IJIET 2025 Vol.15(11): 2335-2346
doi: 10.18178/ijiet.2025.15.11.2429
doi: 10.18178/ijiet.2025.15.11.2429
SuccessNet: An Automated Approach to Predict Student Academic Performance Using PCA Extracted CNN Novel Features and RF-SVM Ensemble Model
Khaled Alnowaiser1 and Muhammad Umer2,*
1. Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University,
Al-Kharj, P.O. Box 151, 11942, Saudi Arabia
2. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Email: k.alnowaiser@psau.edu.sa (K.A.); umer.sabir@iub.edu.pk (M.U.)
*Corresponding author
2. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Email: k.alnowaiser@psau.edu.sa (K.A.); umer.sabir@iub.edu.pk (M.U.)
*Corresponding author
Manuscript received March 15, 2025; revised March 31, 2025; accepted April 29, 2025; published November 10, 2025
Abstract—With the rapid growth of data in the education sector, traditional techniques have failed to predict student academic success effectively. This research work uses features extracted from Convolutional Neural Networks (CNN) with a Random Forest (RF) and Support Vector Machine (SVM) ensemble model to predict the academic performance of students. We called this novel framework SuccessNet. It obviates manual feature extraction and surpasses independent Deep Learning (DL) and Machine Learning (ML) models in performance. The experiments are carried out in two sets. First, the original features are used to apply nine ML algorithms. The second set of experiments contains features extracted by CNN. The SuccessNet is formed with a soft voting mechanism that combines the top models generated during the above two sets of experiments based on academic performance prediction for students using an ensemble of RF and SVM. A comparison of performance with existing models shows auspicious results. SuccessNet gives an accuracy of 99.35% with a precision, recall, and F-Score of 99%.
Keywords—computer and education, educational data mining, ensemble learning, machine learning
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—computer and education, educational data mining, ensemble learning, machine learning
Cite: Khaled Alnowaiser and Muhammad Umer, "SuccessNet: An Automated Approach to Predict Student Academic Performance Using PCA Extracted CNN Novel Features and RF-SVM Ensemble Model," International Journal of Information and Education Technology, vol. 15, no. 11, pp. 2335-2346, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).