A few weeks ago a reporter for the Yale Herald (“Yale’s most daring publication since 1986”) interviewed me about teaching at Yale. We had a long pleasant talk, and the resulting article was just published. Many faculty are quoted, but it seems I was willing to say the most extreme things and thus got fairly high billing. I don’t (yet) regret anything I said, but I do want to flesh out a few points.
I was talking another parent the other day and she mentioned that her first grader gets homework every night. I was a little jealous because, believe it or not, I have fond memories of homework in elementary school. My older daughter’s school believes in kids working hard during the school day and then having fun and decompressing afterward, and they rarely assign homework.
Now that I have a fair bit of data on my students’ performance and participation in the class, I’ve been champing at the bit to start analyzing it. I figured this would have to wait until the end of the semester since my plate is pretty full with class prep and other responsibilities, but the other day over lunch, my friend Edward and I had a great idea: Why not combine the two and analyze the data during lecture? It would make a great introduction to multiple regression and hopefully teach students how to use their study time more efficiently.
My econometrics class has about 150 students, and that’s exactly the capacity of our regular classroom. It works fine for lectures since not everyone shows up and I want them sitting close together as they work through problems. For our midterm exam, however, this would have been just awful. Luckily, I was able to additionally reserve the room across the hall that holds 170. This let me split the class evenly between the two rooms and give everyone space to breathe. And because I used random assignment, I had a natural experiment to test the effect of location on exam performance.
I spend a fair amount of time during class extolling the virtues of evidence-based decision making. The tools of data analysis that I teach are applicable to a wide array of fields and questions, and teaching itself should be no different. We should be using available data to tell us how different aspects of a class are working and inform us about the progress of particular students while there is still time to intervene.
The other day one of my students came up to me after class and asked if I would be holding a review session before our upcoming midterm exam. I said I would not because I was philosophically opposed to review sessions. Maybe this was a little dramatic, but I do think most review sessions are counter-productive.
A good friend asked for my take on student evaluations after seeing this recent NPR article lambasting them, and (not surprisingly) I have a lot to say. The short version is that I agree that student evaluations are imperfect measures of course (and teaching) quality, but I also believe that if you run the data collection process well and read them with care, they can be extremely informative.
The American Cancer Society recommends that everyone get a colonoscopy at age 50 and then every ten years after that. It is an effective way for for doctors to identify colo-rectal cancer in its early stages, but it is usually a painful procedure. Physicians insert a camera into the rectum and push it pretty far up into the colon looking for potential problem areas.
Last week I was feeling pretty good about myself. We were four weeks into the semester and the lectures seemed to be going well. I had written three labs for my discussion sections, and my teaching assistants who “fielded” them seemed to like them. I didn’t have a clear idea of what the students thought of these labs, but I was cautiously optimistic. On Friday I committed to giving a presentation on these labs as an innovation in teaching at the upcoming Yale Technology Summit.