Student Perceptions and Achievements of Online Learning: Machine Learning Approaches

Abstract

The Covid-19 pandemic has been changing all aspects of human life. In education, the online learning method has been selected as one of the ways to prevent the spread of Covid-19. The classical learning method turns into online learning using information technology facilities. There are many challenges to implementing a class online. However, online learning could provide a new perspective for student learning. The study aims to analyze student perceptions of the online learning process. Their perception would be used as an independent variable to predict the student achievement index. The research data were obtained from a student questionnaire. Students provided an assessment through a questionnaire about the online learning methods they experienced during the Covid-19 pandemic. The machine learning algorithms, namely Random Forest and Support Vector Machine, were applied to examine the dataset. The study focused on the criteria (variable importance) that affect student perceptions of the online learning process. The results described that the student achievement in online learning is influenced by: 1) technology to access online learning, 2) student efforts, and 3) active and independent learning. The study contributes to improving the online learning method for the student.