Avoiding Common Pitfalls

Section Avoiding Common Pitfalls

There are predictable challenges that new data science educators encounter. Learning to recognize and plan for these pitfalls will help your first lesson go smoothly and build your confidence for future lessons.

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Exploration 41. The Most Common Pitfalls and How to Avoid Them.

Pitfall 1: Trying to Cover Too Much

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Problem: Planning lessons that touch on all five strands superficially

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Solution: Choose 1-2 strands to emphasize deeply. Better to do one thing well than five things poorly.

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Pitfall 2: Technology Taking Over

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Problem: Spending more time troubleshooting tools than thinking about data

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Solution: Use simple, reliable tools you’re comfortable with. Paper and pencil work fine for first lessons!

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Pitfall 3: Weak Curriculum Connections

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Problem: Data science activities that feel like add-ons rather than integral parts of learning

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Solution: Start with curriculum content, then find the data angle. Not the other way around.

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Pitfall 4: Questions Without Answers

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Problem: Investigation questions that are too broad, too complex, or impossible to answer with available data

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Solution: Test your question yourself first. Can you actually answer it with the data and methods you’re planning?

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Pitfall 5: No Plan for Extensions

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Problem: Students finish early or want to investigate further, but there’s no plan for what to do next

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Solution: Always have 2-3 follow-up questions ready for students who want to dig deeper.

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Checkpoint 89.

Looking at your lesson plan so far, which pitfall are you most at risk of falling into?

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Hint.

Consider your planned timeline, technology requirements, and curriculum connections.

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Solution.

The most common pitfall for first-time data science educators is trying to cover too much in one lesson. If you’re planning to touch on more than 2 strands or multiple complex concepts, consider simplifying. Remember: one meaningful data experience is worth more than five rushed activities.

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Exploration 42. Building in Flexibility and Backup Plans.

Data science lessons often take unexpected turns because students get curious and want to explore further. This is good! But it requires planning for flexibility.

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Flexibility Strategies:

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Time Buffers: Plan core activities for 75% of available time, leaving 25% for exploration or catch-up

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Stopping Points: Identify natural places where you could end the lesson and continue later if needed

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Extension Questions: Have 2-3 follow-up questions ready for fast finishers or naturally emerging curiosity

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Simplification Options: Know how to simplify your investigation if students are struggling or time is short

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Low-Tech Alternatives: Have non-digital backup plans in case technology fails

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Checkpoint 90.

For your lesson plan, identify: (1) One natural stopping point if you run out of time, (2) One way to simplify the investigation if needed, (3) One extension question for students who finish early.

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