Machine Learning Literacy for Measurement Professionals: A Practical Tutorial

Autor/inn/en	Nie, Rui; Guo, Qi; Morin, Maxim
Titel	Machine Learning Literacy for Measurement Professionals: A Practical Tutorial
Quelle	In: Educational Measurement: Issues and Practice, 42 (2023) 1, S.9-23 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Nie, Rui) ORCID (Guo, Qi) ORCID (Morin, Maxim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/emip.12539
Schlagwörter	Artificial Intelligence; Electronic Learning; Literacy; Educational Assessment; Measurement Techniques; Programming Languages; Misconceptions + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Alphabetisierung; Schreib- und Lesefähigkeit; Education; assessment; Bewertungssystem; Messtechnik; Missverständnis
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. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01