36-402, Advanced Data Analysis, Spring 2015: Self-Evaluation and Lessons Learned

Attention conservation notice: 2000+ words of academic navel-gazing about teaching a weird class in an obscure subject at an unrepresentative school; also, no doubt, more complacent than it ought to be.

Once again, it's the brief period between submitting all the grades for 402 and the university releasing the student evaluations (for whatever they're worth), so time to think about what I did, what worked, what didn't, and what to do better.

My self-evaluation was that the class went decently, but very far from perfectly, and needs improvement in important areas. I think the subject matter is good, the arrangement is at least OK, and the textbook a good value for the price. Most importantly, the vast majority of the students appear to have learned a lot about stuff they would not have picked up without the class. Since my goal is not for the students to have fun⁰ but to challenge them to learn as much as possible, and assist them in doing so, I think the main objective was achieved, though not in ways which will make me beloved or even popular.

All that is much as it was in previous iterations of the class; the big changes from the last time I taught this were the assignments, using R Markdown, and the size of the class.

Writing (almost) all new assignments — ten homeworks and three exams — was good; it reduced cheating¹ to negligible proportions² and kept me interested in the material. It was also a lot more work, but I think it was worth it. Basing them on real papers, mostly but not exclusively from economics, seems to have gone over well, especially considering how many students were in the joint major in economics and statistics. (It also led to a gratifying number of students reporting crises of faith about what they were being taught in their classes in other departments.) Relatedly, having the technical content of each homework only add up to 90 points, with the remaining 10 being allocated for following a writing rubric³ seems to have led to better writing, easier grading, and I think more perception of fairness in the grading.

Encouraging the use of R Markdown so that the students' data analyses were executable and replicable was a very good call. (I have to thank Jerzy Wieczorek for over-coming my skepticism by showing me R Markdown.) In fact, I think it worked well enough that in the future I will make it mandatory, with a teaching session at the beginning of the semester (and exceptions, with permission in advance, for those who want to use knitr and LaTeX). However, I may have to reconsider my use of the np package for kernel regression, since it is very aggressive about printing out progress messages which are not useful in a report.

The big challenge of the class was sheer size. The first time I taught this class, in 2011, it had 63 students; we hit 120 this year. (And the department expects about 50% more next year.) This, of course, made it impossible to get to know most of the students — at best I got a sense of the ones who ere were regular at my office hours or spoke up in lecture, and those who sent me e-mail frequently. (Linking the faces of the former to the names of the latter remains one of my weak points.) It also means I would have gone crazy if it weren't for the very good TAs (Dena Asta, Collin Eubanks, Sangwon "Justin" Hyun and Natalie Klein), and the assistance of Xizhen Cai, acting as my (as it were) understudy — but coordinating six people for teaching is also not one of my strengths. Over the four months of the semester I sent over a thousand e-mails about the class, roughly three quarters to students and a quarter among the six of us; I feel strongly that there have to be more efficient ways of doing this part of my job.

The "quality control" samples — select six students at random every week, have them in for fifteen minutes or so to talk about what they did on the last assignment and anything that leads to, with a promise that their answers will not hurt their grades — continue to be really informative. In particular, I made a point of asking every student how long they spent on that assignment and on previous ones, and most (though not all) were within the univer