Quick Fixes for Canned Labs (Semester Review Pt 3)

I’d love to get rid of my lab books.  They are the standard, canned variety: the instructions are overly helpful and they ask “known-answer questions.”   But I just couldn’t overhaul them this semester — my free time is between 2AM and 7AM.

I did find 2 quick fixes, though, that made canned labs less bad.

1: Assigning the purpose, not the title.

No, seriously — it made a difference when I stopped telling students to “Do Lab 31”, and wrote the purpose of the lab on the skills sheet.  The skill is now “predict the results of a low-pass frequency sweep.  Build a circuit to test your predictions.”  Which is basically what Lab 31 is about.  (I use a consistent wording, which probably helps too.  “Predict (something).  Build a circuit to test your predictions.”)

Some students thumb through the lab book until they find one that suits their purpose.  Some students just make something up.  In both cases, they now know what they’re looking for.

It gives them some choice about the level of guidance they want.  It gives them a backup plan if they get frustrated and don’t know what to do next.  They can use the lab procedure as a recipe, or they can use it for inspiration.  Having that control seems to improve their ability to assess whether they have met the requirement (“test your predictions”).  It also improves their reading comprehension of the lab book (having a purpose makes things make sense… thanks Cris Tovani!).  If that’s all they needed, why didn’t they read the purpose that’s printed in the lab?  I dunno. (Ok, the purpose is often badly written and buries the lead).

Anyway, even those who use the lab procedure word-for-word are now choosing which words to follow.  What I mean is, they evaluate each step in the lab procedure to find out if it’s necessary to meet my requirements.  I say again — they evaluate each step in the lab procedure.

If they decide that they can demonstrate the skill I asked for without doing step 14, then they figure they’ve pulled one over on me.  I never thought I’d be so delighted to see them game the system.

2: Measuring how wrong the theory is, not how right.

“When things are acting funny, measure the amount of funny.” (Bob Pease, National Semiconductor) .

Now that’s a lab purpose I can get behind: find the funny, and measure it.

What if the goal of the inquiry was not to find the right answer (which after all is already known) but to find out how wrong the right answer is?  In other words, let’s discover the extent to which the theory fails.  This is several kinds of useful: the result is no longer known, and it gives you a gut feeling for how much your experimental data should reasonably diverge from predictions.  It lets the students evaluate the model, instead of being evaluated by it.  It also raises the question, “what else is going on that we don’t know about”?

Yesterday we had a lab on band-pass filters.  About half of the class discovered the parasitic capacitance of inductors.  They were excited about this.  I can’t help thinking that this happened partly because the goal was to test their predictions — not to match them.  (Also, because I don’t require them to fill in the pre-printed table in the lab report, they increased the frequency until the signal generator topped out — just to see what would happen).

Yes, it’s important that they understand measurement error, and assess their lab technique against some known results.  But my students often interpret “sources of error” as a shameful failure, which like other shameful failures should be hidden and/or lied about.  I hope I’m not mangling experimental philosophy by challenging students to develop more sophisticated predictions that take into account the effects of common sources of error. You test the theory.  Don’t let it test you.  If your data doesn’t come up how you predicted, that means the prediction is wrong.  Is your model too simple?  Are you measuring something other than what you thought?  In either case, answer those questions.  Stop trying to make reality match theory. Reality is not wrong. Reality is real.

“The most exciting phrase to hear in science, the one that heralds new discoveries, is not Eureka! (I found it!) but rather, ‘hmm… that’s funny…'” (Isaac Asimov, probably apocryphal)

No cool new discoveries will be made as long as “funny” means “wrong.”


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