Andrew Drinkwater
As many of you may know, I spent the winter as an Adjunct Professor in Engineering at the University of British Columbia.
Overall, it was a really enjoyable experience, one that I’d recommend to others. I was fortunate to have great students who were engaged every week, despite the exhaustion of first term of grad school in what for many was a new city.
I want to start with a few notes of gratitude, which are expanded on towards the end of the post:
My students were outstanding. They were engaged each week, and provided valuable perspectives on the course content, pacing, and applicability for mechanical, civil, and other engineering disciplines. The course was markedly better with their input.
Several people mentored me, including Dr. Ryan Rad (the previous instructor), Dr. David Harper, and Donabel Santos. They provided valuable perspectives, lessons learned, and techniques for being a top instructor.
My wife and kids were great through this experience. It was tough leaving them every Monday to spend my day on campus, but they made everything easier. It’s also pretty neat seeing my toddler get familiar with university in general and UBC specifically.
My team at Plaid: Pat and Melinda kept things humming in my absence and were valuable collaborators for the kinds of learning activities that matter.
Onto the experience in the class.
To me, there are four key roles when teaching a class: preparation and research, teaching in class, assignments and marking, and administration.
Preparation and Background Research
To me, this is the hardest part the first time you teach a course.
Usually, you’re handed either a paragraph plus learning outcomes from the calendar or maybe syllabus with a weekly outline.
It’s rare to be given a fully developed course, at least it has been rare for me.
In turn, you need to prepare materials for each week, plus assignments. For me, this included four assignments plus a final team project, weekly in-class activities, a few quizzes, practice materials, and weekly slides.
My course focused on data quality for about 3 weeks, data visualization with Tableau for about 5 weeks, and predictive modelling with Python for about 5 weeks.
Given my previous teaching experience was teaching Tableau at the British Columbia Institute of Technology, and that I’ve used Tableau since 2007, I had no shortage of ideas for what to include in that section and had to narrow them substantially for the time available.
The data quality section seemed like it would be easy for me, but it was harder than I anticipated to teach.
Python I knew would be challenging for me as it was the first time that I taught it.
While I tried my best to be prepared early, it was a struggle to keep up with while growing Plaid Analytics in our busiest quarter. In the end, I was happy with the materials I prepared, but I look forward to a future opportunity to teach where I have a starting point based on my own experience.
I also tried adding a couple modules focused on Analytics in the Age of AI. This turned out to be trickier than I expected, because AI development is moving so fast. I’d prepare materials a few weeks in advance only to have the world change by the time I was ready to deliver it. But I also got lucky – UBC approved the use of a newer version of ChatGPT for classroom activities literally the week before I was going to ask students to use an AI tool for coding practice. While they had previously approved both Microsoft Copilot and an earlier version of ChatGPT, both of these AI models were substantially out of date so I was pleased we ultimately could work with the latest and greatest.
My biggest debate going forward is how much AI should be integrated into an analytics class, particularly for a leadership-focused master’s degree. Despite some exhaustion, it was clear the students enjoyed learning Tableau and Python hands on, and if we go too far into AI it’s possible we’ll lose some of that foundational learning. I’d welcome your thoughts – we’re all learning right now with how AI is changing our worlds.
Teaching in Class
Not everyone enjoys speaking in front of a room, but I do. The balance to strike is the right mix of hands-on and student-driven learning versus places where my knowledge of industry and entrepreneurship could be helpful to me students. I tried to shy away from spending three hours lecturing so that my students could practice or work in groups, but there were days where I felt the balance could be improved.
Assignment Creation & Marking
When I was hired, I was told that courses like these can consume reasonable or enormous amounts of time, and both marking and class prep are the usual culprits.
For me, the most interesting part of creating assignments was deciding what was important to learn, versus what was nice to learn. The trickiest part of marking was establishing a reasonable rubric, especially as we got further into visualization and predictive models where there are many right answers. I found the act of putting together a rubric and solution key before an assignment helped me uncover places where instructions were unclear or where something didn’t quite add up. Having my TA review these helped further. Yet, there were numerous places where I’d only discover after real students doing the work that something wasn’t described the way I hoped and the result was different than I expected. This led to improvements (as students are highly creative) and opportunities to revise in the future.
Assigning grades was more difficult than I had hoped both because it’s hard to determine the right level and because the systems in place aren’t all that helpful. The gradebook in Canvas, for instance, was much more basic than I was hoping, and I found myself working with a spreadsheet to assemble grades far more than I wish to admit.
Administration
There was a bit more administrative work than I was initially expecting. UBC has a variety of courses that are mandatory for new faculty, many of which were quite helpful (and a few of which were repetitive because I’m a consultant and do privacy training all the time).
Conversely, as an adjunct professor, I had no committees or departmental meetings, no research requirements, and no students to supervise.
I now turn to a few notes of thanks to the team of people who made it possible.
Gratitude:
I’m fortunate to have a supportive family who ensure that I can pursue some of my dreams. Not only was Mika my sounding board on ideas, she ensured our kids got to school, were well fed, and had activities to keep them occupied while papa commuted across the city. As a bonus for me, she even made my lunch, which was an absolute treat as she is a phenomenal cook (DM me if you’d like to see some photos of how we eat).
I’m ever grateful to Pat and Melinda at Plaid for supporting me in this endeavour. They might tell you that I kept up, but I know they made it possible through their hard work and creativity.
Mentors:
I would not have been able to do this without the support of some gracious mentors. I’ve worked in higher ed for a long time, so I have more faculty members than I can count, but I wanted to specifically call out Dr. Ryan Rad who so kindly walked me through his materials from past years, Donabel Santos who helped me see the big picture, and Dr. David Harper who had coffee with me and walked me through teaching philosophies and techniques.
It goes without saying that I’m grateful to UBC for taking this chance on me, and specifically Jola Lekich and the entire team at MEL.
Students:
Last, and most importantly, I want to thank my students. It was an absolute pleasure to work with you, and I appreciate the kindness you showed me this term. There were plenty of moments where I got the balance wrong and assigned an in-class activity that took way longer than planned, or put too many slides in the lecture and ended up talking too fast. But there were also plenty of times where we collaborated, swapped stories about how predictive modelling and AI are impacting our respective industries, and places where your feedback helped me make the next week a little bit better. I am utterly blown away by how far my students came in 13 weeks – from a group where most had never created a data visualization or done programming to teams that found interesting data, told great stories and made compelling visualizations, to using their data to predict what might happen next. Truth be told, I didn’t know it would be possible, but together we did it. I am so excited to see where you change the world following this program.
Considering Teaching?
If you’re considering getting into teaching, I’d highly recommend it. It’s a great way to give back to the community to has helped you grow, and it’s a joy to see students building their futures. It’s not without hard work or challenge, but the end result is professional growth both for you and your students. Give it a try!