Pattern Detection Engine
Summary
The Pattern Detection Engine identifies unusual student checkout behavior—like skipping the same class repeatedly, leaving before tests, or checking out in sync with others. It flags trends like group skipping or repeated long hallway durations, helping staff spot issues early without constant monitoring.
Module Lead
Varun Bhadurgatte Nagaraj
Start Date
May 2025
Target Users
Administrators, Deans, Campus Security
Key Features
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Highlighted behavior patterns (e.g., repeat offenders)
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Heatmaps and time-based movement analytics
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Customizable flags for review
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Exportable incident summaries
The Need
In a busy school day, patterns of hall pass misuse often go undetected — students repeatedly skipping the same class, checking out just before tests, or coordinating with friends to roam in groups. These behaviors can be subtle, but over time, they disrupt learning, erode accountability, and contribute to broader safety concerns. Traditional systems offer no way to track or flag these patterns, leaving staff to rely on gut feeling or scattered reports.
The Pattern Detection Engine brings clarity to this chaos. By analyzing trends across time, location, and students, it flags suspicious behaviors like synchronized checkouts, pre-test absences, and excessive hallway time. With visual reports and early alerts, it empowers administrators to act sooner, identify problem trends, and make informed decisions — without micromanaging every pass.
