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Spreadsheets are apprentices. Teach them one clean move and they’ll repeat it perfectly a thousand times. The temptation is to improvise down the column… little tweaks in row 7, a “fix” in row 23. That’s how you lose the plot. Instead, make one formula that tells the truth, copy it through, and let the machine […]

Sigma notation looks intimidating until you read it as a story: “Add these, from here to there.” The index is the who. The bounds are the when. The expression is the what. Change any one and you change the story. Summation isn’t “add everything”… it’s “add exactly what matters.” That means choosing the right unit

Formulas are little plays. Parentheses are the stage directions. Without them, the actors guess… and the plot wanders. Most spreadsheet errors aren’t dramatic; they’re quiet: a total a hair too high, an average off by one cell, a minus sign treated like decoration. Order of operations is ruthless about what gets calculated first, so you

1) Labels Nudge Behavior  Call a student “behind” and you’ll triage. Call them “not yet” and you’ll coach. The same reality; different category; different action. Choose labels that produce the behavior you want… from you and from them. 2) Count Before You Calibrate  If you’re overwhelmed, start with a count: How many submissions? How many absences? Counting

Most measurement stalls because the variable is too grand. “Engagement,” “success,” “well-being.” Shrink it. Pick the smallest indicator that (a) you can collect quickly and (b) would change what you do tomorrow. One click. One minute. One nudge. When a variable is small enough to act on, it earns its keep. And once a tiny

“Participation” can mean eye contact, chat posts, or peer feedback. Until you say which, you haven’t measured; you’ve wished. An operational definition is a promise to your future self: “When I see X, I’ll count it as Y.” That promise reduces arguments and increases learning. Before you reach for a rubric or a dashboard, sharpen

We love talking about “the population”… all students, all courses, all outcomes. But you never hold a population in your hand. You hold a sample: this section, this semester, these ten advising notes. That’s not a bug; it’s the vehicle. A decent, honest sample moves you forward faster than a hypothetical population ever will. Treat

Some problems want bins. Others want measuring rulers. Calling students “on-time/late” highlights a threshold; logging “minutes late” reveals a gradient. Both are true… just different truths. Categories are for commitments (“Do we intervene?”). Quantities are for improvement (“By how much?”). If you’re stuck, ask: What conversation do I want to change: policy or practice? Then

We don’t live in a clean lab. We live in the wild… half-filled forms, distracted students, glitchy software, good intentions. Statistics is not the hero of certainty; it’s the translator that turns mess into movement. When you ask a clear question and choose a measure that actually reflects it, you’re not “doing math,” you’re building