Our guest in this episode is Professor David Easley from the Cornell Economics and Information Science Departments. David is a world-renowned researcher and he’s long been a champion of interdisciplinary work. Several years ago he created a brand new cross-field class with computer scientist Jon Kleinberg called Networks, Crowds and Markets. It’s been a huge success and more than 600 Cornell students are currently enrolled. It’s been taught by multiple instructors (currently David is teaching with computer scientist Eva Tardos), it’s been picked up by other schools, and David and Jon even turned it into a book. During our conversation David tells us how the course came about, how it was built, how it’s changed over the years.
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0:00 ⏯ Intro
0:40 ⏯ Welcome. About David and his class. Co-teachers teaching each other. Another “VH-1’s Behind the Course”:
- TBP Episode 5: Sex, Lectures and Videotape With Laurie Santos
- TBP Episode 19: Teaching CS50 With David J. Malan
- TBP Episode 36: Teaching the Financial Crisis as a MOOC and in Person With Andrew Metrick
5:08 ⏯ Networks, Crowds, and Markets. Teaching difficult research to 600+ undergraduates. And teaching another professor’s discipline
8:57 ⏯ The flap-copy version of the course. (The book is free online.) Rethinking society and economics as networks. Financial contagion as one example.
12:02 ⏯ Teaching students to discover Nash Equilibrium for themselves. Plus a super-fun non-dangerous tossable microphone.
16:46 ⏯ Making a very large class personal. Taking the students to lunch (but not all at once). Challenges in measuring the results. Undergraduates as teaching assistants.
20:14 ⏯ Using Piazza as a discussion board. Video lectures from a book become a de facto textbook.
23:52 ⏯ Exposure, practice, and feedback. ‘Good’ lecturing can give students the comforting illusion that they understand the material better than they do. When students think the quiz questions are all there is to know.
27:31 ⏯ Writing the textbook to avoid handing out lecture notes. A ‘sequel’ that is more empirical and asks students to code in Python.
32:13 ⏯ Teaching undergrads vs. grads. Slides vs. writing live.
36:51 ⏯ Doug’s quasi-experiment. Doing a problem in real time–and students taking it the wrong way.
42:38 ⏯ When students have the wrong model of knowledge. Talking to students about “Who cares?”
44:59 ⏯ What’s next? Is there an emerging perspective or a new discipline? Computational social science? Or information science?
52:18 ⏯ Working across departmental boundaries and learning from each other. Teaching mistakes. Using the Socratic method with graduate students.
55:48 ⏯ Signing off.