当前位置: X-MOL 学术Comput. Hum. Behav. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Construct and consequential validity for learning analytics based on trace data
Computers in Human Behavior ( IF 8.957 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.chb.2020.106457
Philip H. Winne

Abstract 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.

中文翻译:

基于跟踪数据的学习分析的构建和结果有效性

摘要 本文分析了有效性的概念,以列出影响有效性声明的关键因素,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,因此特别考虑了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。这一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进学习的建议方面的基本和不可避免的作用,这是生成和使用学习分析的一个主要目标。
更新日期:2020-11-01
down
wechat
bug