Academic Support: Using Data to Support the Right Students

Prevent costly remedial programs by identifying students who need help early

Providing academic support is essential—but when resources are limited, how do you know who needs help most and when to intervene? Traditional methods like teacher referrals and test scores only tell part of the story, often too late.

With AI-powered data analysis, schools can proactively identify students who are at risk academically, allowing you to deliver support when it's most effective—and far less expensive.

🧩 The Problem: Missed Opportunities for Early Support

Students rarely fail overnight. Their academic struggles usually build up gradually—across subjects, weeks, and behaviors. But without a clear system to track this across multiple classes and data points, it’s easy for early warning signs to go unnoticed.

The result? Students who could have succeeded with timely tutoring or support services end up needing summer school, credit recovery, or more intensive (and expensive) interventions later on.

🤖 The Solution: AI-Driven Early Identification

AI systems like schoolops.ai can analyze data from multiple sources—assignments, quiz scores, class participation, attendance, and even engagement in digital platforms—to identify patterns of academic decline across subjects and student groups.

Instead of waiting for failing grades, the system flags students who exhibit signs of academic risk, such as:

• Declining performance in two or more subjects

• Multiple missing assignments across classes

• Decreased engagement in learning platforms and on SEL indicators

• Correlation between attendance dips and grade drops

These patterns help educators deliver support where it’s needed before the issues become critical.

🧑‍🏫 Example: Grade Level Intervention at Edison Junior High

Scenario: At Edison Junior High, administrators began using an AI-powered dashboard to scan academic performance and attendance trends weekly. Midway through the semester, the system flagged a group of 14 seventh-grade students who were quietly falling behind.

While none had failed a class yet, their data told a different story:

• 12 had missing or late work in both ELA and science

• 10 showed a noticeable decline in quiz scores over four weeks

• Nearly all had a spike in absences or tardies since the start of the grading period

• More than half reported a low sense of belonging on the most recent student survey

The academic support team created a targeted intervention plan: small-group tutoring twice a week, check-ins with the school counselor, and weekly progress monitoring. Teachers also adjusted homework flexibility, conducted informal check-ins, and provided scaffolded support during class time.

By the end of the quarter, 11 of the 14 students had returned to passing grades in all subjects. More importantly, they regained academic confidence and required no placement in after-school remediation or summer learning programs.

📊 Benefits at a Glance

• Proactive support = students stay on grade level without remediation

• Smarter resource allocation = time and staffing go where they’re needed most

• Improved student confidence = support arrives before failure undermines motivation

• School-wide efficiency = fewer last-minute scrambles, better long-term planning

With the right data tools, principals and assistant principals can transform academic support from reactive to strategic—helping groups of students succeed before they fall behind.