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The role of demographics in online learning; A decision tree based approach

机译：人口统计学在在线学习中的作用;基于决策树的方法

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摘要

Research has shown online learners' performance to have a strong association with their demographic characteristics, such as regional belonging, socio-economic standing, education level, age, gender, and disability status. Despite a growing number of studies exploring factors for successful online learning outcomes, most researchers have utilised one or a combination of very few learner characteristics. Moreover, a limited number of studies scrutinised the impact of individual characteristics on learning outcomes as learners progress in a course. The current research aims to explore the dynamic impact of demographic characteristics on academic outcomes in the online learning environment. We investigated and compared the dynamic influence of six demographic characteristics on online learning outcomes using a sample of 8581 UK based learners across four Open University online courses from four different disciplines. We found region, neighborhood poverty level, and prior education respectively, to be strong predictors of overall learning outcomes. However, at a fine-grain level, such influence varied temporally as the course progressed, as well as between different courses. To conclude with, we discussed the implications for institutional support on adopting a tailored approach towards a more personalised student support system.
机译：研究表明，在线学习者的表现与他们的人口特征有着强大的联系，如区域归属，社会经济地位，教育水平，年龄，性别和残疾地位。尽管越来越多的研究探索成功在线学习成果的因素，但大多数研究人员都使用了一个或多个学习者特征的组合。此外，有限数量的研究审查了个体特征对学习成果的影响，因为学习者在课程中的进步。目前的研究旨在探讨人口统计特征对在线学习环境中的学术结果的动态影响。我们调查并比较了在四个不同学科的四个公开大学在线课程中使用8581英国学习者的样本对在线学习结果上的六个人口统计特征的动态影响。我们分别发现地区，邻里贫困水平和先前的教育，是整体学习成果的强烈预测因素。然而，在细粒度的水平下，随着课程的进展情况以及不同的课程之间，这种影响在时间上变化。要结束，我们讨论了对制度支持对采用更加个性化的学生支持系统的量身定制方法的影响。

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