Machine Learning Grade Prediction Using Students' Grades and Demographics

TL;DR

This study presents a machine learning framework that predicts student grades and pass/fail outcomes simultaneously, aiding early intervention and resource optimization in secondary education.

Contribution

It introduces a unified approach combining classification and regression tasks, outperforming baseline models with high accuracy and R^2 scores.

Findings

Classification accuracy up to 96%

Regression R^2 of 0.70

Effective early identification of at-risk students

Abstract

Student repetition in secondary education imposes significant resource burdens, particularly in resource-constrained contexts. Addressing this challenge, this study introduces a unified machine learning framework that simultaneously predicts pass/fail outcomes and continuous grades, a departure from prior research that treats classification and regression as separate tasks. Six models were evaluated: Logistic Regression, Decision Tree, and Random Forest for classification, and Linear Regression, Decision Tree Regressor, and Random Forest Regressor for regression, with hyperparameters optimized via exhaustive grid search. Using academic and demographic data from 4424 secondary school students, classification models achieved accuracies of up to 96%, while regression models attained a coefficient of determination of 0.70, surpassing baseline approaches. These results confirm the feasibility of early, data-driven identification of at-risk students and highlight the value of integrating dual-task prediction for more comprehensive insights. By enabling timely, personalized interventions, the framework offers a practical pathway to reducing grade repetition and optimizing resource allocation.