Timed label-and-data reading
The student is handed a stack of real nutrition labels and health headlines and a clock. Working against time, they read each label cleanly — serving size, % daily value, added sugar, ingredient order — and decide what the marketing on the front of the package is actually claiming versus what the data on the back supports. At the end they separate the science from the sell and justify each call from the evidence in front of them. There is nothing to copy and no key to consult: the labels are real, the time is real, and every call has to hold up.
| Criterion | Not yet | Approaching | Mastered |
|---|---|---|---|
| Label literacy under time | Cannot find the serving size or % daily value, or confuses per-serving numbers with per-package numbers. | Reads the obvious fields but slows on added sugar or ingredient order, or misses one under the clock. | Reads serving size, % daily value, added sugar, and ingredient order quickly and correctly — knowing the first ingredient is the largest by weight. |
| Marketing claims vs. evidence | Takes front-of-package claims at face value — “natural,” “immune support” — as if they were proven. | Senses a claim is marketing but cannot say what the label data does or does not back up. | Separates a marketing claim from what the evidence on the label actually supports, and names which words carry a defined meaning and which are just selling. |
| Correlation vs. causation | Reads a headline’s link as proof of cause. | Suspects a headline overreaches but cannot explain why a link is not a cause. | Spots when a health headline treats a correlation as a cause, and explains what evidence a real causal claim would need. |
| Speed & accuracy under pressure | Either rushes into wrong reads or works so carefully the clock runs out. | Gets most calls right but loses accuracy as the timer drops, or stalls on the hard labels. | Holds both speed and accuracy — reading quickly, catching the tricky fields, and finishing the set without trading correctness for time. |
| Integration (cross-domain) | Treats the reading as isolated; makes no cross-domain connection. | Names a link to history, reading, or statistics but cannot defend why it matters. | Connects the work to evidence-based reasoning — including James Lind’s scurvy trial as a lesson in testing a health claim against data — and defends why the connection matters. |
“The front says ‘supports immunity,’ but the back shows the vitamin C is a tiny % daily value and sugar is the second ingredient — the label does not back the claim. And that headline said soda ‘causes’ low grades; it only found a link, which is not the same as a cause.”
“It says natural and immune support on the front, so it’s probably healthy. I didn’t get to the ingredients — I ran out of time.”
This assessment is AI-proof by design: it happens in person, with real labels and a real clock. No chatbot can read a package it cannot hold, catch a marketing claim in real time, or separate a correlation from a cause while the timer runs. The labels differ from student to student, so there is no answer to look up — mastery is shown by reading and reasoning in person, not by submitting.