A First “Claim” Investigation

A slide showing another version of the instructions

These are something like the entire instructions for a mini-investigation that has taken much of the second and third of our class meetings:

Mess around with U S Census data in Fathom until you notice some pattern or relationship. Then make a claim: a statement that must be either true or false. Then create a visualization (in this case, a graph) that speaks to your claim. Then make one or two sentences of commentary. These go onto one or two PowerPoint slides.

The purpose is severalfold:

• You get chance to play with the data
• You learn more Fathom features, largely by induction or osmosis or something; in any case, you learn them when you need them
• You get to direct your own investigation
• You get practice communicating in writing—or at least slideSpeak
• I get to see how you do on all these things
• We all get to try out the online assignment drop-box

In fact, it has gone pretty well. We started on Wednesday (the second class) with my demonstrating how to get anything other than the default variables. I modeled the make-a-claim and make-a-graph part by showing how to compare incomes between men and women.

Naturally, and not by design, I neglected a whole lot of things that people needed to know in order to do a creditable job on this, but basically everybody is coping. Today (Friday, the third class) I had them turn in what they had so far (to prod progress and to be sure we knew about the drop box. That turned out to be a big hurdle; we got to the bottom of much confusion through direct instruction today).  After that, we spend more time pushing this “mini-project” forward; the revisions are due Tuesday (the next class).

I’ve only looked at a couple of the submissions; I’m quite pleased because (a) they have some obvious skill with, comfort with, and interest in the data and tools and (b) they have a lot to learn—and this will be challenging in a good way. For example, correlation and causation came up right away from their own topics. So I will get to see if I can get a student who is obviously passionate about social injustice to see that the evidence doesn’t actually support the claim as written—and how to

• use data analysis tools to make a stronger argument with the data and
• change the claim to be truly supportable

I am worried that students may look at some of these technology solutions as “the” way to do things rather than as one path among many; as important acts in themselves rather than as instantiations of general principles. But I guess it has to be that way taking this inductive, bottom-up approach.

In general, I’ve liked this. Writing this little reflection, I realize these first few days are kind of my answer to what I (in my ivory-tower days) have called false prerequisites: our tendency to make sure that the cherubs have learned everything they need to know before we let them do interesting stuff. The fear, I guess, is that they’ll waste their time spinning their wheels, doing something too hard, or going in a wrong direction. And although that’s certainly possible, I’m seeing some glimmers of anecdotal evidence to support the glimmers of hope:

• Giving them just enough to get started means that they can start really early.
• If a kid gets what he needs right when he needs it, he remembers it. And I can tell others to see him.
• The data are amazingly rich, yet the fact that everybody is using one data set limits and focuses the class. And the kids find the data interesting.
• The Fathom interface to the Census makes getting and processing the data easy; this has two happy consequences: first, kids can start over if they have to, and can try different ideas quickly; second, it works so well, they think it’s really cool.

Stay tuned; when they’re all in I’ll consider publishing the titles so you can see what comes out when you just let them loose.