In Professor Garfield Newman’s course, I was asked to design a rich learning experience that connected mathematics to “living the good life.” At first, I found the phrase difficult to translate into an assignment. What does the good life have to do with Grade 12 Data Management?
My starting idea was broad: use TTC data to investigate how commuting could be improved. I had several possible directions, including probability, speed, geography, and accessibility, but they did not yet form a coherent learning experience. The project became clearer when I stopped treating transit reliability as only a technical problem and began thinking about what a delay costs people.
Turning delay into a question about the good life
A delayed bus can mean arriving late to work, missing an appointment, losing time with family, or facing an exhausting commute with few alternatives. Reliability is therefore connected to dignity, access, opportunity, and how much control people have over their daily lives. This became the project’s throughline:
How can data help us decide which TTC route should be prioritized for reliability improvements to better support the people who depend on it most?
A funding dilemma with no neutral answer
I designed the experience around a fictional funding dilemma. The TTC could improve only one route, and students would act as data analysts representing different stakeholders: TTC Operations, Toronto City Council, an Accessibility Advisory Committee, or a Community Transit Equity Coalition.
Every group would receive the same data, including delay frequency, average delay, variability, ridership, cost, accessibility, and neighbourhood characteristics. However, they might still reach different conclusions. One route affected more riders, another had longer and less predictable delays, and another served more hospitals, seniors, low-income households, and communities with fewer transit alternatives.
That tension was intentional. I wanted students to discover that data does not make decisions for us. People decide what to measure, which evidence matters, and how competing priorities should be weighted. A mathematically correct model can still reflect incomplete or unfair assumptions.
Making mathematical judgment visible
Students would use probability, measures of central tendency and spread, and expected value to calculate the total rider-delay burden of each route. They would then create a weighted decision matrix based on their stakeholder’s priorities. Before presenting to a simulated Transit Improvement Panel, another group would challenge their criteria, mathematics, and assumptions.
Assessment was woven throughout the experience. Students would record their initial judgments, revise their criteria, interpret visualizations, make an interim recommendation, respond to peer challenges, and explain how their thinking changed. The processfolio was designed to capture the reasoning behind the final answer, not simply the polished recommendation.
Whose lost time counts?
Designing this experience changed how I understood the connection between mathematics and the good life. The goal was not to attach a real-world story to a set of calculations. It was to help students use mathematics to examine a public decision that distributes time, access, and opportunity unevenly.
The question was never only, “Which route has the worst numbers?” It was also, “Whose lost time counts, who has alternatives, and what does a fair decision require?”
That is where mathematics became more than a school subject. It became a way for students to reason about the kind of city, and the kind of life, they believe public systems should make possible.