For the past four years, I’ve taught a social science statistics class,
“Applied Quantitative Research Design.”
Without going into all the details, I teach causal inference (econometrics) and
prediction, including difference-in-differences, fixed effects, survey sampling
and weighting, using R and revolving around exemplary papers in political science
and economics published around 2010-2023, at the level of Angrist and Pischke’s Mastering Metrics.
This year, I thought I’d document some of my reflections on the class and notes on
what I’ve tried,
especially how AI and LLMs should change student learning.
I was fortunate to have the Yale’s Center for Teaching and Learning assign Tina Zeng ‘28
as a Pedagogy Partner to my class, and I thought this blog would be a good way to
document our thoughts as a teaching team (which includes other teaching fellows who may make an appearance later).
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What we tried
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What we noticed
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What we would change
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