Student Performance Prediction Using Machine Learning

Abstract

Student academic performance is an important factor in evaluating the effectiveness of the learning process and identifying students who may require additional academic support. Traditional methods of evaluating student performance mainly depend on examination marks and teacher observations, which may not provide sufficient information for early identification of students at academic risk. This paper presents a machine learning-based approach for predicting student performance using relevant academic and personal attributes. The proposed system involves data preprocessing, feature selection, model training, and performance evaluation. Machine learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Support Vector Machine can be applied to identify patterns in student data and predict their expected academic performance. The performance of the models can be compared using suitable evaluation metrics such as accuracy, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), depending on the prediction task. The proposed approach can assist educational institutions in identifying students who may need additional guidance and support. The system demonstrates the potential of machine learning in supporting data-driven academic decision-making.

Introduction

The text presents a machine learning-based system for predicting student academic performance. The main idea is to use existing student information—such as previous grades, attendance, study time, assignment performance, participation, and assessment results—to predict academic outcomes before final examinations. This can help teachers identify students who may struggle and provide early support.

Problem

Schools often recognize poor academic performance only after examinations are completed. This delays intervention. The research therefore asks how machine learning can predict student performance using academic and behavioral factors.

Objectives

The study aims to:

• Collect and preprocess student academic data.
• Identify factors that strongly influence student performance.
• Apply different machine learning algorithms.
• Compare the performance of the models.
• Select the most suitable prediction model.
• Identify students who may need academic assistance at an early stage.

Proposed System

The system follows five main stages:

Data Collection → Data Preprocessing → Feature Selection → Model Training → Performance Evaluation

The data may include previous examination marks, attendance, study time, assignments, internal assessments, participation, and previous academic performance. The data is cleaned by handling missing values, removing duplicates, encoding categorical information, managing outliers, scaling features when necessary, and dividing the data into training and testing sets.

Machine Learning Algorithms

The proposed study compares several algorithms:

• Linear Regression – predicts numerical academic scores.
• Decision Tree – uses decision rules based on student features.
• Random Forest – combines multiple decision trees and can capture more complex relationships.
• Support Vector Machine (SVM) – classifies students into different performance categories.
• K-Nearest Neighbors (KNN) – predicts performance based on similar students in the dataset.

Evaluation

For classification models, the study can use accuracy, precision, recall, F1-score, and confusion matrix. For numerical score prediction, it can use MAE, MSE, RMSE, and R² score.

However, the text does not yet provide actual experimental results. The comparison table contains dashes instead of values, so the best-performing algorithm cannot yet be determined.

Implementation

The system can be developed in Python, using tools such as Pandas and NumPy for data processing and Scikit-learn for machine learning and evaluation. The implementation involves cleaning the dataset, selecting features, training multiple models, generating predictions, calculating evaluation metrics, and choosing the best model.

Conclusion

This paper presents a machine learning-based approach for predicting student academic performance. The proposed framework involves data collection, preprocessing, feature selection, model training, and evaluation. Different machine learning algorithms can be compared to identify an effective model for the prediction task. Such a system can assist educational institutions in identifying students who may require additional academic support and can contribute to data-driven decision-making. With improved datasets and responsible use of student information, machine learning can provide useful support for educational performance analysis.

References

[1] C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 40, no. 6, pp. 601–618, 2010. [2] R. S. Baker and K. Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions,” Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009. [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2009. [4] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. [5] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

Copyright

Copyright © 2026 Shalini S., Sahana R., Poorvika R., Aaliya Ashme A. W., Rubali R.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.