Anomaly Detection in Educational Assessment Data

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Anomaly detection in educational assessment data involves identifying unusual or abnormal patterns or behaviors in student assessment data that deviate from expected patterns. This is important for ensuring the validity and reliability of assessment results, as well as for identifying potential issues such as cheating or data errors.

Anomaly detection can be accomplished through various methods, such as statistical analysis, machine learning, or a combination of both. For example, a simple statistical approach might involve setting thresholds for acceptable ranges of assessment scores and flagging any scores that fall outside of these ranges as anomalies. More sophisticated methods might involve using machine learning algorithms to identify patterns and relationships in the data that can be used to detect anomalies.

One specific example of using machine learning for anomaly detection in educational assessment data is the use of autoencoder-based neural networks, as described in the research paper "Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks" published in Bioengineering in 2023. The authors propose a deep learning model that can detect anomalies in sensor signals.

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