Closer Than They Think: Why I Am Optimistic About This Week's Check-In Results
What this week's low-stress exam taught me about how students reason with data.
We just finished a check-in on hypothesis tests and tests to determine whether two categorical variables were related. All the questions were framed in a context-rich setting. Students were placed in a short interview situation and had to do some quick calculations to help a hiring manager make a decision.
(Quick note: I call these short, low-stakes exams "check-ins." They're designed to lower stress while giving students space to think and reflect in a real-world setting)
The results were encouraging. Most students were on the right track, which is a big step forward.
But things got a little fuzzy when they had to explain why they made their conclusion.
They were able to calculate standard error consistently for hypothesis tests. Some forgot t-values when calculating the margin of error for confidence intervals. A few used the confidence interval approach but made minor calculation errors. Some skipped the heart of the decision and did not explain why the size of the test statistic or the p-value helped them draw their conclusions about the strength of the relationship.
Still, I left feeling hopeful. Students are getting the structure, and they're doing the work. What they need now is more practice explaining what their results mean.
So here's what I'm trying next as we move to comparisons of two group means, ANOVA, and correlation:
Ask students to explain each step: What test? What result? What does it tell you?
Use side-by-side examples to show what a strong explanation sounds like.
Add short prompts that ask students to explain results in plain English.
This check-in helped me see where they are—and where I can help them go next.