Tuesday, October 28, 2014

Computational Literacy

            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.        

1 comment:

  1. I like your comment about how physical models are irreplaceable in biology, however computational models can provide the critical missing link of dynamics and interrelation that some students struggled with in the interviews. I have used a computer model in ecology class to explore population dynamics and found it to be extremely helpful. The program we used basically took one of the equations ecologists use for population dynamics, allowed users to type in numerical values for different variables and then graphed the population over time. The equation itself seemed pretty complicated, but exploring different scenarios through manipulating certain variables was crucial to my understanding of the concept. It also allowed for a user to not only manipulate one variable, but two or three at the same time so you could really explore the interrelatedness of each part to the concept as a whole.

    I would also agree with you that we are a long way off from compulsory computer science in high school. I can also see how many aspects of computational thinking align with modeling as described by Lehrer. However, I think there needs to be a lot more research done to prove that the concept of computational thinking should be well integrated into the K-12 education framework. I like the use of computer models, but not the idea of programming and debugging code. Although at the end of the day the pilot study showed that computational thinking can be integrated into scientific disciplines (as proven by significant gains in ecology for both groups), it also shows some real struggles. There was no significant gain in either group for the kinematics unit, which is troubling. When discussing their findings from the pilot study they say, “Furthermore, the difference in learning gains between the Scaffolded and Classroom groups indicate the necessity of importance of one-on-one scaffolding to support student learning in a learning environment that integrates CT and science learning” (pg. 33). Keeping in mind that the classroom group had only nine students and one teacher (plus a researcher), it makes me think how much lower scores would be in the real world if there was one teacher, no researcher, and 30 students. Considering how important one-on-one scaffolding is for this computational thinking learning environment, I wonder how successful this perspective would be in an average classroom. I would like to see more research and bigger sample sizes to get a better idea of just how effective this approach can be if integrated into the K-12 science framework.


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