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
In publication
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
Comments
No comment yet! Be the first to add one!