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.
Over the weekend I surveyed my class to ask them how it’s going. I had heard a few rumblings of discontent about some specific issues and I was curious if there were any other issues I didn’t know about. I strongly believe that if you are going to go to the trouble of collecting student feedback, you should also take the time to read it, analyze it, and most importantly, respond to it. Addressing the issues raised in the survey was almost trivially easy, and the changes I’ve made will make a huge difference to most of my students. I’m so grateful that I didn’t wait for end-of-semester evaluations when it would have been too late to fix anything.