Predictive Student Performance Using Machine Learning

Amani Raad  Akram

Amani Raad Akram Arts ,Sciences& Technology in Lebanon(AUL)/Computer, Science & Communication, Iraq /Missan

Keywords: Prediction, Machine Learning, Random Forest, Data Analysis, Information Systems, Big Data

Abstract

This study provides advanced insight into the prediction of student performance by implementing multiple machine learning models including Decision Tree Regressor, Support Vector Regressor (SVR), Random Forest Regressor, and K-Nearest Neighbors Regressor. These models were assessed via key evaluation metrics such as “Mean Squared Error” (MSE), “Mean Absolute Error” (MAE), as well as the R² Score. Among them appears the Random Forest Regressor demonstrated superior predictive capability through achieving the highest R² score of 86.4% while maintaining the lowest MSE and MAE. This highlights its effectiveness in modeling student performance compared to individual models like Decision Trees and SVR. The final result suggests ensemble-based methods particularly random forest show better generalization. The future research must focus on the best hyperparameter tuning and integrating additional student-related features to enhance prediction accuracy.

References

S. Batool, J. Rashid, M. W. Nisar, J. Kim, H. Y. Kwon, and A. Hussain, "Educational data mining to predict students' academic performance: A survey study," Education and Information Technologies, vol. 28, no. 1, pp. 905-971, 2023.

N. Bošnjaković and I. Đurđević Babić, "Systematic review on educational data mining in educational gamification," Technology, Knowledge and Learning, pp. 1-18, 2023.

S. Hussain and M. Q. Khan, "Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning," Annals of Data Science, vol. 10, no. 3, pp. 637-655, 2023.

M. Segura, J. Mello, and A. Hernández, "Machine learning prediction of university student dropout: Does preference play a key role?," Mathematics, vol. 10, no. 18, p. 3359, 2022.

Y. Zhang, Y. Yun, R. An, J. Cui, H. Dai, and X. Shang, "Educational data mining techniques for student performance prediction: Method review and comparison analysis," Frontiers in Psychology, vol. 12, p. 698490, 2021.

R. Trakunphutthirak and V. C. Lee, "Application of educational data mining approach for student academic performance prediction using progressive temporal data," Journal of Educational Computing Research, vol. 60, no. 3, pp. 742-776, 2022.

J. Jovanović, M. Saqr, S. Joksimović, and D. Gašević, "Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success," Computers & Education, vol. 172, p. 104251, 2021.

A. Alam and A. Mohanty, "Predicting students’ performance employing educational data mining techniques, machine learning, and learning analytics," in International Conference on Communication, Networks and Computing, Cham: Springer Nature Switzerland, 2022, pp. 166-177.

N. Z. Salih and W. Khalaf, "Improving students performance prediction using machine learning and synthetic minority oversampling technique," Journal of Engineering and Sustainable Development, vol. 25, no. 6, pp. 56-64, 2021.

M. T. Sathe and A. C. Adamuthe, "Comparative study of supervised algorithms for prediction of students’ performance," International Journal of Modern Education and Computer Science, vol. 13, no. 1, p. 1, 2021.

C. F. Rodríguez-Hernández, M. Musso, E. Kyndt, and E. Cascallar, "Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation," Computers and Education: Artificial Intelligence, vol. 2, p. 100018, 2021.

S. J. H. Yang, O. H. T. Lu, A. Y. Q. Huang, J. C. H. Huang, H. Ogata, and A. J. Q. Lin, "Predicting students’ academic performance using multiple linear regression and principal component analysis," J. Inf. Process., vol. 26, pp. 170–176, 2018.

S. D. A. Bujang, A. Selamat, R. Ibrahim, O. Krejcar, E. Herrera-Viedma, H. Fujita, and N. A. M. Ghani, "Multiclass prediction model for student grade prediction using machine learning," IEEE Access, vol. 9, pp. 95608-95621, 2021.

G. Feng, M. Fan, and Y. Chen, "Analysis and prediction of students’ academic performance based on educational data mining," IEEE Access, vol. 10, pp. 19558-19571, 2022.

"Student performance," Kaggle, Available: https://www.kaggle.com/code/zabihullah18/student-performance.