There are many factors I’ve witnessed districts monitor, but some common ones seem to be: students below an ADA threshold, students above an infraction threshold, students failing certain classes, and also some form of demographic indicator (i.e. SPED, LEP, etc.).
A couple of districts are using this benchmark information as it was intended: to identify those students in most need of intervention, and to project students’ proficiency in specific content areas.
Specifically, students’ spring scores from the previous school year (Spring Term, SY 2016) are compared to students’ fall scores for the current year (Fall Term, SY 2017) to determine who regressed and who demonstrated growth.
Both of these examples (using an At-Risk report and using predictive analytics to determine likelihood of advancement or regression) use their own student data to identify correlated trends in student populations in order to attempt to act proactively rather than reactively.