Current - Issue
Original Article
An Explainable Ensemble Learning Framework for Academic Performance Prediction Using Educational Data mining
Dr. Sasikala P1
Dr. Nanditha Prasad2
1 Associate Professor, Department of Computer Science, Lal Bahadur Shastri Government First Grade College, Bengaluru, Karnataka, India. 2 Associate Professor, Nrupathunga University, Bengaluru, Karnataka, India.
Published Online: May-August 2026
Pages: 859-867
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502093References
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: Applications and Reviews, vol. 40, no. 6, pp. 601–618, Nov. 2010, doi: 10.1109/TSMCC.2010.2053532.
2. M. Yağcı, "Educational data mining: Prediction of students' academic performance using machine learning algorithms," Smart Learning
Environments, vol. 9, no. 1, 2022, doi: 10.1186/s40561-022-00192-z.
3. 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, pp. 905–971, 2023.
4. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
5. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.
6. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
7. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009.
8. I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Burlington, MA,
USA: Morgan Kaufmann, 2017.
9. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge University Press, 2008.
10. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2012.
11. F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
12. W. McKinney, "Data structures for statistical computing in Python," in Proc. 9th Python in Science Conference, 2010, pp. 56–61.
13. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and
Data Mining, 2016, pp. 785–794.
14. S. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems
(NeurIPS), vol. 30, 2017.
15. M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proc. ACM SIGKDD,
2016, pp. 1135–1144.
16. D. Dua and C. Graff, "UCI Machine Learning Repository," University of California, Irvine, School of Information and Computer Sciences,
2019.
17. T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
18. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. IJCAI, 1995, pp. 1137–1145.
19. G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2021.20. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge University Press, 2000.
21. L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, no. 1–2, pp. 1–39, 2010.
22. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann, 1993.
23. D. Wolpert, "Stacked generalization," Neural Networks, vol. 5, no. 2, pp. 241–259, 1992.
24. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and
System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
25. T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
26. G. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient over F1 score and accuracy in binary classification
evaluation," BMC Genomics, vol. 21, 2020.
27. J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of Machine Learning Research, vol. 13, pp. 281–
305, 2012.
28. T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967.
29. S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed. Birmingham, U.K.: Packt Publishing, 2019.
30. A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 3rd ed. Sebastopol, CA, USA: O'Reilly Media, 2022.
Part C: Applications and Reviews, vol. 40, no. 6, pp. 601–618, Nov. 2010, doi: 10.1109/TSMCC.2010.2053532.
2. M. Yağcı, "Educational data mining: Prediction of students' academic performance using machine learning algorithms," Smart Learning
Environments, vol. 9, no. 1, 2022, doi: 10.1186/s40561-022-00192-z.
3. 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, pp. 905–971, 2023.
4. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
5. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.
6. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
7. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009.
8. I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Burlington, MA,
USA: Morgan Kaufmann, 2017.
9. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge University Press, 2008.
10. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2012.
11. F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
12. W. McKinney, "Data structures for statistical computing in Python," in Proc. 9th Python in Science Conference, 2010, pp. 56–61.
13. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and
Data Mining, 2016, pp. 785–794.
14. S. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems
(NeurIPS), vol. 30, 2017.
15. M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proc. ACM SIGKDD,
2016, pp. 1135–1144.
16. D. Dua and C. Graff, "UCI Machine Learning Repository," University of California, Irvine, School of Information and Computer Sciences,
2019.
17. T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
18. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. IJCAI, 1995, pp. 1137–1145.
19. G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2021.20. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge University Press, 2000.
21. L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, no. 1–2, pp. 1–39, 2010.
22. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann, 1993.
23. D. Wolpert, "Stacked generalization," Neural Networks, vol. 5, no. 2, pp. 241–259, 1992.
24. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and
System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
25. T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
26. G. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient over F1 score and accuracy in binary classification
evaluation," BMC Genomics, vol. 21, 2020.
27. J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of Machine Learning Research, vol. 13, pp. 281–
305, 2012.
28. T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967.
29. S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed. Birmingham, U.K.: Packt Publishing, 2019.
30. A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 3rd ed. Sebastopol, CA, USA: O'Reilly Media, 2022.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
Unveiling Deepfake Detection Using Vision Transformers: A Survey and Experimental Study
Share Article
Or copy link
https://www.indjcst.com/archives/an-explainable-ensemble-learning-framework-for-academic-performance-prediction-using-educational-data-mining
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.