Here's a pattern that plays out in districts across the country. In October, a group of 3rd graders at one elementary school starts missing more days than usual. Not dramatically — attendance drops from 94% to 89% over three weeks. No single absence is alarming. No parent calls to report a crisis. The building principal has 400 other students and a dozen daily fires to manage. The attendance clerk logs the absences and moves on.
In January, the quarterly attendance report surfaces a chronic absenteeism problem at that school. The district attendance coordinator flags it. The principal is surprised — they knew a few students were struggling, but didn't realize the scope. By February, the students who needed intervention four months ago have missed 30 or more days. Catching up gets harder with every week.
This isn't a failure of caring. It's a failure of timing. The data was there in October. It was sitting in the SIS, recorded accurately, available to anyone who pulled the right report. But no one pulled the right report, because no one knew they needed to — and even if they had, the pattern was spread across dozens of individual records that don't look alarming in isolation.
What the Decision Engine Does
Arcline's Decision Engine is built around a straightforward idea: instead of waiting for someone to ask the right question at the right time, the system should surface the patterns that matter — automatically, continuously, and to the right person.
Think of it as a layer that watches for the things a skilled data analyst would catch, if that analyst had the time to review every data point in the district every single day. Attendance trends by grade and building. Enrollment shifts that affect funding projections. Assessment results that diverge from historical patterns. Staffing gaps that have persisted long enough to warrant a different approach.
The Decision Engine monitors connected data sources nightly, identifies anomalies and trends, and routes alerts to the people whose job it is to act on them.
What It Catches
Here are real examples of the kinds of alerts the Decision Engine generates:
Attendance
"Jefferson Elementary's 3rd grade attendance has dropped 8% over the last 3 weeks. This is the steepest decline of any grade/school combination in the district."
This alert goes to the building principal and the district attendance coordinator. It includes the specific students driving the trend (visible only to staff with appropriate access), the comparison to building and district averages, and a link to the attendance detail for that group. The principal doesn't have to pull a report to find this. It arrives before the pattern becomes a crisis.
Funding
"Based on current FRL enrollment trends, your Title I allocation for next year may be $120K lower than projected. Consider reviewing eligibility counts before the October snapshot."
This alert goes to the director of federal programs and the CFO. Title I funding is calculated from free and reduced lunch eligibility, and the snapshot date is fixed. A district that doesn't realize its FRL counts have shifted until after the snapshot has no recourse. The Decision Engine flags the trend months ahead, when there's still time to conduct outreach and ensure eligible families have submitted applications.
Staffing
"Three schools have had special education vacancies open for 45+ days. Districts with similar profiles have used shared staffing models — here's what that looks like."
This alert goes to the HR director and the director of special education. It doesn't just flag the problem — it provides context from anonymized peer district data about alternative approaches that have worked in similar situations. Not a prescription, but a starting point for a conversation that might otherwise not happen until the vacancy has been open for six months.
Assessment
"Winter MAP results show 9th grade math proficiency at Washington High dropped 6 points from fall. This is an outlier compared to district trends."
This alert goes to the building principal and the chief academic officer. It includes the comparison to other grade levels at the same school, to the same grade at other schools, and to the historical trend for that grade/school combination. A 6-point drop in one semester isn't always cause for alarm — but when it's an outlier against every relevant comparison group, it warrants a closer look before spring.
How It Works
The Decision Engine runs pattern detection across all connected data sources on a nightly cycle. It compares current metrics against three baselines:
- Historical data. How does this metric compare to the same period in prior years? Is the trend consistent, or is something diverging?
- Peer benchmarks. How does this metric compare to anonymized data from similar districts? "Similar" accounts for enrollment size, demographic composition, urbanicity, and state context. No district names are shared — ever.
- Configurable thresholds. Districts can set their own alert criteria. One district might want to be notified when any building's chronic absenteeism rate exceeds 15%. Another might set that threshold at 10%. The Decision Engine respects those preferences.
Alerts are ranked by urgency based on the severity of the anomaly, the rate of change, and the potential impact. A sudden 8% attendance drop at one school ranks higher than a gradual 2% shift district-wide — not because 2% doesn't matter, but because the sudden change is more likely to be addressable with immediate intervention.
Each alert is routed based on the recipient's role. A building principal sees alerts for their school. A district attendance coordinator sees attendance alerts for every building. A CFO sees finance-related alerts. No one is buried in notifications that aren't relevant to their work.
What It's Not
The Decision Engine does not make decisions for administrators. It doesn't reassign staff, change budget allocations, or enroll students in intervention programs. Those are human decisions that require human judgment, local context, and the kind of relationship knowledge that no system can replicate.
What it does is change the sequence. Instead of discover-then-investigate-then-act, the workflow becomes investigate-then-act — because the discovery already happened, automatically, the night the pattern emerged.
Administrators can dismiss alerts they've reviewed, mark them for follow-up, or escalate them to a colleague. Every alert includes a "Why am I seeing this?" explanation that describes the specific data pattern that triggered it, in plain language, with links to the underlying data for anyone who wants to verify or explore further.
Closing the Timing Gap
The districts we work with don't lack information. They have more data than any prior generation of school administrators has ever had access to. What they lack is time — time to review it all, time to spot the patterns, time to connect a trend in one system to an implication in another.
The gap between knowing and doing in K-12 isn't an information gap. It's a timing gap. The data that would have changed a decision in October is sitting in a system that no one queries until January. The funding trend that should have triggered outreach in September gets discovered in November, after the snapshot date has passed.
The Decision Engine closes that gap. Not by replacing the people who make decisions, but by making sure they see the right patterns at the right time — before a trend becomes a crisis, before a deadline passes, before the students who needed help four months ago are four months further behind.
Because the data was always there. The question was always whether someone would see it in time.