Grade 4 — What's the Story?
Complete instructor pack for the Grade 4 data science course. Ages 9–10. Students move from description to interpretation — reading graphs deeply, calculating averages, identifying trends, comparing groups, and telling evidence-based data stories.
Course Arc — How Sessions Build on Each Other
Classroom Supplies — Keep These on Hand All Term
Most sessions use physical materials alongside calculation and graphing. Stock these once and you're set.
ND-Friendly Tips for Grade 4 (Ages 9–10)
- Post the agenda + a vocabulary anchor chart every session — Grade 4 vocabulary is denser. A visible word wall reduces cognitive load during activities.
- Model one full example before independent work — For mean/median/mode, averages, and stem-and-leaf, always work through one example as a class before students attempt independently.
- Color-code consistently — Two groups = two colors, all session. Categorical = one color, numerical = another. Consistency reduces confusion significantly.
- Physical first for abstract concepts — Number cards on desks before calculating median; finger-tracing line graphs before answering trend questions.
- Scaffold writing with sentence frames — "The data shows ___ because ___." "I notice that ___ while ___." Frames allow content focus without language barrier.
- Allow alternative presentations for Session 8 — Pointing to a chart and speaking 2 sentences is a valid data story. Typed text is valid. Minimize performance anxiety.
- Brain breaks = data challenges — Keep brain breaks in the data context: "Calculate this in your head using today's data." It maintains focus while giving a reset.
- Pair work for survey piloting — Some students struggle with peer interaction in collection activities. Pre-assign pairs and give a structured script: "Can I ask you 3 questions for my data project?"
Other Grade Packs in This Series
Grade 1 — I Notice, I Wonder (Ages 6–7) | Grade 2 — Let's Count and Compare (Ages 7–8) | Grade 3 — Asking Better Questions (Ages 8–9) | Grade 5 — Data Detective (Ages 10–11) | ← Back to Data Science Instructor HubData Tells a Story
Raw numbers vs. meaning · The full data cycle · Why interpretation is hard · Claim vs. evidence
Types of Data
Categorical vs. numerical · Discrete vs. continuous · Which graph fits which data
Designing Better Surveys
Likert scales · Bias detection · Piloting questions · Multiple-choice vs. open-ended
Organizing Numerical Data
Line plots · Stem-and-leaf plots · Spread · Clusters · Range
Understanding Averages
Mean · Median · Mode · Range · When each measure tells the best story
Trends and Patterns Over Time
Line graphs · Reading direction · Interpolation vs. extrapolation · Predicting
Comparing Two Groups
Side-by-side bar charts · Two-column tables · Drawing conclusions · Comparison statements
Our Data Story — Capstone
Full investigation · Two graph types · One average · 3-paragraph data story · Evidence-based conclusions