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Busy day here at the ICTCM. I need both an extended time for brain-dumping and a full night’s sleep, and I think the latter is going to win. So here’s a brief listing, in no particular order, of some of the standout items I’ve learned today.
- I learned first thing this morning that rigorous, scientific scholarship of teaching and learning does actually exist, and it’s being done by Dave Pritchard of MIT. Prof. Pritchard was our keynote speaker this morning. In his words, he has basically forsaken a successful career in atomic physics (in which role he mentored or taught three Nobel laureates) to devote his energies to physics education. His keynote this morning gave me enough reading material for a semester and a whole new outlook on what educational research could look like.
- I learned (through Pritchard’s keynote) that there is a school of thought that says partial credit in math and science courses should not be given, because — and I quote — “Partial credit rewards partial understanding”. More to think about here.
- I learned that, thanks to the research of Pritchard and his cohorts, there is a growing field of educational data mining, or one might say educational informatics, out there, designed to take data from online assessment tools and making observations about student learning. There’s even a journal.
- I learned that the difference between novice and expert behaviors in learning pretty much describes all the issues I’ve encountered with the MATLAB course and other courses I’ve taught.
- I learned, through Scott Franklin’s prezi on this subject this morning, that online lectures can be done that aren’t just lectures.
- I learned that Geogebra is pretty cool, and I’ll learn more tomorrow as I take a minicourse on that software.
- I learned there’s a whole website out there — and probably more than this one — for project-based learning ideas.
- I learned that MATLAB has an interactive GUI…. for creating interactive GUI’s. Definitely something to play with later.
- I learned that Gino’s East Pizza is among the best stuff I’ve ever ate, and the copious amounts of it in my stomach right now are a strong argument for sleeping over brain-dumping.
Tomorrow will be a Geogebra minicourse, as I mentioned, and more sessions which I haven’t mapped out yet. We’re getting sporadic wireless access, so I’m able to tweet a lot. More to come!
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Casting Out Nines 
I’ve done the “no partial credit” thing in math classes before with some success. I relax the grading scale (by a lot – think 80-60-40-20 as cutoffs in the most extreme case) and simply refuse to give any partial credit.
While it is perhaps a bit unfair to give the same zero scores to someone who works the problem correctly except for some minor arithmetic mistake as well as someone who has no clue how to even setup the problem, the fact is, a wrong answer is still wrong.
One thing I do see when no partial credit is given is that the better students will actually start to check their work. I have always shown students how to check their work (in part, by always checking my work on problems I do at the board), but they start to see the importance of it after getting a bunch of zeros on problems they know how to do. Spend the 30 seconds to verify that your solutions to the equation are actually solutions!
Regarding partial credit: for the past few years, I have graded everything based on rubrics which essentially boil down to 3 points for fully correct solutions, 2 points for minor mistakes, 1 point for major mistakes, and 0 points for no work (or, more precisely, they get zero if none of their work makes any progress towards a solution).
Missing 1/3 of the points on a problem due to a minor mistake is generally enough to get people checking their work, and this scale does allow differentiation between the careless and the clueless.
I’ve found that this approach leads to a plethora of Bs and Cs and fewer As than I used to award. I’m fine with that. (Drops and withdraws generally keep the Ds and Fs off the books.)
The reason for using partial credit is because the instructor is assessing understanding and process. The final answer itself is often much less important for assessing mathematical learning. When a student performs analysis, solution process, and gives a final result, if that final result is wrong, but nearly all of the analysis and process was right, then most of the item’s credit should be earned.