Machine Learning Grade Prediction Using Students' Grades and Demographics
arXiv:2603.00608v1 Announce Type: new 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 st
arXiv:2603.00608v1 Announce Type: new 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.
Executive Summary
This article presents a unified machine learning framework that predicts both pass/fail outcomes and continuous grades in secondary education. The framework, which utilizes academic and demographic data from 4424 students, demonstrates high accuracy and coefficient of determination. The results indicate the feasibility of early identification of at-risk students and highlight the value of integrating dual-task prediction. The framework offers a practical pathway to reducing grade repetition and optimizing resource allocation. The study's findings have significant implications for education policymakers and practitioners, particularly in resource-constrained contexts. The framework's potential to enable timely, personalized interventions underscores its importance in improving student outcomes and reducing educational inequalities.
Key Points
- ▸ The framework integrates classification and regression tasks, departing from prior research that treated them as separate tasks.
- ▸ The study achieves high accuracy and coefficient of determination using machine learning models and academic/demographic data.
- ▸ The framework enables early identification of at-risk students and offers a practical pathway to reducing grade repetition.
Merits
Strength in Methodology
The study employs a rigorous methodology, including the use of exhaustive grid search for hyperparameter optimization and the evaluation of six machine learning models.
Robustness in Results
The study achieves high accuracy and coefficient of determination across multiple models, indicating the robustness of the framework's results.
Demerits
Limited Generalizability
The study's results may not be generalizable to other educational contexts or populations due to the dataset's specificity to secondary education in a particular region.
Dependence on Data Quality
The framework's accuracy and effectiveness are dependent on the quality and availability of academic and demographic data, which may not be consistently available in all educational settings.
Expert Commentary
The study's methodology and results demonstrate the potential of machine learning to improve student outcomes in secondary education. However, the framework's limitations, including its dependence on data quality and limited generalizability, underscore the need for further research and development. The study's implications for policymakers and educators highlight the importance of investing in data-driven approaches to education and implementing targeted interventions to support at-risk students.
Recommendations
- ✓ Future research should aim to replicate the study's findings in diverse educational contexts and populations to improve the framework's generalizability.
- ✓ The development of more robust and scalable machine learning models is necessary to ensure the framework's effectiveness in resource-constrained contexts.