Advanced Data Analysis from an Elementary Point of View: Self-Evaluation and Lessons Learned
Accidentally left in my drafts folder for two months. I still haven't looked at my student evaluations.
Now that most of the final exams are graded, but before I've gotten to see
my student evaluations, it seems like a good time to reflect on the class.
Also, I have had enough May
wine, with woodruff from my garden, that the prospect of teaching it again
next year can be greeted with equanimity.
First, and conditioning everything else, this was by far the largest class
I've taught (70 students), and to the extent it went well it's entirely due to
my teaching assistants, Gaia
Bellone, Shuhei Okumura and
Zachary Kurtz. I'd say I couldn't thank them enough, but clearly I'll have to
do 30--40% better than that next year, when there will be between 90 and 100
students. (Memo to self: does the university allow me to pay bonuses to TAs in
- Getting anonymous feedback mid-semester seems to have gone over pretty
well. Unfortunately from my point of view, the feedback was all over the map,
making it hard to know what to change. I do however need to be clearer about
exactly, what I am lecturing on feeds into the data analyses I am asking them to do each week.
- I am pretty happy with the subject matter and its arrangement. Just as an
undergraduate "introduction to modern physics" class aims to bring the student
up to about 1926, my hope was to bring them, methodologically, up to about
1990. (Statistics is after all a younger field than physics.) Judging by the
final, most of them got most of it, and were able to use it with only minimal
- I should have done more to emphasize the identification/estimation
distinction throughout the class, rather than just at the end in causal
inference. This could I think be done fairly straightforwardly.
- There were three topics I seriously regret cutting:
relative distribution methods
and smooth tests of goodness of fit; time series and longitudinal data
analysis; and hierarchical regression models. I am not sure what I would remove
or shorten from the current curriculum to make room for them.
- The lecture notes
come to about 400 pages, more than half of it new text. From a purely selfish
point of view, I should have written maybe 40 pages, if that, and trimmed my
content to some existing textbook. On the bright side, chunks of it could be
- I was surprised at how many students had no real programming knowledge
(operationally: didn't know how to write R functions). With any luck, the new
statistical computing class I'll teach in the fall will help keep that from
repeating in the spring. Similarly, it would have saved a lot of time for both
me and the students if they'd all had copies of something
- This was the first class I've had at CMU where (attempted!) cheating was at
all an issue. I suspect this was due to a combination of the size and the fact
that it's required for several majors and programs. This raises the question
of whether I need to come up with all-new assignments next year. I am inclined
not to, and just flunk anyone who copies the old solutions, but am open to
- I offered too much easy extra credit on several assignments. The ranking
by grades sometimes noticeably distorted the obvious ranking by actual
knowledge. I do not think the final grades suffered from this, but points must
be revised for the future.
- I didn't give enough opportunities to practice writing, and get feedback.
Perhaps, next year, all of the data analysis assignments will require at least
a page of prose? (I am not sure what I will cut to make time for that on the
- Having "Intro to Nietzsche" in the lecture room right before our class
probably did the students no favors, because it kept reminding me of things
like the passage from The Gay Science about how science ought to
become "the great dispenser of pain".
Advanced Data Analysis from an Elementary Point of View
Posted at July 11, 2011 17:15 | permanent link