Student success prediction using a novel machine learning approach based on modified SVM

P. Umamaheswari; M. Vanitha; P. Vimala Devi; J. Glory Theporal; Badal Rihan Basha

doi:10.31893/multiscience.2024ss0103

Keywords:

Student result prediction

Abstract

Education holds an indispensable place in society. The 2020 coronavirus outbreak, which wrought havoc worldwide, imparted varying ramifications on the educational landscape. Numerous studies underscored a decline in student performance, thereby accentuating the urgency of addressing this concern proactively and discerning the contributory factors. As a cornerstone of societal progress, education is universally championed by governments and nations alike. Recognizing the vital need to monitor students to avert academic derailment, the capacity to predict student performance equips educators to vigilantly track outcomes and make informed decisions that bolster both learning and achievement. The model proposed in this study emerges as a superior classifier, offering enhanced accuracy while concurrently mitigating risks of overfitting and underfitting, courtesy of sophisticated machine learning algorithms. This investigation delineates the primary drivers influencing student success. It undertakes student data-based classification and juxtaposes various classifiers. The efficacy of the proposed methodology was corroborated using metrics like accuracy, recall, and the F1 score, registering commendable values of 84%, 95%, and 82% respectively, outpacing traditional models. This innovative approach promises to be instrumental in forecasting students' scholastic trajectories, thereby empowering stakeholders to execute timely interventions.
References
1. Agrawal, H., Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(03), 111-113.
2. Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 1-21.
3. Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498-506.
4. Gardner, J., Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127-203.
5. Gareth J., Daniela, W., Trevor, H., Robert, T. (2013). An introduction to statistical learning: with applications in R. Springer.
6. Hoffait, A. S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1-11.
7. Kuzilek, J., Zdrahal, Z., & Fuglik, V. (2021). Student success prediction using student exam behaviour. Future Generation Computer Systems, 125, 661-671.
8. Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15.
9. Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-112.
10. Mayilvaganan, M., & Kalpanadevi, D. (2014). Comparison of classification techniques for predicting the performance of student's academic environment. In 2014 International Conference on Communication and Network Technologies IEEE, 113-118.
11. Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51.
12. Putnik, G., Costa, E., Alves, C., Castro, H., Varela, L., & Shah, V. (2016). Analyzing the correlation between social network analysis measures and performance of students in social network-based engineering education. International Journal of Technology and Design Education, 26(3), 413-437.
13. Serrano, C., Murgui, S., & Andreu, Y. (2022). Improving the prediction and understanding of academic success: The role of personality facets and academic engagement. Revista de Psicodidáctica (English ed.), 27(1), 21-28.
14. Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.
15. Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., & Abreu, R. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance. International Educational Data Mining Society.
16. Yagci, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 1-19.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Umamaheswari, P., Vanitha, M., Vimala Devi, P., Glory Theporal, J., & Basha, B. R. (2023). Student success prediction using a novel machine learning approach based on modified SVM. Multidisciplinary Science Journal, 6, 2024ss0110. https://doi.org/10.31893/multiscience.2024ss0103

[1] Agrawal, H., Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(03), 111-113.

[2] Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 1-21.

[3] Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498-506.

[4] Gardner, J., Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127-203.

[5] Gareth J., Daniela, W., Trevor, H., Robert, T. (2013). An introduction to statistical learning: with applications in R. Springer.

[6] Hoffait, A. S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1-11.

[7] Kuzilek, J., Zdrahal, Z., & Fuglik, V. (2021). Student success prediction using student exam behaviour. Future Generation Computer Systems, 125, 661-671.

[8] Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15.

[9] Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-112.

[10] Mayilvaganan, M., & Kalpanadevi, D. (2014). Comparison of classification techniques for predicting the performance of student's academic environment. In 2014 International Conference on Communication and Network Technologies IEEE, 113-118.

[11] Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51.

[12] Putnik, G., Costa, E., Alves, C., Castro, H., Varela, L., & Shah, V. (2016). Analyzing the correlation between social network analysis measures and performance of students in social network-based engineering education. International Journal of Technology and Design Education, 26(3), 413-437.

[13] Serrano, C., Murgui, S., & Andreu, Y. (2022). Improving the prediction and understanding of academic success: The role of personality facets and academic engagement. Revista de Psicodidáctica (English ed.), 27(1), 21-28.

[14] Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.

[15] Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., & Abreu, R. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance. International Educational Data Mining Society.

[16] Yagci, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 1-19.

Abstract

References

How to cite