Open with the purpose: "You've collected results — now we make them speak. Scientists turn data into tables and graphs so patterns jump out."
Model the difference between a clear and a messy results table, then introduce the line graph for continuous data (like foil length vs brightness). Think aloud through building one on the board:
Then model evaluating: compare the result to the prediction ("Did it match? By how much?") and judge fairness ("Did we really change only one thing? What might have made it unfair?"). Model writing one honest improvement.
Using one group's data (or pooled class data) projected for all, build a results table and then a line graph together, step by step, naming each part as you go (axes, title, scale, points, line).
Then evaluate that investigation as a class using three prompts:
- Compare: did the result match the prediction?
- Fairness: what was kept the same, and what might have slipped?
- Improve: name one change that would make it fairer or clearer next time.
Model reading the graph back: "What does this point tell us? What's the overall trend?"
Students work with their own (or class) data to:
- record their results in a clear, titled table;
- draw a line graph with titled, evenly scaled axes and plotted points;
- write one sentence comparing their result to their prediction;
- write one way to make the investigation fairer next time.
Circulate to check axes are titled and scaled evenly, and that the graph type matches the data.
Exit ticket: one way to make the investigation fairer, and why it would help.