The readings this week focused on the implementation of computational thinking in high school curriculum. In Grover and Pea, the authors presented a synthesis of contemporary definition and debate of computational thinking as a discrete topic with little reference to it’s implications for other subjects. Sengupta et al. take the problem solving mechanisms of computational thinking one step further and envision their integration into science curricula. Using kinematics and ecosystems as sample contexts, Sengupta et al. walk the reader through their “three world” system and clearly map the ways in which carefully scaffolded computational thinking can improve student comprehension of complex systems.
I found the argument in Sengupta et al very compelling, and think that the process of abstraction and pattern recognition can be readily incorporated into Lehrer’s modeling cycle. During the “construction world”, students can define and create their own models, test them in the “enactment world”, and finally modify them during the “envisionment world”. Besides readily supporting active modeling, computer models can additionally help students envision complex interactions among elements in a system that would otherwise require many physical and two dimensional models to represent. While I think that physical models are irreplaceable in biology, computational models can provide the critical missing link of dynamics and interrelation that my students struggled with in the interviews. I’m excited to learn more about the programs in class and potentially include the ecosystem program in my food web curriculum to help explain trophic cascades and keystone species.
In comparison, the Grover and Pea article seemed a little limited in it’s vision for computer science in the classroom. I think we are a long way off from compulsory computer science in high school, but agree that computational thinking is very valuable in modern society and across many disciplines, so it makes more sense to me to incorporate computational theory into existing math and science courses. Similar to highlighting engineering in biology, chemistry, and physics like we talked about last week, incorporating computer science into the other sciences can help students see the interconnectivity between sciences and the application of computer models across disciplines. By exposure to computer programming in a well-scaffolded way like Sengupta et al. suggest, you can counteract the issues of challenge and disinterest that Grover and Pea raise. When students see that computer models are fun, accessible, and helpful for a wide variety of problems, they may be more likely to choose a computer science elective and subsequently achieve the goals that Grover and Pea set for computational literacy, avoiding the common challenges to novices and skipping the unnecessary pandering to ‘sexed’ interests like gaming or sewing.