05 March 2014

Science Fair Participants

First: Congratulations to Elliott Rebello, winning his category in the Eleanor Roosevelt HS Science Fair.  (The reason I single him out -- he's my intern.  Be sure, though: the work he presented was his.  And it was his presentation and understanding that earned him his place.  Yay Elliott!)

Having judged another year's science fair at ERHS, I'll share some thoughts for participants.  I'm a little emboldened that maybe I know something since Elliott did well.  On the other hand, maybe he did well in spite of me.  Use your own judgement on what ideas to make use of, and how to make use of them.

One note: I never did very well in science fairs when I was growing up.  You don't have to do well in science fairs to do well in science, even more true than you don't have to be good at math to do well in science.  One failing in most of my projects: I was setting about learning what was already known, rather than striking out my own path.  This is an excellent way to learn more, but not to get science fair points.

My base suggestion for any age: try to learn more about the universe, know what you did and why you did it.  Maybe there are points in it, maybe not.  But you'll definitely learn something, which is always good.

For science fairs, the major categories on the official judge's score sheet are: 'Scientific Thought', 'Creative Ability', 'Thoroughness/Clarity', and 'Exhibit Presentation'.  They have some connection to usual professional proposal or paper review criteria (except, mostly, for exhibit presentation).
But we all, and it's interesting that it's all of us given that we come from different backgrounds, even judges in my rather small niche, think differently than this.  We start more like journalists:
  • What did you do?
  • Why did you do it?
  • Why did you do it this way?
  • What did you learn?
  • How would you do it differently?  (given what you've learned)
If you're doing ok at this level, we get more detailed.  But a little explanation of these questions and what we (or at least, what I) look for.  'What did you do' should be a no-brainer for you.  Your challenge is to keep the answer fairly brief, a couple of sentences.  I didn't really learn this until graduate school, when one of my graduate advisors really challenged me.  Very difficult to condense my previous 3 years of work in to 1 sentence.  All those wonderful things I did, for wonderful reasons, and he wanted 1 sentence!  Turns out there's a purpose to this.  Namely, it gives us a very quick starting point for what we're looking at and what kinds of more detailed questions we'll be needing to ask (or checking that you answer in your set piece presentation).

The other side is of the what did you do and why did you do it is -- we like to see some enthusiasm from you for your project.  This can be replaced by acting, if you're a good enough actor, but you probably won't carry out the rest well if you're planning on being an actor.  The rest includes some real thinking and work.  The person who's interested in what they're doing will fill these in appropriately. 

'why did you do it' is a slightly different issue.  There are thousands of ways to investigate the things that you looked in to.  The 'what' gives us the concrete.  The 'why' is where you have the chance to talk about how your project fits in to the larger scheme of doing science -- learning about how the universe works.  The project you do will enable you to do or understand ... well, something.  This is partly addressed in the 'future research', but not really.  It's easy to say what else could be done.  More interesting to us is what's interesting to you about how to follow on.  This is a step in some direction of you understanding the universe, what direction is interesting to you from here?

'why did you do it this way' comes to the tough part of your what you did and how you did it.  Given the thousands (millions?) of ways that your part of the universe could be studied, why did you choose this method?  Maybe it is that this is the only route that you had data for.  That's fine, just say so.  If this the why, it's a good place to mention what you'd do if you had access to some different kind of data that might exist in the future, or that would exist if you had -- more time, better equipment, different equipment, sibling didn't eat the peas you were growing+, ....

That's also where you're displaying your 'creative ability' points.  You're choosing what to do.  This is a creative act.  A hard line for mentors (me included!) is how much to tell the student what to do, and how much to let them figure things out themselves.  Your creative ability is where you're the one choosing what to do, or researching/understanding why the things you're doing (however you got that list) are better than the things you're not doing.

Knowing what you did and why you did it is huge.  Sequencing Neanderthal DNA can be great and interesting.  But if your answers are "I dumped stuff from vial a in to vial b and tossed the result in to a machine." and "Because my mentor said so."  you're going to 'lose' to the kid who snagged data from the web (and knows what it is and means) and analyzed it to see if she could predict something about radio reception on earth*. Whether you're successful in predicting the earth's radio reception doesn't matter -- you knew what you were doing and why.  We try tons of ideas in science, most of which don't work out.  If you're learning this at 14 rather than 34, you're much better off!

Non-digression: Some students had mentors, some did not.  You're not necessarily better off with a mentor than without.  Since one of the things we're judging for is your creativity, you might do better without a mentor telling you what to do.

What did you learn? is the standard conclusions kind of question.

What would you do differently? is where you show what you really learned.  One student today was apologetic about not including all sorts of data she discovered existed, but only after she'd turned in her project statement.  The important part to me as a judge wasn't whether she'd included all possible data -- nobody knows what that will turn out to be.  But that she recognized that some of it (not all) was relevant to the thing she was studying.  This happens all the time in science.  Being able to recognize it happening in your project is good.  Knowing how you'd incorporate it if you did the project again is even better.  Drawing your conclusions on the data you did have is paramount.

This is a subcategory of its own "The conclusion is justified based on the data and results." -- and one of the harder things to get used to in science.  You may be confident that something is true.  And you may be right (turn out to be right once more/better data are available).  But if you don't have the data, you've got to go -- for now -- with it not being true, or not being supported, etc..  Whatever conclusion the data do support.  In reviewing professional papers, I've occasionally been in the position of thinking that the authors were right, but having to say that they didn't have the data to support their conclusion.  This is a difficult point.

But, again, the main thing to me is to learn something.  It's a fun process, and maybe you get a point or three in science fair as well.

+ This didn't exactly happen, but sibling effects have factored in to a number of results over the years.  Consider avoiding this to be part of your creativity points for getting a good experiment done.  One of today's students did address the sibling factor++, and yay him.

* I haven't seen the Neanderthal example exactly, but close enough.  And the solar data project is not exactly what one of today's students did, but close enough for blog purposes.

++ I hope you got a laugh here.  But it's real.  Ocean buoy data gets contaminated by sea gulls perching on anemometers, algae growing on the temperature gauges, and fishing ships sweeping up the buoy along with fish.  Satellite data get contaminated by solar storms, sun glint off the ocean, micrometeors slamming in to the instruments, and so forth. Not siblings, but things which exist and have nothing to do with what you're trying to measure, yet affect your observations.  Dealing with these is important.

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