An Enhanced Predictive Approach for Students’ Performance

Applying data mining for improving the outcomes of the educational process has become one of the most significant areas of research. The most important corner stone in the educational process is students’ performance. Therefore, early prediction of students’ performance aims to assist at-risk students by providing appropriate and early support and intervention. The objective of this paper is to propose an enhanced predictive model for students’ performance prediction. Selecting the most impor-tant features is a crucial indicator for the academic institutions to make an appropriate intervention to help students with poor performance and the top influencing features were selected in feature selection step besides the dimensionality reduction and build an efficient predictive model. DB-Scan clustering technique is applied to enhance the proposed predictive model performance in the preprocessing step. Various classification techniques are used such as Decision Tree, Logistic regression, Naive Bayes, Random Forest, and Multilayer Perceptron. Moreover ensemble method is used to solve the trade-off between the bias and the variance and there are two proposed ensemble methods through the experiments to be compared. The proposed model is an ensemble classifier of Multilayer Perceptron, Decision Tree, and Random Forest classifiers. The proposed model achieves an accuracy of 83.16%.

@article{Yacoub2022,
  title     = {An Enhanced Predictive Approach for Students’ Performance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Farouk Yacoub and Huda Amin Maghawry and Nivin A Helal and Sebastian Ventura and Tarek F. Gharib},
  doi       = {10.14569/IJACSA.2022.01304101},
  url       = {https://doi.org/10.14569/IJACSA.2022.01304101}
}