Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a Private University in Uganda
| dc.contributor.author | Komuhangi, Alimah | |
| dc.contributor.author | Mpirirwe, Hilda | |
| dc.contributor.author | Lubanga, Robert | |
| dc.contributor.author | Wamuyu Githinji, Florence | |
| dc.contributor.author | Nanyonga, Rose C. | |
| dc.date.accessioned | 2022-12-13T15:55:40Z | |
| dc.date.available | 2022-12-13T15:55:40Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Universities have come to terms with the fact that education, as we have traditionally delivered it, cannot be sustained under the current circumstances imposed on us by the Coronavirus diseases 2019 pandemic. Adoption of e-learning is one obvious way to foster continuity of learning. During the lockdown in Uganda, it was not known whether health professional students were willing to adopt e-learning as a way to foster continuity of learning. We therefore adopted a Technology Acceptance Model to determine the predictors for adoption of learning using learner and information technology variables. Methods. A cross-sectional study among 109 health professional student’s ≥18 years at Clarke International University was conducted. Adoption of e-learning was measured as self-report. Data were obtained using a smart survey and descriptively summarized. The differences in the study outcome were compared using the chi-square test. The factors that independently influenced adoption of e-learning were determined using binary logistic regression and reported as adjusted odds ratios (aORs) with a 95% confidence interval (CI). Results: Of the 109 respondents, 71 (65.1%) adopted e-learning. Our data showed low odds of adoption of e-learning among participants in first year (aOR, 0.34: 95%CI, 0.14-0.79), low learning expectations (aOR, 0.01: 95%CI, 0.01-0.34), no confidence in using IT devices (aOR, 0.16: 95%CI, 0.00-0.77), no prior experience in e-learning (aOR, 0.11: 95%CI, 0.02-0.68), not considering e-learning flexible (aOR, 0.25:95%CI, 0.08-0.86) and high cost of internet (aOR, 0.13: 95%CI, 0.02-0.84). | en_US |
| dc.identifier.citation | Komuhangi, A., Mpirirwe, H., Robert, L., Githinji, F. W., & Nanyonga, R. C. (2022). Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a Private University in Uganda. | en_US |
| dc.identifier.uri | https://doi.org/10.21203/rs.3.rs-1544508/v1 | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/6270 | |
| dc.language.iso | en | en_US |
| dc.subject | E-learning adoption | en_US |
| dc.subject | health professional students | en_US |
| dc.subject | Coronavirus diseases 2019 | en_US |
| dc.title | Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a Private University in Uganda | en_US |
| dc.type | Article | en_US |