Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] - Abdul Hadi, Az'lina; Muhammad, Nur Adibah; Mohamad Fadzil, Nurul Najihah; Walter Glispin, Oliver Steve; Mohd Razalil, Nornadiah; Azid@Maarof, Nur Niswah Naslina (Universiti Teknologi MARA, Kelantan, 2023)
The COVID-19 pandemic forced governments throughout the world to shutter educational institutions, implying the transition from traditional learning to online learning. Hence, the aim of this study was to determine the significant effect that contributed to students' satisfaction with online learning. A further goal of this study was to examine the significant difference in students' satisfaction with online learning according to their gender. To reach the objectives of the study, a cross-sectional study was carried out. Convenience sampling was employed in collecting data from 114 undergraduate students at selected universities in West Malaysia. An online questionnaire was adapted and disseminated to these selected students. The main analysis of multiple linear regression was performed to achieve the first goal of the study. From the multiple linear regression analysis, it was found that there were three significant factors that contributed to students' satisfaction with online learning during the COVID-19 pandemic: gender (p-value = 0.011), course management {p-value = 0.001), and online tutorial quality {p-value = 0.000). Apart from the analysis, an independent t-test was applied, and it was found that there was a significant difference in students' satisfaction between genders {p-value=0.015).