In the last post, I shared the general process that I developed to teach statistics through the lens of Claim, Evidence, Reasoning. This process was tested and refined through several iterations. The data that I chose for these assignments & iterations was critical to student engagement and learning.
How do I know what kind of data is going to be interesting to students? Well, I ask them. I’ve been asking them for a lot of years. Every data set isn’t going to be interesting to every student, but overall, I have been able to identify and collect pretty good data sets.
In the spring term I used these data sets (and the associated class devised claims):
- Minutes between blast times for Old Faithful (Claim: The time between blasts will be 90 minutes plus or minus 20 minutes.)
- Ages of Best Actress and Best Actor Oscar winners (Claim: The ages of the Best Actress Oscar winners is typically less than the ages of the Best Actor Oscar winners.)
- Box office (opening weekend, domestic, worldwide), critics & audience ratings for “original” movies and their sequels (Claim: Original movies are better than sequels.)
- Juvenile detention/incarceration rates for various types of crimes by sex and race (Claim: African-American males are incarcerated at a higher rate than any other subgroup.)
- Education level and unemployment rates (Claim: People with a higher level of education have lower unemployment rates.)
- Sales of vinyl records and digital album downloads (Claim: Sales of vinyl records will soon overtake digital album downloads.)
- Class measurements such as height, arm span, kneeling height, forearm length, hand span, etc (Claim: Human body measurements are related in a predictable way.)
- Car data including curb weight, highway mpg, fuel type, and engine size (Claim: Highway mpg depends the most on fuel type.)
- Fast food burger nutrition including calories, fat, protein, carbohydrates, etc (Claim: Fast food burgers are unhealthy.)
- Baseball data from the 1919 Chicago White Sox (Claim: The evidence supports the decisions made about the accused players in the 1919 World Series.)
Even with all of these options, students added their own:
- Skateboarding data including ages and birthplaces of known skaters and number of skate parks in a state (Claim: Professional skateboarders are most likely to come from California.)
- Olympic male swimming data (Claim: Michael Phelps is the best Olympic swimmer of all time.)
What’s important about all of these data sets?
They all provide multiple variables and opportunities for comparison. They offer students multiple ways to investigate the claims. They allow students to create different representations to support their reasoning. So, the lesson here is that the data sets used much be robust enough for students to really dig into.
Imagine what could happen if the course were integrated with science or social studies.
Next post: The results