Data over Space and Time: Self-Evaluation and Lessons Learned

Attention conservation notice: Academic navel-gazing, about a class you didn't take, in a subject you don't care about, at a university you don't attend.

Well, that went better than it could have, especially since it was the first time I've taught a new undergraduate course since 2011.

Some things that worked well:

1. The over-all choice of methods topics --- combining descriptive/exploratory techniques and generative models and their inference. Avoiding the ARIMA alphabet soup as much as possible both played to my prejudices and avoided interference with a spring course.
2. The over-all kind and range of examples (mostly environmental and social-historical) and the avoidance of finance. I could have done some more economics, and some more neuroscience.
3. The recurrence of linear algebra and eigen-analysis (in smoothing, principal components, linear dynamics, and Markov processes) seems to have helped some students, and at least not hurt the others.
4. The in-class exercises did wonders for attendance. Whether doing the exercises, or that attendance, improved learning is hard to say. Some students specifically praised them in their anonymous feedback, and nobody complained.

Some things did not work so well:

1. I was too often late in posting assignments, and too many of them had typos when first posted. (This was a real issue with the final. To any of the students reading this: my apologies once again.) I also had a lot of trouble calibrating how hard the assignments would be, so the opening problem sets were a lot more work than the later ones. (In my partial defense about late assignments, there were multiple problem sets which I never posted, after putting a lot of time into them, because my initial idea either proved much too complicated for this course when fully executed, or because I was, despite much effort, simply unable to reproduce published papers*. Maybe next time, if there is a next time, these efforts can see the light of day.)
2. I let the grading get really, really behind the assignments. (Again, my apologies.)
3. I gave less emphasis to spatial and spatio-temporal models in the second, generative half of the course than they really deserve. E.g., Markov random fields and cellular automata (and kin) probably deserve at least a lecture each, perhaps more.
4. I didn't build in enough time for review in my initial schedule, so I ended up making some painful cuts. (In particular, nonlinear autoregressive models.)
5. My attempt to teach Fourier analysis was a disaster. It needs much more time and preparation than I gave it.
6. We didn't get very much at all into how to think your way through building a new model, as opposed to estimating, simulating, predicting, checking, etc., a given model.
7. I have yet to figure out how to get the students to do the readings before class.

If I got to teach this again, I'd keep the same over-all structure, but re-work all the assignments, and re-think, very carefully, how much time I spent on which topics. Some of these issues would of course go away if there were a second semester to the course, but that's not going to happen.

*: I now somewhat suspect that one of the papers I tried to base an assignment on is just wrong, or at least could not have done the analysis the way it say it did. This is not the first time I've encountered something like this through teaching... ^

Data over Space and Time