Analyzing the Effectiveness of Design Elements Within Statistical Teaching Apps Through Bayesian Network AnalysisPechulis, Nathan Bayesian networksbehavioral data analysiseducational technologyuser engagementBayesian inferencelatent class modelsStatisticsB SThe Book Of Apps for Statistics Teaching is a resource created for undergraduate students to enhance their statistics knowledge through free, interactive apps created via the Shiny package on R. All interactions of app users since 2020, from simply opening the app to completing an activity, has been collected into a dataset of log-files. This data provides a rich foundation for analyzing patterns in student learning and engagement with e-learning applications. This study explores how these log-files can be transformed into actionable insights about student e-learning via Bayesian networks and the rjags package on R. By constructing a probabilistic framework like this, we can capture relationships between observed variables and unobserved, underlying engagement levels, thus improving our ability to understand how students interact with certain educational tools. The analysis ultimately revealed that Interactive Challenges and Right/Wrong-style feedback show a positive association with user performance/engagement under this framework.Neil J Hatfield, Thesis SupervisorMatthew D Beckman, Thesis Honors Advisoropen_access2026-04-08T00:02:27Zhttps://honors.libraries.psu.edu/catalog/10064njp5739