Replicating 21 findings on student success in online learning
Replicating 21 findings on student success in online learning
复制标题
重复关于学生在线学习成功的 21 项研究结果
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Catherine A. Spann
中科院分区：
文献类型：
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作者：
J. M. Andres;R. Baker;George Siemens;D. Gašević;Catherine A. Spann
过去几年，有大量研究致力于研究哪些因素导致学生在在线课程（无论是学分课程还是开放课程）中取得成功。然而，正式研究跨课程重复的发现的工作相对有限。在本文中，我们提出了一种促进此类研究复制的架构，该架构可以从 edX 大规模开放在线课程 (MOOC) 中获取数据，并测试一系列研究结果是否适用（无论是原始形式还是使用自动搜索过程稍加修改）。我们从之前发表的关于 MOOC 完成的研究中确定了 21 个发现，将它们呈现到我们架构中的生产规则中，并在单个 MOOC 的情况下测试它们，使用事后方法来控制多重比较。我们发现先前发布的结果中有九个在当前数据集中成功复制，并且在两个案例中发现了矛盾的结果。这项工作代表了大规模自动复制相关研究成果的一步。
There has been a considerable amount of research over the last few years devoted towards studying what factors lead to student success in online courses, whether for-credit or open. However, there has been relatively limited work towards formally studying which findings replicate across courses. In this paper, we present an architecture to facilitate replication of this type of research, which can ingest data from an edX Massively Open Online Course (MOOC) and test whether a range of findings apply, in their original form or slightly modified using an automated search process. We identify 21 findings from previously published studies on completion in MOOCs, render them into production rules within our architecture, and test them in the case of a single MOOC, using a post-hoc method to control for multiple comparisons. We find that nine of these previously published results replicate successfully in the current data set and that contradictory results are found in two cases. This work represents a step towards automated replication of correlational research findings at large scale.