Large scale study of primary school student performance relative to their LMS activity and socioeconomic demographics using a Bayesian Additive Regression Trees containing random effects
Applications
2025-07-09 v1 Methodology
Abstract
Using data collected on almost every 9-12 years old student in Uruguay, we show how to apply Bayesian Additive Regression Trees (BART) with random effects to study performance association with Learning Managment System (LMS) activity and socioeconomic status. Performance data is joined with LMS activity pattern data. BART is chosen because it is possible to include school-level random effects. The model can be used for early identification of at-risk students, and highlights schools that are successful or need intervention. An interesting finding is that high levels of LMS usage show larger positive effects on performance in low socioeconomic status.
Keywords
Cite
@article{arxiv.2507.05262,
title = {Large scale study of primary school student performance relative to their LMS activity and socioeconomic demographics using a Bayesian Additive Regression Trees containing random effects},
author = {Natalia da Silva and Bruno Tancredi and Ignacio Alvarez-Castro},
journal= {arXiv preprint arXiv:2507.05262},
year = {2025}
}