Overview
Anomaly detection identifies irregular patterns or outliers within data sets. In online exams, it safeguards integrity by detecting cheating behaviors like unauthorized access or abnormal answer submission rates.
Significance in Online Exams
Crucial for detecting cheating attempts such as unauthorized access or abnormal answer submission rates.
Types of Anomalies
- Point Anomalies: Individual data points that are significantly different from the rest of the data.
- Contextual Anomalies: Data points that are only anomalous in specific contexts.
- Collective Anomalies: Groups of data points that are anomalous when considered together
Techniques for Anomaly Detection
- Statistical Analysis: Techniques such as z-scores and interquartile ranges.
- Machine Learning Algorithms: Methods like Isolation Forest, Local Outlier Factor (LOF), and clustering techniques.
- Heuristic Approaches: Rule-based methods that rely on domain knowledge.
Implementation in Online Exam Platforms:
- Behavior Monitoring: Tracking unusual patterns in student behavior.
- Session Management: Ensuring the integrity of the exam session.
- Identifying Suspicious Activities: Detecting irregularities such as unauthorized access or unexpected breaks.
Challenges and Considerations
- Balancing Accuracy with False Positives: Ensuring anomalies are detected without over-flagging normal behavior.
- Adapting to Evolving Cheating Methods: Continuously updating detection methods to address new cheating techniques.
- Ensuring Data Privacy: Maintaining the confidentiality and security of student data.
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