The Wilensky articles touched upon many of the previous article’s and the practices of NGSS’s main ideas, especially the process of asking questions, investigating, creating models, collecting and analyzing data, then going back and fixing models. All of the authors point towards the important computational models as a source of scientific learning.
Main ideas:
· Disciplinary learning should be an active study where students constantly observe, investigate, question, and argue their findings, not readily accepting ideas and theories.
· Computational modeling is a way to bridge the gap between the classroom style learning of biology and the research style learning of biology.
o Allows students to use their prior knowledge and personal experiencesàmakes science more interesting and relatable
o Can grow/build from simple models to complex models
· All science is interconnected and relatedàit is important for students to recognize this.
I, as I’m sure a lot of people, could relate to the opening paragraphs of the “Thinking Like a Wolf…” article by Wilensky, where he discussed the differences between the approaches to the classroom education of biology and that of biological research. In high school and especially in college, we are taught solely to memorize facts and structures, rarely allowed to stop and think about the concepts as related and under the same branch called biology. As a logical result, it makes the process of understanding biology much harder that it has to be. Furthermore, when Wilensky stated that in contrast, researchers approach all data and “facts” as dynamic and ever-changing, never readily accepting it without further testing through modeling. This active quality of research biology that is absent from classroom biology is critical to understanding what biology is and that it is not a stagnant field of study. As we touched upon this last week, if high school biology curriculum were to change to incorporate computational modeling and provide students to be active agents within the field, would it lead them to be more or less prepared for college? While the goal of high school is to graduate students and not necessarily to prepare them for college, as some may not go, how would it prepare those who are going to college?
One question I have is what would this look like for a chemistry unit? Wilensky talked about biology computational models but other topics may not have as many or as interesting computational models. Many chemistry computational models focused on kinetics. When I used Netlogo last semester and was putting together my final project, I had trouble finding an exciting model that the students could use. I felt as though the models could only be used by upperclassmen as they were complex and not as hands on as OneTurtleJar. Furthermore, I felt as though there was less you could manipulate with the models and they therefore would not be a major focus within a lesson. While I say this, I would like to learn more about it and find better ways to incorporate it into a chemistry classroom.
The more I read about modeling and the interconnectivity between levels of concepts and sciences, the more I feel that we should go back to the middle school way of teaching science. I never knew exactly what 'discipline' of science I was learning when I was in middle school. However, yes, none of the concepts were taught as interconnected layers of dependency. If this was added, along with computational modeling to support understanding of the mechanics behind event, then maybe we would be able to get somewhere with science education. Let's learn kinetics along side learning how the skeletal and muscular systems works. Could net logo somehow model this kind of system?
ReplyDeleteAs for a Chemistry unit, there are a few computational models I have explored. These include acid-base reactions where the student finds a neutral pH level, building a voltaic cell and manipulating energy levels of an electron. These models could be helpful to students struggling to make mental models, as I have often found with friends and students struggling in science. These models could be used in a variety of ways, particularly as Wilensky says, to adhere to the strengths and weaknesses of students. Finding the most effective place to use these models in the classroom would depend on the students, hopefully not limited to more advanced students. Some computational models in chemistry would offer an experience to some students to "get the reaction to work right." I experienced a few times in the lab were my yield was very low. Perhaps computational models gives the Chemistry class another opportunity to perform experiments again without using materials.
ReplyDeleteI really like Caitlin's comment about the integration between disciplines of science lacking interdependent layers of understanding. I think the justification of breaking down science into disciplines is that it makes it easier to prepare teachers (in becoming experts of a discipline, rather than several) and to decompose science knowledge into "worth knowing" vs. "worth knowing to get into college." Maybe a better system would be to devise science classes based on general topics - for example, an astronomy class or a living organisms class - that brings in the interdependent layers between disciplines.
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