The Promise of MOOCs Revisited? Demographics of Learners Preparing for University

M. Meaney, T. Fikes
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引用次数: 2

Abstract

This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a subset of data from 29,083 participants who submitted an assignment in one of nine entry-level MOOC courses. Manhattan distance and Gower distance measures are computed based on engagement, achievement, and demographic data. To our knowledge, this marks one of the first such uses of Gower distance to cluster mixed-variable data to explore fairness and equity in the MOOC literature. The clusters are derived from CLARA and PAM algorithms, enriched by demographic data, with a particular focus on education level, as well as approximated socioeconomic status (SES) for a smaller subset of learners. Results indicate that learners without a college degree are more likely to be high-performing compared to college-educated learners. Learners from lower SES backgrounds are just as likely to be successful as learners from middle and higher SES backgrounds. While MOOCs have struggled to improve access to learning, more fair and equitable outcomes for traditionally underrepresented learners are possible.
重新审视mooc的前景?为大学做准备的学习者的人口统计学
本文利用聚类分析来深入了解传统上代表性不足的学习者如何参与旨在降低大学入学门槛的入门级大规模开放在线课程(MOOCs),该课程由美国一所主要研究型大学制作。从260,239名学习者的初始样本中,我们聚类分析了29,083名参与者的数据子集,这些参与者提交了九门入门级MOOC课程之一的作业。曼哈顿距离和高尔距离是根据参与度、成就和人口统计数据来计算的。据我们所知,这标志着首次使用高尔距离来聚类混合变量数据,以探索MOOC文献中的公平性和公平性。这些聚类来自CLARA和PAM算法,通过人口统计数据进行丰富,特别关注教育水平,以及一小部分学习者的近似社会经济地位(SES)。结果表明,与受过大学教育的学习者相比,没有大学学位的学习者更有可能表现出色。来自较低社会经济地位背景的学习者与来自中等和较高社会经济地位背景的学习者一样有可能成功。虽然mooc一直在努力改善学习机会,但对于传统上代表性不足的学习者来说,更公平和公平的结果是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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