Teaching and Learning Community of Practice (CoP)
Department of Economics


All years: 2023, 2022, 2021 2020, 2019201820172016 

Summer(ish) 2024

Our Teaching and Learning Community of Practice (CoP) is our chance to talk teaching experiences and strategies with our colleagues over lunch. We typically meet over summer, however this year we are starting over Winter 2024.

The sessions are open to Economics department faculty members on either the UTM or downtown campus and sessional instructors on either campus. If you want to get on the mailing list please email Kripa Freitas (k.freitas@utoronto.ca). 

Other CoP’s on campus: Arts & Science.

We meet in-person on the dates below, noon-1pm in GE 106. 

The sessions are 100% in-person, no recordings or virtual attendance options.  

More events to be added over time. Check back for the latest schedule. If you have ideas or suggestions or want to colunteer to lead a session, send Kripa an email (k.freitas@utoronto.ca). 

Tuesday February 20, 12:00-1pm
Supporting Students in Data Courses
Discussion Leader: Michael Stepner
Abstract: Demand for data analytics skills is high and growing among our students and among the employers and academic programs they are pursuing after graduating. Meanwhile, the technological context is changing rapidly:
programming language preferences are shifting, version control and reproducibility are increasingly emphasized in research and industry, generative AI is in the hands of every programmer, and more and more students can’t locate a file within their computer’s directories. Every professor teaching an economics course with data analytics currently wages a solo effort to get their students through the mechanics of programming—how to install software, load data and run code—before we can focus on the main ideas of the course. Yet many students in 300-level and 400-level classes still arrive at the start of the semester struggling with the mechanics of getting their code running.
In this talk, I’ll present the software environment I developed to teach students empirical data analytics skills in ECO481 this fall semester.
Students programmed in Python, R or Stata to complete two assignments and write two empirical papers. They wrote and ran their code in a web browser using Github Codespaces—which eliminated issues with students having software problems on their personal laptops and facilitated assistance from the professor, TA and fellow students in debugging code in a single shared environment. Students submitted their code via Github, and Github ran their code in the cloud after each submission—which allowed students to get instant feedback on their work and iteratively debug their errors, while teaching them how to create reproducible research. At the end of the term, 75% of the students in ECO481 submitted code that ran cleanly from top to bottom and produced the empirical results for their final essay.
I’ll conclude by proposing that we make this software environment available in other economics courses in the Data Analytics sequence. I’d like to work with professors that are interested in using this toolkit in their courses, providing assistance with setting up assignments and troubleshooting issues that arise during the semester. Ultimately, I believe that a common toolkit will make life easier for us and our students—helping professors spend more teaching time on economics, while helping students produce their data analysis code more reliably and spend more time learning economics.