Anomaly Detection

LMS Proctor monitors for suspicious behavior during quizzes and can automatically capture evidence, warn students, or terminate the quiz.

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Types of Anomalies

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Behavioral Anomalies

These are detected through browser events and interactions.

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Environmental Anomalies

These are detected before or during the quiz.

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Face Detection

LMS Proctor uses AI-powered face detection to verify student presence.

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How It Works

1. The webcam feed is analyzed every 500 milliseconds
2. A lightweight AI model (TinyFaceDetector) identifies faces
3. Face count and position are recorded
4. Anomalies trigger if:
   • No face is visible for longer than the configured threshold
   • Multiple faces are detected for longer than the threshold

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Configuration

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Developer Tools Detection

Opening developer tools could allow students to inspect quiz answers or manipulate the page. LMS Proctor uses multiple methods to detect this:

1. Window Size Detection: DevTools often changes the window dimensions
2. Console Property Getter: Detects when the console is accessed
3. Visual Inspection: Checks for DevTools-related UI elements

When detected, this is treated as a Critical severity anomaly.

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Anomaly Responses

You can configure how LMS Proctor responds to detected anomalies.

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Silent Capture

• Records the anomaly and captures evidence
• Student is not notified
• Quiz continues normally
• Instructor reviews after submission

Best for: Low-stakes quizzes or when you want to gather data without interruption.

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Warning + Capture

• Records the anomaly and captures evidence
• Student sees a warning message
• Student can dismiss and continue
• After max warnings, escalates to termination

Best for: Most quizzes - provides feedback while allowing recovery from accidental triggers.

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Terminate Quiz

• Immediately ends the quiz
• Records termination as a Critical anomaly
• Student cannot continue
• Submission is marked as terminated

Best for: High-stakes exams where any violation should end the attempt.

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Warning System

When using Warning + Capture mode:

1. First anomaly triggers a warning dialog
2. Student must acknowledge and return to the quiz
3. Warning count increments
4. After reaching Max Warnings, the quiz terminates

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Anomaly Evidence

When an anomaly is detected:

1. A snapshot is immediately captured (regardless of interval)
2. The snapshot is marked as anomaly evidence
3. Additional context is recorded:
   • Anomaly type and code
   • Severity level
   • Timestamp
   • Browser and environment info

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Reviewing Anomalies

Instructors can review anomalies in the submission view:

• Anomaly Summary: Count and types of anomalies
• Timeline View: When each anomaly occurred
• Evidence Snapshots: Screenshots from anomaly moments
• Anomaly Score: Calculated severity score

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Anomaly Score Calculation

The anomaly score weighs violations by severity: Higher scores indicate more suspicious activity and should be prioritized for review.

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Best Practices

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Limitations

• Face detection requires adequate lighting and camera quality
• Browser-based detection can be bypassed by sophisticated attempts
• Some legitimate activities may trigger false positives
• Detection depends on browser API support

For maximum security, combine anomaly detection with Safe Exam Browser.