Upper Schoolers Use Data to Examine Global Warming
As Headmaster Lagarde stated in his address to faculty at the beginning of the year, “Regardless of the field one goes into, data literacy — the ability to read, analyze and use the ever-rising tide of information — will be a must.” To learn these skills, students need practice with real data that is relevant to the world and their lives. Mr. Andrew Otero challenged his Algebra I classes to make persuasive, data-based arguments about global warming using mathematical models.
Ubiquitous and Essential
Mathematical modeling is a form of applied problem-solving widely used in scientific and technological disciplines — it’s everywhere. These types of models help us predict the weather, keep our transit systems running smoothly, prepare for population growth and disease outbreaks, and more. Using data from the past, models can represent just about anything in the tangible world, allowing us to make reasonably accurate predictions about the future.
Practical Application and High Expectations
In developing this project, Mr. Otero was inspired by a mathematical modeling course he took in college — for a freshman-level high school class, the assignment is ambitious. The objective is for students to use data to find a direct correlation between CO2 emissions and the temperature of the earth. After studying linear equations inside and out, our Algebra I students were ready to put it all into practice.
Using data from Earth System Research Library and Met Office Hadley Centre, students plotted annual CO2 concentrations and annual global differences in temperature from 1959-2018 on separate graphs, creating best fit lines and linear equations for each. They then examined the relationship between CO2 and temperature by setting the two equations equal to each other, removing year as a variable. The resulting equation forms a model of how CO2 and temperature correlate through time.
Defending Your Work
An important part of this project was the final presentation of evidence. Students compiled their work and explained each step to a crowd of their peers, several teachers, Headmaster Lagarde and Upper School Head Mr. Steve Soden. In addition to demonstrating their mastery of linear equations, they drew larger conclusions based on their research. Every student in the class found a causal relationship between CO2 levels and temperature increase, although this was not a foregone outcome. Mr. Otero reiterated that students could form any conclusion as long as the data supported it.
“We are working on linear equations, but I want them to see how that relates to the real world. In being persuasive with their research, they are learning how to make a case and back it up with data. They can say this isn’t a good determination of global warming, but they have to back that up with evidence, explain what errors they found and offer ideas about what would work better.” — Mr. Otero
As the groups showed their graphs to the class, it became apparent that scale matters when visualizing data. Some groups chose a larger scale which minimized the audience’s perception of temperature increase over time. Others chose a smaller scale, telling a different story. Their research and arguments were similar, but their graphs made drastically different impressions on the crowd. Because most people are not mathematicians, they rely on the visualization to illustrate the evidence. Those details can make the difference between a persuasive or failing argument.
Data Literacy in Context
Research-based mathematical modeling is a powerful way to teach students how to use data — it has big real-world implications. How often do we see someone present a chart or graph to legitimize a claim or decision? We frequently treat data as objective, factual information, but it can be manipulated to support any number of points of view. Understanding these underlying concepts gives our students the tools to develop informed opinions based on real evidence.