Abstract:
Bayesian additive regression tree (BART) is a recent statistical method
that blends ensemble learning with nonparametric regression. BART
is constructed using a Bayesian approach, which provides the benefit
of model-based prediction uncertainty, enhancing the reliability of predictions.
This study proposes the development of a BART model with
a binomial likelihood to predict the percentage of students retained in
tutorial classes using attendance data. The proposed model is evaluated
and benchmarked against the Random Forest Regressor (RFR). The
proposed BART model reported an average of 20% higher predictive
performance compared to RFR across five error metrics, achieving an Rsquared
score of 0.9414. Furthermore, the study demonstrates the utility
of the Highest Density Interval provided by the BART model, which can
help in determining the best and worst-case scenarios for student retention
rate estimates. The significance of this study extends to multiple
stakeholders within the educational sector. Educational institutions, administrators,
and policymakers can benefit from this study by gaining
insights into how future tutorship programme student retention rates
can be predicted using predictive models. Moreover, the foresight provided
by the predicted student retention rates can aid in strategic resource
allocation, facilitating more informed planning and budgeting
for tutorship programmes.
