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Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda

BACKGROUND: During the recent Coronavirus pandemic, many universities realized that the traditional delivery of educational content was not adequate in the context of imposed restrictions. Adoption of e-learning was one obvious way to foster continuity of learning. Despite its rapid implementation d...

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Autores principales: Komuhangi, Alimah, Mpirirwe, Hilda, Robert, Lubanga, Githinji, Florence Wamuyu, Nanyonga, Rose Clarke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463679/
https://www.ncbi.nlm.nih.gov/pubmed/36088322
http://dx.doi.org/10.1186/s12909-022-03735-7
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author Komuhangi, Alimah
Mpirirwe, Hilda
Robert, Lubanga
Githinji, Florence Wamuyu
Nanyonga, Rose Clarke
author_facet Komuhangi, Alimah
Mpirirwe, Hilda
Robert, Lubanga
Githinji, Florence Wamuyu
Nanyonga, Rose Clarke
author_sort Komuhangi, Alimah
collection PubMed
description BACKGROUND: During the recent Coronavirus pandemic, many universities realized that the traditional delivery of educational content was not adequate in the context of imposed restrictions. Adoption of e-learning was one obvious way to foster continuity of learning. Despite its rapid implementation 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 the adoption of e-learning using learner and information technology variables. METHODS: A cross-sectional study among 109 health professional students ≥18 years of age at Clarke International University was conducted. Adoption of e-learning was measured as a 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 the 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 e-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). CONCLUSION: We identified predictors of e-learning adoption which include having completed at least 1 year of study, high e-learning expectations, confidence in using IT devices, prior experience in e-learning, considering e-learning to be flexible and internet access. This information can be used by universities to enhance infrastructure and prepare potential e-learners. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-022-03735-7.
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spelling pubmed-94636792022-09-10 Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda Komuhangi, Alimah Mpirirwe, Hilda Robert, Lubanga Githinji, Florence Wamuyu Nanyonga, Rose Clarke BMC Med Educ Research BACKGROUND: During the recent Coronavirus pandemic, many universities realized that the traditional delivery of educational content was not adequate in the context of imposed restrictions. Adoption of e-learning was one obvious way to foster continuity of learning. Despite its rapid implementation 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 the adoption of e-learning using learner and information technology variables. METHODS: A cross-sectional study among 109 health professional students ≥18 years of age at Clarke International University was conducted. Adoption of e-learning was measured as a 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 the 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 e-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). CONCLUSION: We identified predictors of e-learning adoption which include having completed at least 1 year of study, high e-learning expectations, confidence in using IT devices, prior experience in e-learning, considering e-learning to be flexible and internet access. This information can be used by universities to enhance infrastructure and prepare potential e-learners. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-022-03735-7. BioMed Central 2022-09-10 /pmc/articles/PMC9463679/ /pubmed/36088322 http://dx.doi.org/10.1186/s12909-022-03735-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Komuhangi, Alimah
Mpirirwe, Hilda
Robert, Lubanga
Githinji, Florence Wamuyu
Nanyonga, Rose Clarke
Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title_full Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title_fullStr Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title_full_unstemmed Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title_short Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda
title_sort predictors for adoption of e-learning among health professional students during the covid-19 lockdown in a private university in uganda
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463679/
https://www.ncbi.nlm.nih.gov/pubmed/36088322
http://dx.doi.org/10.1186/s12909-022-03735-7
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