Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.]
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 le...
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| Format: | Article |
| Language: | en |
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Universiti Teknologi MARA, Kelantan
2023
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| Online Access: | https://ir.uitm.edu.my/id/eprint/89049/1/89049.pdf https://ir.uitm.edu.my/id/eprint/89049/ https://journal.uitm.edu.my/ojs/index.php/JMCS |
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| author | Abdul Hadi, Az'lina Muhammad, Nur Adibah Mohamad Fadzil, Nurul Najihah Walter Glispin, Oliver Steve Mohd Razalil, Nornadiah Azid@Maarof, Nur Niswah Naslina |
| author_facet | Abdul Hadi, Az'lina Muhammad, Nur Adibah Mohamad Fadzil, Nurul Najihah Walter Glispin, Oliver Steve Mohd Razalil, Nornadiah Azid@Maarof, Nur Niswah Naslina |
| author_sort | Abdul Hadi, Az'lina |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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). |
| format | Article |
| id | my.uitm.ir-89049 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2023 |
| publisher | Universiti Teknologi MARA, Kelantan |
| record_format | eprints |
| spelling | my.uitm.ir-890492024-01-28T16:06:38Z https://ir.uitm.edu.my/id/eprint/89049/ Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] jmcs Abdul Hadi, Az'lina Muhammad, Nur Adibah Mohamad Fadzil, Nurul Najihah Walter Glispin, Oliver Steve Mohd Razalil, Nornadiah Azid@Maarof, Nur Niswah Naslina Blended learning. Computer assisted instruction. Programmed instruction Learning. Learning strategies Regression analysis. Correlation analysis. Spatial analysis (Statistics) 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). Universiti Teknologi MARA, Kelantan 2023-06 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/89049/1/89049.pdf Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.]. (2023) Journal of Mathematics and Computing Science <https://ir.uitm.edu.my/view/publication/Journal_of_Mathematics_and_Computing_Science/>, 9 (1): 8. pp. 14-21. ISSN 0128-0767 https://journal.uitm.edu.my/ojs/index.php/JMCS |
| spellingShingle | Blended learning. Computer assisted instruction. Programmed instruction Learning. Learning strategies Regression analysis. Correlation analysis. Spatial analysis (Statistics) Abdul Hadi, Az'lina Muhammad, Nur Adibah Mohamad Fadzil, Nurul Najihah Walter Glispin, Oliver Steve Mohd Razalil, Nornadiah Azid@Maarof, Nur Niswah Naslina Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title | Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title_full | Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title_fullStr | Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title_full_unstemmed | Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title_short | Regression analysis on predicting students' satisfaction with online learning during COVID-19 / Az'lina Abdul Hadi ... [et al.] |
| title_sort | regression analysis on predicting students' satisfaction with online learning during covid-19 / az'lina abdul hadi ... [et al.] |
| topic | Blended learning. Computer assisted instruction. Programmed instruction Learning. Learning strategies Regression analysis. Correlation analysis. Spatial analysis (Statistics) |
| url | https://ir.uitm.edu.my/id/eprint/89049/1/89049.pdf https://ir.uitm.edu.my/id/eprint/89049/ https://journal.uitm.edu.my/ojs/index.php/JMCS |
| url_provider | http://ir.uitm.edu.my/ |