SectionDeveloping an Investigative Mindset and Student Data Agency
The best data scientists aren’t necessarily the most technical—they’re the most curious and persistent. They ask follow-up questions, notice unexpected patterns, and aren’t satisfied with surface-level answers. They also see themselves as capable of asking important questions and finding answers through data.
This video shows practical ways to guide students through the process of making observations, asking questions and answering those questions with data.
How it works: Start with any simple observation or data point from your subject area. Students practice asking “But why?” and “What else could explain this?”
Example Chain (Science/Elementary): Observation: “Most kids in our class like sunny days better than rainy days.” → “But why do they prefer sunny days?” → “Do kids in places where it rains a lot feel differently?” → “Do adults feel the same way as kids?” → “How do people’s daily activities change with weather?”
Example Chain (Social Studies/Secondary): Data point: “Social media usage among teens has increased 40% in the past five years.” → “What factors contributed to this increase?” → “How does usage vary by geographic region or economic status?” → “What were teens doing with that time before social media?” → “How do we measure the effects of this change?”
Starting with “Students in our class spend an average of 2 hours on homework each night,” what would be a good follow-up question to continue an investigative chain?
Good follow-up questions could include: “How does homework time vary by subject?”, “What factors might explain differences between students?”, or “How has this changed over time?” The key is asking questions that could lead to further data investigation rather than making judgments or stating personal experiences.
Data agency means students see themselves as capable of asking important questions and finding answers through data. They don’t just consume information—they create it, question it, and use it to make their world better.
Step 3: Support investigation planning - “How will we collect this information fairly? What do we predict we’ll find? Who should know about our results?”
Step 4: Celebrate their expertise - “What did you discover that adults might not know? How could this information help make decisions? What would you want to investigate next?”
Help them transform their interest into investigable questions. This maintains their ownership and motivation while building their skills in formulating data questions. Rather than providing pre-existing datasets or changing their topic to something easier, we honor their curiosity and help them develop the tools to investigate what they care about. This builds both skills and confidence.
Elementary example: Third graders survey classmates about lunch preferences and present findings to cafeteria staff, leading to a new menu option. What data science skills are they developing?
High school example: Students analyze local census data to understand demographic changes in their community and present findings at a town hall meeting. How does this demonstrate data agency?
In both cases, students see their questions as worthy of investigation and their findings as valuable to decision-makers. This is the heart of data agency.
