Timed data interpretation
The student is handed an unfamiliar dataset and a clock — melt-time against water temperature, say, or plant height across a run of days. Working against time, they organize the numbers, choose a graph that fits, and read the pattern the data reveals, deciding which comparison actually matters. At the end they state a conclusion the data can support and defend it from the evidence in front of them. There is nothing to copy and no key to consult: the dataset is unfamiliar, the time is real, and the conclusion has to hold up.
| Criterion | Not yet | Approaching | Mastered |
|---|---|---|---|
| Organizing the data | Leaves the numbers as a jumble; cannot say which column is which. | Sorts the data but slowly, or mislabels a column and loses track of it. | Quickly arranges the data into a clean table, clearly labeling what was measured against what. |
| Choosing the right graph | Picks a graph that does not fit the data, or makes none at all. | Chooses a workable graph but mislabels the axes or the scale. | Picks the graph type that fits — a line for a trend over time, bars for categories — with axes and scale correct. |
| Reading the pattern / trend | Cannot describe what the data shows, or reads a trend that is not there. | Spots the general direction but misses where it changes or levels off. | Reads the trend accurately — where it rises, falls, or flattens — and describes it in plain terms. |
| Uncertainty & outliers, honestly | Treats every point as exact, or quietly drops the ones that do not fit. | Notices an outlier but cannot say what to do with it. | Flags outliers and the spread honestly, and reasons about whether a point is real or noise without erasing it. |
| Defensible conclusion in the time given | Runs out of time with no conclusion, or states one the data cannot support. | Reaches a conclusion but overstates it, or cannot tie it back to the graph. | States a conclusion the data supports, ties it to the graph, and delivers it within the time limit. |
“I put temperature on the x-axis and melt-time on the y, and the line drops steeply and then levels off — hotter water melts the ice faster, but there’s less gain past about sixty degrees. One point sat way high; I think that trial started with a bigger cube, so I flagged it instead of deleting it. My conclusion: warmer water melts ice faster, up to a point.”
“The numbers go up and down. I made a graph but I’m not sure what kind it should be. I ran out of time before I figured out what it means.”
This assessment is AI-proof by design: it happens in the lab, with an unfamiliar dataset, against a real clock. No chatbot can organize numbers it was never handed, pick the graph, and defend a trend while the timer runs. The dataset differs from student to student, so there is no answer to look up — mastery is shown by reading and justifying in person, not by submitting.