Koushik Roy * , Huu-Hoa Nguyen and Dewan Md. Farid

* Corresponding author (rkoushikroy2@gmail.com)

Main Article Content

Abstract

This study addresses the crucial issue of predicting student performance in educational data mining (EDM) by proposing an Adaptive Dimensionality Reduction Algorithm (ADRA). ADRA efficiently reduces the dimensionality of student data, encompassing various academic, demographic, behavioral, social, and health-related features. It achieves this by iteratively selecting the most relevant features based on a combined normalized mean rank of five feature ranking methods. This reduction in dimensionality enhances the performance of predictive models and provides valuable insights into the key factors influencing student performance. The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. The results show that ADRA achieves an average dimensionality reduction factor of 6.2 while maintaing comprable accuracy with other mehtods.

Keywords: Backward elimination, dimensionality reduction, forward selection, recursive feature elimination, student performance prediction

Article Details

References

Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119-136.

Alhassan, A., Zafar, B., & Mueen, A. (2020). Predict students’ academic performance based on their assessment grades and online activity data. International Journal of Advanced Computer Science and Applications, 11(4).

Bilal, M., Omar, M., Anwar, W., Bokhari, R. H., & Choi, G. S. (2022). The role of demographic and academic features in a student performance prediction. Scientific Reports, 12(1), 12508.

Cortez, P. (2014). Student Performance. [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5TG7T.

Estrera, P. J. M., Natan, P. E., Rivera, B. G. T., & Colarte, F. B. (2017). Student Performance Analysis for Academic Ranking Using Decision Tree Approach in University of Science and Technology of Southern Philippines Senior High School Abstract. International Journal of Engineering and Technology, 3(5), 147-153.

Febro, J. D. (2019). Utilizing feature selection in identifying predicting factors of student retention. International Journal of Advanced Computer Science and Applications, 10(9).

Fida, S., Masood, N., Tariq, N., & Qayyum, F. (2022). A Novel Hybrid Ensemble Clustering Technique for Student Performance Prediction. JUCS: Journal of Universal Computer Science, 28(8).

Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Multi-split optimized bagging ensemble model selection for multi-class educational data mining. Applied Intelligence, 50, 4506-4528.

Mythili, M. S., & Shanavas, A. M. (2014). An Analysis of students’ performance using classification algorithms. IOSR Journal of Computer Engineering, 16(1), 63-69.

Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 1-23.

Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924.

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 10(3), e1355.

Sabri, M., Zahid, M., Abd Majid, N. A., Hanawi, S. A., Talib, N. I. M., & Yatim, A. I. A. (2023). Prediction model based on continuous data for student performance using principal component analysis and support vector machine. TEM Journal, 12(2).

Shetu, S. F., Saifuzzaman, M., Moon, N. N., Sultana, S., & Yousuf, R. (2021). Student’s performance prediction using data mining technique depending on overall academic status and environmental attributes. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020, Volume 2 (pp. 757-769). Springer Singapore.

VeeraManickam, M. R. M., Mohanapriya, M., Pandey, B. K., Akhade, S., Kale, S. A., Patil, R., & Vigneshwar, M. (2019). Map-reduce framework based cluster architecture for academic student’s performance prediction using cumulative dragonfly based neural network. Cluster Computing, 22(Suppl 1), 1259-1275.

Xue, H., & Niu, Y. (2023). Multi-Output Based Hybrid Integrated Models for Student Performance Prediction. Applied Sciences, 13(9), 5384.

Yağcı, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environment, 9(11). https://doi.org/10.1186/s40561-022-00192-z.

Yılmaz, N., & Sekeroglu, B. (2019, August). Student performance classification using artificial intelligence techniques. In International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (pp. 596-603). Cham: Springer International Publishing.

Zaffar, M., Hashmani, M. A., Savita, K. S., & Rizvi, S. S. H. (2018). A study of feature selection algorithms for predicting students academic performance. International Journal of Advanced Computer Science and Applications, 9(5).

Zhang, X., Liu, J., Zhang, C., Shao, D., & Cai, Z. (2023). Innovation Performance Prediction of University Student Teams Based on Bayesian Networks. Sustainability, 15(3), 2335.