Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
This work addresses the challenge for educators in entry-level university engineering modules to make data-informed decisions for personalized teaching, though it is incremental as it applies an existing statistical method to a new educational context.
The study tackled the problem of identifying difficult topics and supporting diverse student needs in undergraduate engineering education by applying hierarchical Bayesian modeling to student response data, revealing clear skill mastery patterns and distinct student subgroups that inform targeted instructional support.
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.