Support without surveillance: a privacy-first approach to student analytics
A privacy-first approach to student analytics: understand a cohort without surveilling individuals, using aggregation and small-cell suppression.
Student analytics has an uncomfortable default. To tell you something useful, most tools hand you a list of named students and a risk score, and ask you to watch them. It feels invasive because it is, and for a stretched team it is rarely even actionable.
There is a better default. The question worth answering is not which individuals to monitor, but where to direct limited support: which cohort, which campus, which week. That question can be answered with aggregate data, and aggregate data does not require surveilling anyone.
Two practices make this safe. The first is aggregation: report at the level of a group, not a person. The second is small-cell suppression: when a group is small enough that a number could identify someone, withhold the number. Together they let you see where help is needed while keeping individuals anonymous.
The payoff is not only ethical. A tool students trust is a tool students are honest with, and honest signals are the ones worth acting on.