Machine Learning Literacy for Measurement Professionals: A Practical Tutorial

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Rui Nie, Qi Guo, Maxim Morin
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引用次数: 1

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.

测量专业人员的机器学习素养:实践教程
2019冠状病毒病大流行加速了评估的数字化,给测量专业人员带来了新的挑战,包括大数据管理、测试安全性和分析新的有效性证据。为了应对这些挑战,机器学习(ML)在这个新时代成为测量专业人员工具箱中越来越重要的技能。然而,大多数ML教程都是以技术和概念为中心的。因此,本教程的目的是在教育测量的背景下提供ML的实用介绍。我们还用几个例子来补充我们的教程,这些例子是应用于标记简短回答问题的有监督和无监督ML技术。Python代码可在GitHub上获得。最后,讨论了关于机器学习的常见误解。©2023国家教育计量委员会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.90
自引率
15.00%
发文量
47
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