Attention conservation notice: Navel-gazing by an academic.
This was my first time teaching our undergraduate course on linear models ("401"). I've taught the course which follows it (402) four times, and re-designed it once, but I've never had to actually take the students through the pre-req. They come in with courses on probability, on statistical inference, and on linear algebra, but usually no real experience with data analysis. Linear regression is usually their first time trying to connect statistical models to actual data — as well as learning about how linear regression works.
I am OK with how I did, but only about OK. The three big issues I need to work on are (1) connecting theory to practice, (2) getting feedback to students faster, and (3) better assignments.
(1) I feel like I did not strike a good balance, in lecture, between theory, computational examples, and how theory guides practice. The last thing I want to do is turn out people who just (think they) know which commands to run in R, without understanding what's actually going on. (As a student put it to a colleague in a previous semester, "The difference between 401 and econometrics is that in econometrics we have to know how to do all this stuff, and in 401 we also have to know why." This was not, I believe, intended as a compliment.) But based on the student evaluations, and still more the assignments, there're still students who are a bit fuzzy about what "holding all other predictor variables constant" actually means in a linear model. But then again, based on student feedback I persistently have a problem connecting mathematical theory to data-analytic practice; more serious re-thinking of how I teach may be in order.
(2) Students need faster and more consistent feedback on their assignments. We were somewhat constrained on speed this semester by a labor shortage, but I could have done more to ensure consistency across graders.
(3) Too many of the assignments were based on small, old data sets from the textbook. Mea culpa.
This was the first time we had two sections of 401, with two separate professors. I think we did OK at coordinating them, and I take full responsibility for all the failures and glitches. (I should add, because I know some of the students read this, that grades were curved and calculated completely independently across the two sections.)
I am very grateful for the work done on designing the curriculum for this course by my colleagues. Still, I feel like a lot of the course was spent on (to be slightly unfair) special cases which people could work out in closed form in the 1920s, and pretending that they had relevance to actual data analysis. (Cf.) The Kids do need at least a nodding acquaintance with that stuff, because people will expect it of them, but I would rather they be taught it as a nice bonus rather than a default. This would mean a lot more re-design that I put into the course.
Relatedly, I came to have a thorough, almost personal, dislike of the textbook, but that's another story.
Some things which did go well:
I'll indulge myself by ending on on an "achievement unlocked" unlocked note. This was (so far as I know) the first class I've taught where a student's response to one of my lectures was to ask Reddit "Is there any truth to this?". There can be few better proofs that I reached at least one of my students and inspired them to think critically about the material. I am being quite serious when I say that I wish something like this happened every week in every course.
Posted at January 09, 2016 22:38 | permanent link