Thinking about "Practice" and "practices"

In week 1, we read two accounts of scientific modeling in action, in which secondary school students used computational, agent-based models to investigate a number of different complex systems. We discussed some of the immediate advantages to modeling exercises and pragmatic obstacles to implementing modeling in the K-12 classroom.

Our conversation about the affordances of modeling was directed mainly towards agent-based modeling. But Collins describes many more types of models that are used in science. Agent-based modeling is just one type of behavioral model, while Collins also lists examples of structural and functional/causal models. Not all of these models are conducive to thinking about complex systems, nor are they all equally viable as computational (i.e., programmable) models. But, the wide variety of models he names are useful for framing conversations about the range of scientific practices we want students to engage with. His article as a whole, outlining the process of scientific inquiry, complements the proposals of the Next Generation Science Standards for reforming science classrooms. Therefore, his list of models is a powerful tool for arguing for modeling throughout the science curriculum, and for evaluating potential scaffolds for instruction.

We also discussed obstacles to model implementation, one of which was the limitation of assessments to evaluate students’ thinking in relation to modeling. Standardized assessments are well designed for measuring learning from direct instruction (of bodies of factual knowledge), but they are not as well suited for measuring learning from guided discovery experiences, such as modeling (which ask students to participate through disciplinary practices). These disciplinary practices, at the heart of modeling, are the procedures and processes that Collins describes in his article as the practice of inquiry. Collins does not use “practice” in the same sense as Pickering does when he refers to “scientific practice;” in Pickering’s case, “practice” refers to the overarching scientific culture, although he uses “practices” in the plural to refer to the everyday procedures of scientists in the discipline.

As I reread Pickering, I was able to clarify a confusion that I had from one of my initial read-throughs. Originally, I recognized that Pickering has a heavy emphasis on machines when he talks about “material agency,” and I had trouble applying his theory to agent-based modeling, which typically deals with other living beings or chemical relationships found in nature (which in some sense can be brought under human control). I wonder if, when Pickering mentions human vs. nonhuman agency, what he is lumping into “non-human.”

While I agree with most of Pickering’s assumptions and find them to be useful points of analysis, I don’t think he gives enough attention to the limitations of human cognition and the individual differences between people in his account. I recognize that is a little beyond the scope of his argument, but it is necessary to consider for instructional and design purposes. If we are going to make the image of modeling presented in the Wilensky articles a wide-scale reality, we need to discuss how Pickering’s – and Collins’ – ideas can be useful for designing needed scaffolds for both teachers and students to implement modeling successfully.

Week 2: Modeling by scientists and by students

All four of our readings have emphasized emergent levels within scientific knowledge and practice. Last week, in “Thinking in Levels,” Wilensky defined emergent levels as “levels that arise from interactions of objects at lower levels." He explained levels in the context of science content, focusing on examples in biology, chemistry and physics, and ecology.

This week, Pickering explained that the practice of science itself also consists of emergent levels in “the Mangle of Practice.” On page 22, Pickering describes the “dance of agency.” This dance consists of human scientist agents and non-human machine agents that interact in an emergent process. Scientists attempt to capture material agency with their machines, and the machines fail and present opportunities for revisions and accommodations to scientists' theories, models, and technology. The scientists’ actions emerge from their observation of current machines and from their interaction with a higher “level:” the aggregate human society and its goals for the future, modelling new technology in accordance with these goals. The machines’ agency emerges from the practices of humans and the intentions of the humans. In this dance, individual and aggregate levels contribute to emergent outcomes in the development of culture and technology. 

Collins reiterates Pickering’s ideas about the mangle of practice with his description of design science, which “attempts to design systems that have desired properties” (human intentionality), while drawing on present understandings of the natural world (nonhuman agents).

Pickering also connects to Wilensky’s point that randomness on one level could lead to a desired outcome on another level, explaining on page 24 that, “captures and their properties just happen,” and that we have to find out, in practice, through “brute chance,” how scientists and machines will develop.

Like Wilensky, Collins argues that students in K-12 schools do not receive a science education based on complex or emergent systems and argues that people need a better understanding of the way scientists approach models in “an increasingly complex world.”

Though Collins described four types of scientific knowledge (theories and models, forming questions and hypothesis, designing and carrying out investigations, and data analysis), I thought it was interesting that all four types of knowledge were rooted in modeling. Models were used to generate and answer questions, were formed from exploratory studies and evaluated in confirmatory studies, and were generated from data analysis. 

The emphasis on modeling in the chapter underscores the importance of modeling in K-12 classrooms to me. I was excited by the quantity types of models described within the chapter. This made me realized that although I focused on modeling as a teacher, I only exposed my students to a narrow slice of scientific modeling through agent-based modeling. These other types of modeling present many more opportunities for modeling as part of science education. I wonder how best to implement these in a classroom – would it be best to model the same phenomena in different ways? Match different phenoma to appropriate modeling strategies? Use the same type of model throughout a year to develop students’ skills with that type of model?