Comparative Study : Predict Dropout Student In MOOC By Deep Learning

Abstract

In the rapidly evolving realm of e-learning, ad-vanced technologies play a crucial role in creating captivating educational experiences. This research explores the world of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, revealing their remarkable potential in predicting student performance with astonishing precision within e-learning environments. This research delves into the application of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for predicting student performance in e-learning settings. By harnessing the temporal patterns in sequential data, these models showcase their proficiency in early identification of struggling students and facilitating personalized learning journeys to enhance educational outcomes. The study presents experimental results on real-world e-learning data sets, showcasing the efficacy of these models in predicting student progress. Moreover, the research explores the interpret ability aspect of these models to address concerns related to trans-parency in AI-driven educational systems. By integrating these advanced machine learning techniques into e-learning platforms, the research underscores their potential to optimize educational resources and cater to individual learner needs, thereby revolutionizing the landscape of digital education.