A Meta-Analysis of Online Learning Outcomes and Their Gender Differences

Zhonggen Yu, Liheng Yu
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引用次数: 3

Abstract

In this COVID-19 pandemic, learners across the world are encouraged to stay at home receiving online education, which has nearly caused various learning outcomes. It is thus necessary to analyze online learning outcomes, as well as their gender differences to provide constructive references for learners, teachers, and technology developers. This study obtained reliable data from various databases using searching techniques. The study also selected the research articles based on inclusion and exclusion criteria. After analyzing the forest and funnel plots drawn via Review Manager 5.3, the study arrived at the conclusion that online learning outcomes were significantly higher than the traditional learning outcomes with a large effect size (d = 1.24), while the gender differences in learning outcomes were not significant with a small effect size (d = -0.03). In the future, experts could make every effort to develop advanced emotion detection applications via interdisciplinary cooperation to improve online learning outcomes.
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网络学习成果及其性别差异的元分析
在新冠肺炎大流行期间,世界各地的学习者都被鼓励呆在家里接受在线教育,这几乎造成了各种各样的学习成果。因此,有必要分析在线学习成果及其性别差异,为学习者、教师和技术开发人员提供建设性的参考。本研究使用搜索技术从各种数据库中获得可靠的数据。本研究还根据纳入和排除标准选择了研究文章。在对Review Manager 5.3绘制的森林图和漏斗图进行分析后,研究得出在线学习结果显著高于传统学习结果,效应量大(d = 1.24),而学习结果的性别差异不显著,效应量小(d = -0.03)。未来,专家们可以通过跨学科合作,尽一切努力开发先进的情感检测应用,以改善在线学习效果。
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