Abstract:
Admissions into universities in Sri Lanka are determined through the cutoff marks published
annually in the University Grants Commission (UGC), although the procedure itself is not only
unclear but opaque to students as well as counselors. Since the procedure is unclear, students
end up making academic choices based on misleading information. This research proposes a
machine learning model for accurate determination of the cutoff marks in the UGC university
admissions. The proposed approach uses the UGC cutoff dataset (2020-2025), with features
like the year, the district quota, the stream, the university, the degree, the intake capacity, and
the cutoff marks. The final supervised regression approach using XGBoost with year-related
features and lag features for cutoff points is proposed. In this work, the error in the regression
method is calculated by the RMSE, MAE, and R² values. The regression analysis will be done
by the subgroup error analysis for districts and streams, and the regression results will be explained
by SHAP values. The RMSE, MAE, and R² score of the XGBoost algorithm come out
to be 0.2062, 0.1446, and 0.7484, respectively. Among various factors given importance by the
algorithm, Z score is given maximum importance, followed by subject stream, district quota,
university, intake capacity, and previous year cutoff trends. On checking the equity of the algorithm,
it is found that the disparity of errors is very low, which makes it a fair algorithm. This
study has shown the viability and accuracy of using machine learning algorithms to predict the
admission cutoffs of UGC admissions. This work fills the very significant research gap created
by the absence of publications focusing on the educational data-mining domain. This work can
form the basis of an effective and transparent decision support system that can help students,
counselors, and policymakers make educated decisions regarding educational planning and access
to higher education.
