Paulo Blikstein complemented this perspective when he described bifocal modeling, wherein the physical and the virtual were blended in models, sometimes by using the physical world as inputs to a model, by calibrating a model, or by comparing the output of model mechanisms to sensor data. He argued that such blending was becoming more common in the practice of science and was also a powerful means of engaging students.

Yasmin Kafai noted an example of the importance of understanding models and their limitations: “Government authorities often use models to make predictions, but people often don’t understand how these models were made, what the parameters are, or what kind of assumptions are underlying them … here we have a really great example … [in talking] about computational thinking for everyone and kind of as a goal for citizenship [in] that citizens need to also understand how decisions are being made and what some of the pitfalls in the models will be.” Wilensky added that computational thinking involves more than using models, experimenting with models, or even constructing them; it also involves creating a culture of model critique.



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