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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
Winne, Philip H.
Date
2020
In publication
Computers in Human Behavior
Volume
112
Pages
106457
Resource type
en Research/Scholarly Media
Resource status/form
en Published Text
Scholarship genre
en Theoretical
Keywords
learning analytics
reliability
self-regulated learning
theory
trace data
validity
Language
en
Open access/full-text available
en No
Peer reviewed
en Yes
ISSN
0747-5632
DOI
10.1016/j.chb.2020.106457
URL
Official Publisher's Webpage (ScienceDirect)
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
Abbreviation
Computers in Human Behavior
Item sets
Handbook Chapter 20 Citations

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