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.
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?
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