Respond to the NRC today!

The comments on the National Research Council’s draft Framework for Science Education are due today.  Please do visit and comment on them. Overall, after spending some time reading them, I was impressed.  They’re good.  I liked Dimension 2 on Cross-Cutting Elements quite a bit, and I really liked breaking out the Scientific and Engineering Practices as a separate Dimension.  The biggest flaw for me was not highlighting computation as having a special role as a technology used in science education.

To spur thoughts about what to comment on, I’m posting here some of my responses to the survey questions.

• I commented on the Engineering and Technology Core Disciplinary ideas, specifically “ET4 – In today’s modern world everyone makes technological decisions that affect or are affected by technology on a daily basis. Consequently, it is essential for all citizens to understand the risks and responsibilities that accompany such decisions.”My comment: In ET4, there is a missing phrase after “Consequently, it is essential for all citizens to understand the risks and responsibilities that accompany such decisions.”  I’d add “with the recognition that the technological world is broad, with diverse members, who may not share the same sense of risk and responsibility.”  Students need to realize that technology may be wrong, may lie, may break, and may be used in ways that can harm them — at an appropriate level of understanding depending on developmental progression.
• I recommended adding another ET core disciplinary idea: Students should know about computational technology, as key to scientific modeling practice.  A computational model is one that can be executed on a computing device.  A computational model builds on our ability to create machines that can store values, do mathematical operations on those values, compare values, and take actions based on those comparisons.  These models are limited due the discrete nature of the computational device, e.g., all computational floating point numbers are simply simulations of real numbers, and parallelism in models is a simulation.
• Most of my comments were on the Scientific and Engineering Practices Dimension:
  I recommend adding to the definition of “Modeling” to explicitly require computational modeling. “Computational science” is a third kind of science practice (besides theoretical and empirical). All students should have experience generating data from a computational model, analyzing it, and drawing conclusions from that model.

  To the practice of “Constructing and Critiquing Arguments,” I recommend adding critique of data gathered or generated via computational means.  Students should ask where data on the Internet are coming from and whether those sources are trustworthy.  For example, Wolfram Alpha facilitates a wide range of analyses, but the data sources are nearly invisible.  Counts of “Likes” or “Review Stars” on polls might easily be falsified.

  Under “Collecting, Analyzing, and Interpreting Data,” I recommend including the limitations and affordances of different representations of data. Data generated from a computer model has limitations which should be understood by the student scientist.  Lists of x,y, and z positions vs. velocity and acceleration vectors permit different kinds of analyses and modeling algorithms.

I got a chance to review the ACM’s response.  It’s bold and interesting. The ACM pushes for computer science to be its own disciplinary area, not integrated with engineering and technology.  They also responded with a letter, rather than fit their comments into the survey framework as I did.  The survey framework is constraining — on purpose, I’m sure, to get focused reaction to issues that the committee was most interested in hearing about. My responses are mostly within-the-box, about injecting computing into what they have. The ACM is going bolder, pushing to add computer science as its own thing. I hope it works.