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Construct and consequential validity for learning analytics based on trace data
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DOI：10.1016/j.chb.2020.106457.png)
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Abstract
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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.
Keywords:
Validity
Reliability
Learning analytics
Trace data
Self-regulated learning
Theory
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