Construct and consequential validity for learning analytics based on trace data
作者：
Highlights：
• Trace data are increasingly useful in developing learning analytics.
• “Raw” data are biased by the theory that recommends observing those data.
• Self-regulating learners acting as agents complicate reliability of trace data.
• Reliability of trace data concerns dynamic events, not static aspects of a measure.
• Generalizability over facets of data sets limits on reliability and validity.
摘要
•Trace data are increasingly useful in developing learning analytics.•“Raw” data are biased by the theory that recommends observing those data.•Self-regulating learners acting as agents complicate reliability of trace data.•Reliability of trace data concerns dynamic events, not static aspects of a measure.•Generalizability over facets of data sets limits on reliability and validity.
论文关键词：Validity,Reliability,Learning analytics,Trace data,Self-regulated learning,Theory
论文评审过程：Received 19 January 2020, Revised 3 June 2020, Accepted 13 June 2020, Available online 23 June 2020, Version of Record 26 June 2020.
论文官网地址：https://doi.org/10.1016/j.chb.2020.106457
