Temporal and Causal Modeling of Learner Behavior in Online Platforms via Hybrid Bayesian-Transformer Networks

Grace Cho; Henry Wilson

doi:10.54097/ye49vm12

Authors

Grace Cho
Henry Wilson

DOI:

https://doi.org/10.54097/ye49vm12

Keywords:

Learner Behavior Modeling, Temporal Dependencies, Causal Inference, Transformer Networks, Bayesian Networks, Online Learning Platforms, Educational Data Mining, Sequence Analysis

Abstract

Online learning platforms generate vast amounts of learner interaction data that contain rich temporal patterns and causal relationships essential for understanding learning processes and optimizing educational outcomes. Traditional behavioral modeling approaches struggle to simultaneously capture the complex temporal dependencies in learner trajectories while identifying causal factors that drive learning success or failure. The challenge lies in developing frameworks that can model both the sequential nature of learning interactions and the underlying causal mechanisms that influence learner behavior across diverse online educational environments. This study proposes a novel Hybrid Bayesian-Transformer Network (HBTN) framework that integrates probabilistic causal modeling with transformer-based temporal sequence analysis to comprehensively model learner behavior in online platforms. The framework employs transformer architectures to capture long-range temporal dependencies in learner interaction sequences while utilizing Bayesian networks to model causal relationships between behavioral factors, learning outcomes, and contextual variables. The hybrid approach enables simultaneous discovery of temporal learning patterns and causal behavioral mechanisms through joint optimization of sequence modeling and causal structure learning objectives. Experimental evaluation using large-scale online learning platform datasets demonstrates that the proposed framework achieves 45% improvement in learner behavior prediction accuracy compared to traditional approaches. The HBTN method results in 38% better identification of at-risk learners and 42% improvement in personalized intervention recommendation effectiveness. The framework successfully combines temporal sequence modeling with causal reasoning to provide 34% better interpretability of learner behavioral patterns while maintaining computational efficiency suitable for real-time online platform applications.

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