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Construct and Consequential Validity for Learning Analytics Based on Trace Data

Item

Title

Construct and Consequential Validity for Learning Analytics Based on Trace Data

Abstract/Description

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.

Author/creator

Date

Volume

112

Pages

106457

Resource type

Research/Scholarly Media

Resource status/form

Published Text

Scholarship genre

Theoretical

Open access/full-text available

No

Peer reviewed

Yes

ISSN

0747-5632

Citation

Winne, P. H. (2020). Construct and Consequential Validity for Learning Analytics Based on Trace Data. Computers in Human Behavior, 112, 106457. https://doi.org/10.1016/j.chb.2020.106457

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