Predicting the Influence of Internet Use on Student Academic Performance using Generative Adversarial Networks

Internet technology is essential to IT in the information-driven world. The Internet has made the world smaller and more accessible by improving communication, research, learning, and information management. Students benefit from Internet connectivity's large resources and realtime information. This strategy improved online learning through data preparation, feature selection, and model training. E-learning platform data on learners' behaviors was cleansed during preparation. The SFS stop condition was adjusted to improve feature selection. The model was trained via a GAN. The proposed model outperformed the latest RNN and SVM methods. An average accuracy of 92.58% was achieved. GANs and better feature selection could transform e-learning platforms. This proposed emphasizes the use of Internet technology and novel ways in increasing student learning.