Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education

TL;DR

This paper introduces a hierarchical Bayesian knowledge tracing model that uses detailed student response data to identify learning patterns and challenges in undergraduate engineering education, supporting personalized teaching strategies.

Contribution

The study applies hierarchical Bayesian modeling to student response data, providing an interpretable and accurate method for tracking skill mastery and student abilities in engineering courses.

Findings

Identified consistent challenges in specific engineering concepts.

Uncovered distinct student learning subgroups.

Provided reliable metrics for personalized instruction.

Abstract

Educators teaching entry-level university engineering modules face the challenge of identifying which topics students find most difficult and how to support diverse student needs effectively. This study demonstrates a rigorous yet interpretable statistical approach -- hierarchical Bayesian modeling -- that leverages detailed student response data to quantify both skill difficulty and individual student abilities. Using a large-scale dataset from an undergraduate Statics course, we identified clear patterns of skill mastery and uncovered distinct student subgroups based on their learning trajectories. Our analysis reveals that certain concepts consistently present challenges, requiring targeted instructional support, while others are readily mastered and may benefit from enrichment activities. Importantly, the hierarchical Bayesian method provides educators with intuitive, reliable metrics without sacrificing predictive accuracy. This approach allows for data-informed decisions, enabling personalized teaching strategies to improve student engagement and success. By combining robust statistical methods with clear interpretability, this study equips educators with actionable insights to better support diverse learner populations.