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Educator Professional Development Onboarding to the Data Science Learning Progressions

Hannah Kurzweil

Section Introduction to Digital Tools and Simple Models

As datasets become larger or more complex, digital tools become essential for analysis. Additionally, students can begin creating simple models—ways of representing patterns in data that help make predictions or understand relationships.
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Exploration 20. Digital Tool Progression for Data Analysis.

Elementary Tools and Approaches:
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• Simple graphing tools (online graph makers, classroom tablets)
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• Digital tally counters and basic spreadsheet functions
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• Interactive data visualization websites designed for kids
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• Focus on organizing and displaying data clearly
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Secondary Tools and Approaches:
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• Spreadsheet software (Google Sheets, Excel) for calculations and basic analysis
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• Introduction to coding platforms (Scratch for data, Python basics)
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• Data visualization tools (Tableau Public, simple R/Python plots)
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• Focus on efficiency, accuracy, and handling larger datasets
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Watch this webinar if you are interested in hearing about some fun non-coding approaches to data analysis and visualization to use in your classroom!
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Watch this webinar if you are interested in hearing about some fun lotw-coding to coding approaches to data analysis and visualization to use in your classroom!
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Checkpoint 53.

When should students use digital tools for data analysis instead of working by hand?
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Hint.
Consider both practical factors (size of data) and learning factors (what skills you want to develop).
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Solution.
Digital tools become valuable when: (1) datasets have more than about 20-30 data points, (2) you need to perform repetitive calculations, (3) you want students to focus on interpretation rather than calculation, or (4) you’re explicitly teaching digital literacy skills. For smaller datasets, hand calculation often provides better understanding of the underlying concepts.
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Simple models help students understand relationships in data and make predictions. Even elementary students can work with basic models without complex mathematics.
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Exploration 21. Try This Week: Pattern-Based Models.

Time needed: 25 minutes
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Elementary Approach - Rule Models:
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Students create simple rules based on their data: “Students who walk to school tend to live within 4 blocks” or “Kids who read more than 3 books per month usually spend less than 2 hours watching TV.”
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Test the rule: Do new data points follow this pattern? When does the rule break down?
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Secondary Approach - Trend Models:
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Students identify mathematical relationships: “For every additional hour of study time, test scores tend to increase by about 5 points” or “Plant height appears to double every 2 weeks under these conditions.”
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Test predictions: Use the model to predict what would happen with new values, then collect data to check.
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Checkpoint 54.

Students create a model that says “Students who eat breakfast score 10 points higher on math tests.” What’s the most important limitation they should understand about this model?
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Hint.
Think about what this model can and cannot tell us about cause and effect.
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Solution.
While the model describes a pattern in the data, it doesn’t establish causation. Other factors might explain both breakfast eating and test performance (family income, sleep habits, general health consciousness). Students should understand that models describe relationships but additional evidence is needed to establish cause and effect.
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Checkpoint 55.

What ethical considerations should students think about when using digital tools for data analysis?
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Hint.
Consider privacy, accuracy, and the impact of automated analysis.
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Solution.
Students should: (1) Protect personal information when using cloud-based tools, (2) Understand that tools can make errors or have biases, (3) Be transparent about which tools they used and how, (4) Not blindly trust automated results without understanding how they were generated, and (5) Consider whether their tool use might exclude others who don’t have access to the same technology.
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