CDS’ Arthur Spirling Wins CAS Golden Dozen Teaching Award

NYU Center for Data Science
4 min readMay 8, 2019

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Spirling, Deputy Director of CDS, talks about his approach to teaching and shares advice for students

Arthur Spirling, Associate Professor of Politics and Data Science, has been honored this year with a Golden Dozen Teaching Award, which “recognizes faculty for their outstanding contribution to learning in the classroom.” He sat down with me to talk about his experience in the classroom as an educator and researcher.

1. What do you enjoy most about teaching, and what are the most significant challenges you encounter?

For most teachers, I think there’s a part of them that enjoys the performance — not performance in the egotistical sense, but performance in the service of something they deem important. You get to combine knowledge with the performance of knowledge, which is very enjoyable. I’m not an extroverted person, but I enjoy delivering this combination of entertainment and information — infortainment.

In my entire career of teaching, the biggest challenge has been conveying technical and conceptual material to students who believe that they are not capable or prepared to learn it. A lot of what I have to do is to help them become more confident in their own ability. They have this prior worry that it’s going to be impossible for them to understand the material. It’s my job to convince them that they will be fine and that they don’t lack any special knowledge. I make sure they know that we will get them through it, and by the end of the course they will have learned a lot.

2. How has your approach to teaching changed from when you started to now?

The biggest change is that I try to build in more time for students to contemplate and discuss the material. When I started, I viewed teaching as pouring knowledge into students like a cup. Now I’m very deliberate in slowing down and building in pedagogical exercises. I like to make it a more interactive process so that the students feel more confident in the material. Also, frankly, as you get older, you get more confident in teaching the material, and that’s actually very important.

3. How do your own research interests overlap with or inspire your experience in the classroom?

In my own research, I really like subtle, slightly counterintuitive results from data, and that inspires my teaching because I am able to find peculiar results that are worth considering and thinking about. I’ll give you two technical examples of this. One is called Simpson’s paradox, where in aggregate it looks like two variables are related, but when you look at subgroups that relationship doesn’t exist anymore. I do lots of work on observational data where you have this problem. And the other one I find very interesting is called conditioning on a collider. So, for example, we know that taller people are better at basketball, but when you look at the NBA, taller people are not necessarily the better players. This comes from the fact that when they’re selected into the teams, this has already been completely adjusted for. So, we can think of NBA teams as consisting of two sets of people: taller people who are useful to the team for the “usual” reasons, and shorter people who are, say, great leaders. This type of problem is everywhere in social science data, and you have to think carefully about it when drawing conclusions.

4. What’s a piece of advice you received from a mentor you’d like to share?

In graduate school I had an advisor who told me that there are two types of publications: killer and filler. And you have to know which is which. But I’d like to say something else: people think they’re bad at things, and that becomes a self-fulfilling prophecy. For almost everything you struggle with or believe you are struggling with, you can get better with practice, and you might be surprised by how much better you can get.

5. How do you balance a learning environment that intersects both academia and industry?

The fundamental thing that unites both academia and industry is communication. It’s far more important than you think. Whether you continue in academia or start working at any type of company, you’ll spend your entire career communicating your ideas and results to other people. And it’s often the ability to communicate those ideas that sets you apart and allows you to advance.

6. Is there anything else you’d like to add?

Awards like this one — which I’m honored to receive — go to individuals, but they actually reflect the work of the TAs. Professors have a whole structure behind them. So everything is a team effort, and I’m grateful that I’ve had such great TAs behind me.

By Paul Oliver

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NYU Center for Data Science
NYU Center for Data Science

Written by NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.

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