Track: Artificial Intelligence

Abstract

Learning is the secret to success for career-oriented people who want to grow in both their personal and professional life and may pursue it for the rest of their lives. Sadly, the bulk of us are having trouble fitting new hobbies into our demanding and strict schedules. This research is mainly based on finding the factors that motivate or demotivate the students or the teachers for using online learning management systems such as Zoom, Moodle, Microsoft Teams etc. For the purpose of forecasting the influencing elements of online learning management systems, this study assessed and contrasted several supervised machine learning algorithms such as Gaussian Naive Bayes, Multinomial Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Decision Tree Classifier and Random Forest Classifier. All these algorithms are included in the proposed model. With a focus on how students and teachers perceive online learning management systems, the survey includes information gathered from 102 students and 20 teachers at an engineering university in Bangladesh (LMS). A set of significant factors to the development of online learning systems are revealed by an analysis of the data that was collected. The findings recommend enhancing positive experiences and reducing demotivating elements in the online learning environment.