Analyzing the difficulty level in learning mathematics online using a quantile regression approach

Abstract

Online learning is a difficult type of educational process due to its limitations, particularly in college mathematics courses. The essential aim of this article is to elucidate the students' difficulty level in learning mathematics amid online learning and predict its statistically significant influencing factors. The data used in this study is secondary from a paper in the mathematics education literature on the form of cross-sectional. The data were summarized employing some descriptive measures and regression modeling as an inference. The result of the study showed that, on average, students are facing "difficulty" in learning their mathematics lessons during distance learning due to some problems. The quantile regression revealed that younger and female students are experiencing higher levels of difficulty. Plus, students who spent more money on the internet are facing higher difficulty in learning. In addition, a not conducive learning environment and social distractions are predictors of difficulty in learning. Conclusively, the difficulty in learning mathematics which adversely affects their performance is due to the distractions and problems in the learning environment, low coping mechanisms, and unprecedented educational process in the form of online setup. Hence, the study suggested that teachers must be flexible with students and provide digital simulations of mathematical problems, and use interactive models in their classes.