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

Abstract

Abstract 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.