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Machine Learning Literacy for Measurement Professionals: A Practical Tutorial
Educational Measurement: Issues and Practice ; 2023.
Article in English | Scopus | ID: covidwho-2243769
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. © 2023 by the National Council on Measurement in Education.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Educational Measurement: Issues and Practice Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Educational Measurement: Issues and Practice Year: 2023 Document Type: Article