(Grover & Pea, Sengupta et al.)
The Grover & Pea paper essentially gives insight into where computational thinking is now in K-12 education and gives an overview of some gaps in research as well as priorities for future research. The article gives many viewpoints of how people define computational thinking and what it entails. The article brings up questions about how to assess computational thinking and what teachers need to develop into successful computer science teachers.
The Sengupta et al. paper draws on the similarities between computational thinking and developing scientific knowledge through modeling, reasoning and problem solving in many science and math disciplines. The purpose of this paper was to show, “That the development of scientific modeling in K-12 curricula can be synergistically supported by a science curriculum that is based on computational thinking” (pg. 3). This paper goes into detail about a possible framework that incorporates agent-based computation into K-12 science education. It focuses on two disciplines as examples (Physics and Biology), and explains how and why CT can and should be integrated into science education. The paper then explains a pilot study and examines findings and possible implications.
Both of the readings this week focus on computational thinking and its integration into K-12 education. They also mention modeling and simulation as key parts of computational thinking. The idea of cross-cutting concepts from last week is also mentioned as the readings describe computational thinking as much more than just programming. The sengupta et al. paper goes into much more detail and mentions ideas aligned with what we have been learning in class about modeling, scaffolding, representations, inquiry, revision, and more. The paper even uses Lehrer & Schauble as a reference to a science as practice perspective! The authors clearly understand modeling and how we view it today; I wonder how many questions or discussions these Vandy authors had with Lehrer while composing this research?
I have used computational modeling in an ecology class before to explore population dynamics and found it to be extremely useful. We basically had a program created that reflected the equation used by biologists in population dynamics and then explored what changing each variable would do to the future population. The program let us change variables and then it would graph the population over time and we could visualize what effect each variable had on the population. I think integrating computer simulations and models into K-12 science education would be an effective way to scaffold learning and understanding about certain scientific concepts. However, I don’t think I buy into the idea of learning the actual programing behind the models we are using. I see the potential value of learning the programming as well, but I think it would add an unnecessary challenge to any classroom. The important thought process might be there, but the programming aspect of class might overshadow the content specific knowledge of biology, or chemistry, or physics. The thought of spending 5 weeks of class to learn and explore a new computer-programming program troubles me. I also think a lot more research needs to be done on CT before implementing it in the classroom.