Why Theory Matters More than Ever in the Age of Big Data
Why Theory Matters More than Ever in the Age of Big Data
复制标题
为什么理论在大数据时代比以往任何时候都更加重要
DOI：
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发表时间：
2015
影响因子：
3.9
通讯作者：
D. Shaffer
中科院分区：
文献类型：
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作者：
A. Wise;D. Shaffer
对于进行学习研究来说，这是一个令人振奋的重要时刻，拥有史无前例的大量数据。然而，认为有了足够的数据，数字就能说明问题的想法是危险的。事实上，随着数据量的增加，理论在分析中扮演着越来越重要的角色。在学习分析和学习理论这一专题部分的介绍中，我们描述了在不涉及理论的情况下，大规模数据分析中出现的一些关键问题。这些问题从研究人员应该注意的许多可能变量中的哪一个，到如何解释大量微观结果并使其具有可操作性的问题。在我们的评论结束时，我们讨论了特别部分中包括的经验文件集和应邀就这些文件发表的评论如何应对这些挑战，并在这样做时代表着朝着了解理论和为理论作出贡献的学习分析工作迈出的重要一步。我们的最终目标是在该领域引发一场批判性对话，讨论学习分析工作如何借鉴和贡献理论。
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These range from the question of to which of the many possible variables a researcher should attend to how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section and the commentaries that were invited on them speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics work draws on and contributes to theory.