Amy Tai, Computer Science
Before Amy Tai's first session of SYDE 223, she reached out to previous instructors and teaching assistants to ask what had worked well. She then surveyed her own students about their goals, adjusted her approach based on their feedback throughout the term and spent her evenings designing activities that made exam preparation feel like something other than dread.
One of those activities became a memorable end-of-term tradition: an elaborate class-wide puzzle built from more than 20 practice sheets, each leading to a letter, the completed message reading "You are going to do great."
That same spirit carried through everything Tai did in the course. Her PhD research in computer vision shaped how she taught and pushed her to prioritize understanding over memorization. "In research, you're constantly asking why something works and adapting ideas to new problems," she says, "so I tried to bring that mindset into the classroom by focusing on conceptual understanding, encouraging questions, and showing how abstract ideas connect to real-world problem-solving."
She also made the course's long-term relevance explicit. Having gone through the engineering at Waterloo herself, she remembered data structures and algorithms as critical for software interviews and wanted students to see that value before it was in hindsight. She organized mock interviews and LeetCode sessions for students searching for co-op placements, helped students refine their résumés and tagged them on LinkedIn at the end of term to support their professional networks.
For one student who entered the course with a long history of struggling with programming, the effect was lasting. "Her teaching reshaped how I viewed myself as a learner in technical subjects," that student wrote, "which is something I had not experienced or expected to experience with a programming course."