A Deep Learning Dive into Online Learning: Predicting Student Success with Interaction-Based Neural Networks
Keywords:
online learning, student success prediction, neural network, interaction features, educational data miningAbstract
As online learning continues to gain prominence due to its accessibility and flexibility, the need to enhance student success and retention in virtual classrooms has become paramount. This research paper presents a comprehensive study on predicting student performance in an online learning context. Leveraging a rich dataset sourced from the Open University, we investigate the effectiveness of utilizing various interaction features to build a neural network model for predicting learning outcomes of online learners. By examining interactions with learning resources, forums, quizzes, and collaborative tools, this study could achieve a remarkable 75% accuracy in prediction of learning outcomes. The study not only highlights the potential of leveraging diverse features but also sheds light on the intricacies of online learning dynamics and the factors that influence student success. By synthesizing the theoretical insights with practical applications, educators and stakeholders can design equitable online contents that can possibly address the various learning preferences of learners with diverse learning styles and needs thereby enhancing the learning experience of at-risk learners.
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