Construct and consequential validity for learning analytics based on trace data
Construct and consequential validity for learning analytics based on trace data
复制标题
基于跟踪数据的学习分析的构建和结果有效性
DOI：
10.1016/j.chb.2020.106457
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Philip H. Winne
中科院分区：
文献类型：
--
作者：
Philip H. Winne
本文分析了有效性的概念，提出了影响一般有效性主张的关键因素，特别是关于学习分析。由于跟踪数据在学习分析中的使用正在迅速增加，因此特别考虑跟踪数据的可靠性及其在声称基于跟踪数据的解释的有效性方面的作用。这种分析揭示了理论在决定应该收集哪些跟踪数据以及跟踪数据如何有助于改善学习的建议方面的重要和不可避免的作用，这是生成和使用学习分析的主要目标之一。
This article analyzes the concept of validity to set out key factors bearing on claims about validity in general and particularly regarding learning analytics. Because uses of trace data in learning analytics are increasing rapidly, specific consideration is given to reliability of trace data and their role in claiming validity for interpretations grounded on trace data. This analysis reveals the essential and inescapable role of theory in deciding what trace data should be gathered and how trace data can contribute to recommendations for improving learning, one main goal for generating and using learning analytics.