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Meta-Analysis
. 2019 Dec;24(1):1666538.
doi: 10.1080/10872981.2019.1666538.

Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis

Leisi Pei  1 , Hongbin Wu  2
Affiliations
Meta-Analysis

Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis

Leisi Pei et al. Med Educ Online. 2019 Dec.

Abstract

With the increasing use of technology in education, online learning has become a common teaching method. How effective online learning is for undergraduate medical education remains unknown. This article's aim is to evaluate whether online learning when compared to offline learning can improve learning outcomes of undergraduate medical students. Five databases and four key journals of medical education were searched using 10 terms and their Boolean combinations during 2000-2017. The extracted articles on undergraduates' knowledge and skill outcomes were synthesized using a random effects model for the meta-analysis.16 out of 3,700 published articles were identified. The meta-analyses affirmed a statistically significant difference between online and offline learning for knowledge and skill outcomes based on post-test scores (SMD = 0.81; 95% CI: 0.43, 1.20; p < 0.0001; n = 15). The only comparison result based on retention test scores was also statistically significant (SMD = 4.64; 95% CI: 3.19, 6.09; p < 0.00001). The meta-analyses discovered no significant difference when using pre- and post-test score gains (SMD = 3.03; 95% CI: -0.13, 4.13; p = 0.07; n = 3). There is no evidence that offline learning works better. And compared to offline learning, online learning has advantages to enhance undergraduates' knowledge and skills, therefore, can be considered as a potential method in undergraduate medical teaching.

Keywords: Online learning; meta-analysis; offline learning; systematic review; undergraduate medical education (UME).

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Figures

Figure 1.
Figure 1.
Study inclusion flowchart.
Figure 2.
Figure 2.
Summary of the risk of bias.
Figure 3.
Figure 3.
Venn diagram of the 16 identified articles, clustered by the statistical methods used.
Figure 4.
Figure 4.
Meta-analysis of post-test performance.
Figure 5.
Figure 5.
Meta-analysis of post-test performance without the article of Subramanian et al.
Figure 6.
Figure 6.
Meta-analysis of pre- and posttest score gains.
Figure 7.
Figure 7.
Meta-analysis of retention test scores.
See this image and copyright information in PMC

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