Discovering Student Behavior Patterns from Event Logs: Preliminary Results on A Novel Probabilistic Latent Variable Model

TL;DR

This paper introduces the Hidden Behavior Traits Model (HBTM), a probabilistic latent variable model that analyzes detailed student event logs to uncover behavior patterns, aiding online learner modeling and educational planning.

Contribution

The study proposes a novel HBTM that models learner activities, timing, and interaction levels simultaneously, with evaluation on clustering and pattern interpretation.

Findings

HBTM effectively clusters learners based on behavior patterns.

The model provides interpretable latent behavior traits.

Results show promising potential for online learner analysis.

Abstract

Digital platforms enable the observation of learning behaviors through fine-grained log traces, offering more detailed clues for analysis. In addition to previous descriptive and predictive log analysis, this study aims to simultaneously model learner activities, event time spans, and interaction levels using the proposed Hidden Behavior Traits Model (HBTM). We evaluated model performance and explored their capability of clustering learners on a public dataset, and tried to interpret the machine recognized latent behavior patterns. Quantitative and qualitative results demonstrated the promising value of HBTM. Results of this study can contribute to the literature of online learner modeling and learning service planning.