Understand the Detection Process
Beacon uses supervised learning — an ML technique where models are trained on labeled datasets to make predictions about new outcomes. Beacon’s models are trained on datasets labeled by individuals with experience in recognizing and responding to mental health risks. When deployed by a school, Beacon’s ML models continuously analyze student online activity. If the models detect activity that matches the patterns they were trained to identify, Beacon generates an alert.Designated school staff are always the ultimate decision-makers as to whether a Beacon-generated alert requires human intervention.
Review Alert Contents
Each alert contains the following information to help the reviewing staff member make an informed, rapid decision about student safety:- Date and time of the event
- 30-minute browsing history window (±15 minutes from the event)
- Student name and email address
- Guardian contact information (if available)
- Risk category
- URL
- Relevant content
- Screen captures (if available)
Understand Alert Phases
Beacon categorizes each alert into one of five phases:Resources
About GoGuardian Beacon
Overview of what Beacon is and how schools use it.
Beacon Glossary
Definitions for key terms used throughout Beacon documentation.
Create a Deployment
Create a Beacon deployment and connect it to an Organizational Unit.
Initial Setup Checklist
Step-by-step checklist for Super Users setting up Beacon Starter.