Students investigate a question connected to their school experience by collecting and analyzing real screen time and math performance data from their peers. They use scatter plots, two-way frequency tables, lines of best fit, and linear equations to identify patterns, describe the strength of associations, and make predictions grounded in evidence. Working in teams, they create a presentation for classmates, teachers, administrators, and district visitors that explains their mathematical reasoning, what the relationship may or may not mean, and how different students’ access, responsibilities, and daily circumstances might shape the data. The experience builds statistical reasoning, data literacy, and reflection as students critique conclusions, revise their work from feedback, and consider both the limits of their data and the fairness of the assumptions they make.

Students will collect and organize real bivariate data, then represent it in scatter plots and two-way frequency tables with accurate labels and scales. They will identify patterns in the data, including positive or negative association, clustering, outliers, and the strength of a relationship, and use relative frequencies to describe possible associations between categorical variables. Students will create an informal line of best fit, write and interpret a linear equation in context, and use the model to make and justify predictions about math grades from screen time data. Through a group presentation and reflection, they will communicate their methods, findings, feedback-based revisions, and possible reasons the relationship in their data may or may not exist, while also considering fairness, bias, and how different students’ access to time, resources, and technology may shape the patterns they see.

Student teams will create a shared class survey, a cleaned data set, a two-way frequency/relative frequency table, a scatter plot, and an informal line of best fit with a linear equation and predictions. Along the way, they will produce brief analysis notes that identify patterns such as clustering, outliers, direction, and strength of association, plus a draft slide or poster for peer feedback during a gallery walk. The final product will be a group presentation that explains their data collection process, displays their tables and graphs, interprets the slope and intercept in context, and reflects on possible reasons for the relationship, including whose experiences may or may not be represented in the data, what outside factors could influence both screen time and math performance, and why the results should not be used to make unfair assumptions about students. Students will also include a short audience feedback form or survey to gather critique from visiting classes, teachers, and administrators and use it to revise their presentation.

Start with a quick, anonymous class poll on daily screen time, a recent math quiz score, and one access-related factor such as whether screen time is mostly for entertainment, homework, or family responsibilities, then project the results without names and ask students what patterns, surprises, fairness questions, or missing perspectives they notice. Follow this with two short, contrasting student profiles that show different screen time habits, life responsibilities, and math outcomes, and have groups discuss why screen time may connect to performance differently across students’ experiences. Introduce the challenge: teams will collect and analyze their own data using scatter plots, two-way tables, and a line of best fit, then present conclusions to other math classes and school leaders. End by having students generate hypotheses about the relationship, name possible limitations or biases in their data, and consider how context and opportunity may shape the patterns they find.

Host a gallery walk where each group presents a tri-fold, slide deck, or poster showing their survey process, scatter plot, two-way frequency table, line of best fit, predictions, and conclusions about the relationship in their data. Invite other math classes, school and district administrators, and, if possible, families to circulate, ask questions, and leave feedback on a short response form or QR-code survey. Include a brief presentation round in which each group explains the strength of association, outliers, and possible reasons the relationship may or may not appear in their sample, including how access to technology, study time, family responsibilities, or other inequities may shape the data. End with a reflection wall where visitors and students post key takeaways about what the data suggests, whose experiences may be missing or overrepresented, and what limits or biases may affect the conclusions.