Unsupervised Anomaly Detection of Learning Behaviors Considering Contextual Information

Le résumé fourni par la source

With the widespread adoption of digital learning environments, learning support utilizing log data collected from Learning Management Systems (LMS) has garnered significant attention. However, many conventional learning analytics rely on aggregated metrics such as total study time or the number of accesses, thereby failing to capture complex temporal dynamics, such as the sequential context of learning behaviors. In this study, we propose an unsupervised anomaly detection method that extracts atypical behaviors that deviate from standard learning patterns using students' behavior stream data. Specifically, we use a Transformer Encoder to generate contextualized embedding vectors from the time-series sequences of learning behaviors, and apply an Isolation Forest to detect anomalous behaviors without incurring labeling costs. Experimental results using actual university learning log data confirmed that the majority of the detected anomalous behaviors were immediate exits (i.e., merely opening and closing materials) lacking the context of page transitions. While these anomalous behaviors were predominantly observed among low-performing students (F and D grades), approximately 30\% of the logs classified as anomalous belonged to high-performing students (A grades). Atypical behaviors extracted through unsupervised anomaly detection suggest the inclusion of not only indications of learning deficiencies, such as immediate exits, but also advanced learning strategies adopted by high-performing students, such as the concurrent study of multiple materials and the marking of crucial sections.

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