{"title":"MxML(探索测量与机器学习之间的关系):领域现状","authors":"Yi Zheng, Steven Nydick, Sijia Huang, Susu Zhang","doi":"10.1111/emip.12593","DOIUrl":null,"url":null,"abstract":"<p>The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as <i>measurement</i>). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years’ literature. We provide a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles (e.g., applications, conceptual, etc.), (3) ML methods discussed, and (4) potential risks associated with involving ML in measurement, which result from the differences between what measurement tasks need versus what ML techniques can provide.</p>","PeriodicalId":47345,"journal":{"name":"Educational Measurement-Issues and Practice","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MxML (Exploring the Relationship between Measurement and Machine Learning): Current State of the Field\",\"authors\":\"Yi Zheng, Steven Nydick, Sijia Huang, Susu Zhang\",\"doi\":\"10.1111/emip.12593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as <i>measurement</i>). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years’ literature. We provide a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles (e.g., applications, conceptual, etc.), (3) ML methods discussed, and (4) potential risks associated with involving ML in measurement, which result from the differences between what measurement tasks need versus what ML techniques can provide.</p>\",\"PeriodicalId\":47345,\"journal\":{\"name\":\"Educational Measurement-Issues and Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Educational Measurement-Issues and Practice\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/emip.12593\",\"RegionNum\":4,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Measurement-Issues and Practice","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/emip.12593","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
摘要
近年来,机器学习(ML)的迅猛发展影响了许多学科,包括教育和心理测量(以下简称测量)。在测量文献中,应用 ML 解决测量问题的案例迅速增加。然而,正如我们在本文中所强调的,必须严格审查将 ML 应用于测量的潜在风险。MxML 项目旨在探索测量与 ML 之间的关系,从而识别和应对风险,更好地利用 ML 的力量为测量任务服务。本文介绍了 MxML 项目的第一项研究,通过对最近 10 年的文献进行系统回顾,我们总结了有关测量背景下 ML 的应用、扩展和讨论领域的现状。我们提供了以下方面的文献快照:(1) 讨论 ML 的测量领域;(2) 文章类型(如应用、概念等);(3) 讨论的 ML 方法;(4) 将 ML 应用于测量的潜在风险,这些风险源于测量任务的需求与 ML 技术所能提供的需求之间的差异。
MxML (Exploring the Relationship between Measurement and Machine Learning): Current State of the Field
The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as measurement). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years’ literature. We provide a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles (e.g., applications, conceptual, etc.), (3) ML methods discussed, and (4) potential risks associated with involving ML in measurement, which result from the differences between what measurement tasks need versus what ML techniques can provide.