What Does Data Science Learning Look Like?

Section What Does Data Science Learning Look Like?

Data science learning is often more visible in how students think and approach problems than in what they produce. Recognizing these thinking patterns helps you support student growth and assess their development across the five data science strands.

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Exploration 56. Observable Signs of Data Science Thinking.

Data Dispositions and Responsibilities (Strand A):

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• Students ask “How do we know that?” or “Where did this data come from?”

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• They question claims that aren’t supported by evidence

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• Students consider who might be missing from a dataset

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• They use phrases like “This suggests...” or “It appears that...”

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• Students ask permission before sharing information about classmates

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Creation and Curation (Strand B):

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• Students plan data collection before starting to gather information

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• They notice and correct inconsistencies in how data is recorded

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• Students organize data systematically for easier analysis

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• They consider whether their sample represents the group they want to understand

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Analysis and Modeling (Strand C):

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• Students look for patterns beyond just finding the “biggest” or “smallest”

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• They explain variability rather than seeing it as a problem

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• Students use data to make predictions and test them

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• They choose appropriate tools for different types of analysis

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Interpreting Problems and Results (Strand D):

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• Students distinguish between correlation and causation

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• They acknowledge limitations in their conclusions

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• Students consider alternative explanations for what they observe

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• They connect data findings back to real-world contexts

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Visualization and Communication (Strand E):

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• Students choose visualizations that match their data and audience

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• They explain what their graphs show in their own words

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• Students adapt their explanations for different audiences

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• They use data to support arguments or tell stories

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Checkpoint 106.

A student looks at a graph showing test scores by gender and says, “Boys scored higher, but I wonder if boys and girls took the same classes or if there were other differences.” Which data science thinking skills are they demonstrating?

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Hint.

Consider what the student is doing beyond just reading the graph.

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Solution.

This student is demonstrating sophisticated data thinking: they’re reading the graph accurately, but then going deeper to consider alternative explanations and potential confounding variables. They’re not jumping to conclusions about gender causing score differences, which shows critical thinking about correlation vs. causation and awareness that data might not tell the complete story.

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Exploration 57. Developmental Progressions in Data Science Learning.

Understanding how data science thinking develops helps you set appropriate expectations and recognize growth:

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Beginning Data Scientists:

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• Focus on concrete, observable patterns (more/less, bigger/smaller)

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• Accept data and claims at face value

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• Prefer simple explanations and single causes

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• Create basic visualizations with support

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Developing Data Scientists:

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• Notice and describe more complex patterns and relationships

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• Begin to question data sources and methods

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• Consider multiple factors that might explain observations

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• Choose appropriate visualizations for different purposes

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Proficient Data Scientists:

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• Systematically analyze variability and uncertainty

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• Regularly question bias and limitations in data

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• Generate and test multiple hypotheses

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• Adapt communication for different audiences and purposes

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Advanced Data Scientists:

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• Design sophisticated investigations independently

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• Integrate multiple data sources and types of evidence

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• Understand and explain statistical concepts in context

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• Use data to advocate for change or solve complex problems

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Checkpoint 107.

What’s the most important indicator that a student is growing in data science thinking?

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Hint.

Think about the fundamental shift in how students approach information and evidence.

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Solution.

The most significant growth indicator is when students develop a questioning disposition toward data—when they automatically wonder about sources, limitations, and alternative explanations. This represents a fundamental shift from passive consumption to active, critical engagement with information. Once students develop this habit of inquiry, other data science skills follow naturally.

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