Profiling Students Engagement in Full Online Learning

Abstract

The emergence of online learning platforms has transformed the landscape of education, offering flexibility and accessibility to learners worldwide. There are extensive datasets encompassing diverse aspects of online learning, including demographic information, course enrollment, participation metrics, and academic performance. Through EDA, we uncover patterns, trends, and anomalies within the data, shedding light on the characteristics of online learners. Machine learning approaches are employed to develop predictive models for various facets of online learning. Furthermore, this study explores the potential of personalized learning recommendations and interventions based on the analysis of student behavior and engagement. we use computers to learn about online students and help them do better. This helps make online learning more enjoyable and useful for everyone.