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Machine Learning Literacy for Measurement Professionals: A Practical Tutorial.

Authors :
Nie, Rui
Guo, Qi
Morin, Maxim
Source :
Educational Measurement: Issues & Practice. Mar2023, Vol. 42 Issue 1, p9-23. 15p. 9 Charts.
Publication Year :
2023

Abstract

The COVID‐19 pandemic has accelerated the digitalization of assessment, creating new challenges for measurement professionals, including big data management, test security, and analyzing new validity evidence. In response to these challenges, Machine Learning (ML) emerges as an increasingly important skill in the toolbox of measurement professionals in this new era. However, most ML tutorials are technical and conceptual‐focused. Therefore, this tutorial aims to provide a practical introduction to ML in the context of educational measurement. We also supplement our tutorial with several examples of supervised and unsupervised ML techniques applied to marking a short‐answer question. Python codes are available on GitHub. In the end, common misconceptions about ML are discussed. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07311745
Volume :
42
Issue :
1
Database :
Academic Search Index
Journal :
Educational Measurement: Issues & Practice
Publication Type :
Academic Journal
Accession number :
162672153
Full Text :
https://doi.org/10.1111/emip.12539