One of the things I don’t like about most of the modeling curriculum I have experienced is the tendency to jump into taking data. In the summer workshops I attended at ASU and the several mini-workshops I have attended elsewhere, I was pretty much always asked to go take some measurements of things.
(1) Go measure some distance and time data
(2) Go measure some force and distance data
(3) Go measure some current.
(4) Go measure some frequency and length data.
And then we graph the data and look for relationships among variables. Next, we’ll often try to make a best fit curve to model the situation and interpret the meaning of the parameters in the equation.
The problem is that I always felt like I was just measuring things and looking for relationships without purpose. There were no puzzles we had identified as worthy of my epistemic curiosity. We had identified no perplexing questions that made me wonder, “what kind of data should I take to help answer that question?” What relationships am I expecting and why? How will I know if the measurements I’ve taken are good enough to either support or refute one idea or another? None of that was going on–not for me at least. And that was the thing that was puzzling to me.
Now don’t get me wrong–empirical data is important in science. But it alone is not science, at least not to me. I have been more apprenticed into starting science with something perplexing and letting that perplexing situation be the source of ideas, arguments, and explanations that need to be sorted out. In my mind, there’s now a reason to take data–that data will be EVIDENCE to support for claims. To me, the the subtleties that entangle and distinguish data and evidence are crucial for understanding the nature of science–both to do science and to be scientifically literate.
Leslie Atkins is fond of this quote that supports this view:
Observation and experiment are not the bedrock on which science is built, but rather they are the handmaidens to the rational activity of generating arguments in support of knowledge claims. (Driver, Newton, & Osborne, 2000, p.297)
Now, I’m certainly not saying that scientists never just muck around with data, or discover interesting things by looking for at relationships, or plotting data, or trying to make sense of mathematical equations. So what am I saying? I’m not exactly sure. Maybe, I’m asking someone to explain to me what I’m not understanding about the modeling curriculum.