ABSTRACT

To examine the similarities and differences between two closely related yet distinct fields – Educational Data Mining (EDM) and Learning Analytics (LA) – this study conducted a literature review of the empirical studies published in both fields. We synthesized 492 LA and 194 EDM articles published during 2015–2019. We compared the similarities and differences in research across the two fields by examining data analysis tools, common keywords, theories, and definitions listed. We found that most studies in both fields did not clearly identify a theoretical framework. For both fields, theories of self-regulated learning are most frequently used. We found, through keyword analysis, that both fields are closely related to each other as “learning analytics” is most frequently listed keyword for EDM and vice versa for LA. However, one notable difference relates to how LA studies listed social-related keywords whereas EDM studies listed keywords related to technical methods. The tools used for data analysis overlap largely but some of the LA studies listed tools for qualitative data analysis and social network analysis whereas EDM studies did not. Finally, the distinction of the two fields is defined differently by authors as some demarcate the differences whereas some address them interchangeably.

摘要

为了检验教育数据挖掘（EDM）和学习分析（LA）这两个密切相关但又截然不同的领域之间的异同，本研究对这两个领域发表的实证研究进行了文献综述。我们综合了 2015 年至 2019 年期间发表的 492 篇 LA 文章和 194 篇 EDM 文章。我们通过检查数据分析工具、常见关键词、理论和列出的定义来比较两个领域研究的异同。我们发现这两个领域的大多数研究都没有明确确定理论框架。对于这两个领域，自我调节学习理论是最常用的。通过关键词分析，我们发现这两个领域彼此密切相关，因为“学习分析”是 EDM 最常列出的关键词，反之亦然。然而，一个显着的区别在于 LA 研究如何列出与社交相关的关键词，而 EDM 研究如何列出与技术方法相关的关键词。用于数据分析的工具大部分重叠，但一些 LA 研究列出了定性数据分析和社交网络分析的工具，而 EDM 研究则没有。最后，作者对这两个领域的区别有不同的定义，有些作者划定了差异，而有些作者则可以互换。