An E-learning system model to enhance user experience with content recommendation

Abstract

The rise of E-learning platforms has created a requirement to monitor and evaluate student performance while delivering interactive content, ultimately improving student learning. This research project focuses on studying the use of various data mining algorithms to extract user interactions from E-learning systems and identify patterns for recommending personalized content. The study will explore manipulating content through translations and formatting across different media to maintain high student interest. Additionally, it highlights the benefits of personalized learning, increased satisfaction, and early intervention in extracting student behavior. Moreover, it emphasizes best practices for formatting E-learning management system content, such as using headings, shorter paragraphs, images for illustration, and consistent style. Ultimately, this research concludes a model that helps to create an intelligent E-learning system that leverages data mining algorithms and machine learning techniques to generate personalized content recommendations based on user performance ratings to improve engagement and learning outcomes. In the initial testing of the model, it was given around 63.16% accuracy. After retraining the model, it was given a 78.90% accuracy in testing. Finally, the content will be arranged using the SCORM standard.