During the last month, my TA and I have created quizzes for all 13 modules in the class. Each quiz is made up of 10-15 multiple choice questions that test whether the student is absorbing the main points of the video lectures. The students get practice applying and combining the concepts with more difficult exercises in their homework. I think the quizzes have been successful, although with just 8 students in the class it’s hard to tell for sure.
We reached a big milestone Friday as my students read their first primary research articles and we discussed them during our live session. Coming into the course, most of my students knew that correlation doesn’t imply causation. For example, just because cities with more police tend to have higher crime rates doesn’t imply that hiring more police induces people to commit more crime. In a sense, econometrics is just a set of statistical methods for going beyond correlation and estimating actual causal effects.
First Mile Communications was an Atlanta-based telecommunications firm that sold DSL Internet connections to local residents. Potential customers would call, and based on their address, First Mile would compute the straight-line distance to the nearest Central Office (CO). Using that distance, First Mile would approximate the length of the copper wire that connected the location to the CO. The standard rule they used was to multiply the straight-line distance by the square root of 2. This wire distance would determine how fast an Internet connection the firm could promise the customer.
At the end of every semester, I take the last 15-20 minutes of the last class to get feedback about what went well and what could be improved the next time I teach the class. I find it more useful and constructive to do this as a discussion than to wait for anonymous evaluations (which are valuable in other ways). Because I’m trying so many new things in my online class, I did a midterm feedback session last Friday. Here’s what I learned:
Last summer I complained bitterly about how we implemented exams in my online class. It was a multi-step process that started with me emailing all my students the exam as a PDF. They wrote their answers on paper in front of their webcams while my TA and I watched for signs of foul play. After two hours, they took pictures of their work with their phones and emailed us the results. Overall, it was a perfectly reasonable experience for them.
Teachers make the world better every day by helping students grow and learn. We teach our students valuable skills, show them new perspectives on the world, and mentor them as they face the variety life’s opportunities and challenges. And we get to share some of the glory when these students go on to do amazing things after they leave our classrooms. This spring I came across two new classes at Yale where students and faculty worked together to make an immediate positive impact on the external community.
In traditional (in-person) courses, it’s very common for students to talk outside the classroom about the concepts and issues that come up in the classroom. This often leads to studying and working on problem sets together. I’m a big believer in the value of peer-to-peer interaction and the learning that happens when students explain things to each other. Unfortunately, this kind of learning is a lot less common in online classes, as it’s harder to get to know your fellow students during live video sessions (assuming the course even has them), and there’s minimal chance of students running into each other outside class.
I’m afraid today’s report is more of a rant and is a little light on the teaching tips. Given that we’re playing close to the technological bleeding edge, it was bound to happen. I love to tell my friends who ask me for computer advice to buy a Mac because it “just works.” It didn’t just work today.
In our first intensive week of Econometrics and Data Analysis, we covered the basics of probability. My students can now model uncertain processes mathematically and use their models to answer questions. One prototypical example I teach is about an ATM machine. We assume a number of individuals make withdrawals in a given day and model the probabilities associated with the possible amounts of each withdrawal. We use the model to figure out how much money the bank should put in the machine at the beginning of the day such that it is pretty unlikely that it will run out. We also talk about the weaknesses of the model and how we might extend it to allow for an uncertain number of customers.