Intelligent recommendation of content for enhancing user experience in E-learning systems

Abstract

The rise of E-learning systems has created a requirement to monitor and evaluate stu- dent 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, its em- phasizes best practices for formatting E-learning management system content such as using headings, shorter paragraphs, images for illustration, and maintaining a consis- tent style throughout. 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 train- ing and testing of the model, it was given around 73.99% accuracy in training and 63.16% accuracy in testing. After retraining the model, it was given an 85.58% accuracy in training and 78.90% accuracy in testing. Finally, the content will be arranged using the SCORM standard. Then the machine learning model is used to deal with intelligent E-learning systems, which enables the system to identify and analyze student behaviors in order to recommend content accordingly.