Student's Interest and Opinion Towards Online Education

Abstract

The paper presents a study of the interest and opinions of students regarding online learning using a machine learning approach, as well as the evolution of different e-learning platforms based on education following the pandemic period. The study uses student data from a questionnaire-based survey of college students to improve the learning environment from the perspective of the students. The survey and questionnaire were designed with students' needs, requirements, and preferred level of quality for online learning. Survey data was subjected to data analysis and classification to gain a deeper understanding of the learner in a virtual learning environment. Through data preparation, analysis, visualization, and machine learning algorithm accuracy, machine learning classification algorithms and analysis are used to examine the collected data. The research study will illustrate the expectations and enhancement of online learning education patterns according to the requirements of students. With a 93% accuracy evaluation, the Random Forest algorithm has the best accuracy among the several classification algorithms.