to think about physics, but it is fascinating what other practices and theories I have been closed off from.
c. For me, these articles were among the most convincing in demonstrating the power of computational modeling. In the Wilensky & Reisman article, both examples of modeling showed clear connections to the NGSS Practices for Science Classrooms. There were clear questions that were asked, models were used to carry out investigations and to interpret the results. Models were refined to better fit the data throughout each process. It was a clear example of using modeling to as a way to summarize information, but as a tool to explore a new topic.
d. It seems like the articles we have read so far have all been supporting the same general point about modeling, that it is an effective and powerful way to explore new topics in science, and it provides access to topics that would otherwise require advanced mathematical skills.
e. I am excited to get to work with some more advanced modeling software like NetLogo, where we can use more than one agent. It seems that OneTurtle is limited in its ability to be used as a predictive tool.

Question:
On what kind of level does this approach of modeling need to take place? Can it be something that a few teachers decide to use in their classroom? Or does it need a more widespread implementation, where it is being used in multiple grades throughout an entire school or community to really create the change these articles suggest is necessary?