PA
Other
Fall
Middle
MS
Ms. Jennifer Okafor
MTSS Coordinator
Pacific View Middle School, West
Early Warning System: Catching At-Risk Students Before They Fall
Early Identification Rate
23%
84%
+265%
Days to First Intervention
34 days
5 days
-85%
False Positive Rate
N/A
12%
New
Symptom
Students were falling through the cracks. By the time a teacher referred a student for intervention, the student had already failed a class, been absent 15+ days, or accumulated multiple behavioral incidents. The school was reactive — always catching students after they'd already fallen behind.
The data to predict these outcomes existed in the system. Attendance patterns, behavioral events, and grade trends all showed warning signs weeks before a crisis. But no one was watching.
People
Ms. Jennifer Okafor, the MTSS Coordinator, designed the early warning system with the data team.
Team:
- Ms. Jennifer Okafor — MTSS Coordinator, system design and response coordination
- Data analyst — Trigger configuration, dashboard creation
- School counselors — Student follow-up, family contact
- Grade-level team leads — Weekly review participation
Data
The system combined three data streams into a single dashboard: attendance (3+ absences in 10 days), behavior (2+ events in 5 days), and academics (grade below 65 in any core class). Students who triggered 2 or more indicators were automatically flagged. The dashboard updated daily.
The team tracked how many students were identified by the system vs. by traditional teacher referral, and how quickly intervention started after identification.
Plan
The early warning system operated on a weekly cycle:
Monday: Dashboard review. The MTSS coordinator reviewed all newly flagged students and assigned each to a counselor.
Tuesday-Wednesday: Counselors made initial contact — a brief, non-threatening check-in with the student. "I noticed you've missed a few days. Everything okay?"
Thursday: Team huddle (15 minutes). Counselors reported back. Students needing intervention were matched with available supports (mentoring, tutoring, attendance contract, counseling).
Friday: Intervention started. The goal was same-week response — flag on Monday, support by Friday.
The system was tuned over the first month. Initial triggers were too sensitive (flagging too many students). The team adjusted thresholds based on false positive rates until the system identified the right students at the right time.
graph LR
A["Monday<br/>Dashboard<br/>Review"] --> B["Tue-Wed<br/>Student<br/>Check-In"]
B --> C["Thursday<br/>Team Huddle"]
C --> D["Friday<br/>Intervention<br/>Starts"]
D --> E["Following Week<br/>Monitor +<br/>Adjust"]
Resources
Early Warning Trigger Configuration Guide
Contact us for access
Student Check-In Protocol
Contact us for access