Online Course Learning System using Non-Linear Gravity Coefficient with Mayfly Optimization Approach based on Web Technology

An online learning system generates a platform for remote education based on web technology which makes the user to evaluate lectures, courses, and resources through the Internet. It provisions interactive features such as forums, quizzes, and real-time feedback that improves user engagement. However, the present online course learning system in web technology struggled with personalized adaptive learning and content delivery which results in minimized engagement and learning outcomes. This research proposes Non-Linear Gravity Coefficient based Mayfly Optimization Approach (NLGC-MOA) for online course learning system based on web technology. In traditional MOA, the NLGC is incorporated which effectively balance among exploitation and exploration as well as enhance convergence speed in online course learning system. In pre-processing stage, the one-hot encoding transforms categorical form into numerical format that allow to interpret non-numeric data effectively. At last, the Long Short-Term Memory (LSTM) is used for determining online course learning performance based on web technology. The proposed NLGC-MOA achieves a better standard deviation of 0.948 for online learning performance compared to online learning engagement, online learning experience, and the quality of course content respectively.