Heather M. Buzick, Jodi M. Casabianca, Melissa L. Gholson
{"title":"Personalizing Large-Scale Assessment in Practice","authors":"Heather M. Buzick, Jodi M. Casabianca, Melissa L. Gholson","doi":"10.1111/emip.12551","DOIUrl":null,"url":null,"abstract":"<p>The article describes practical suggestions for measurement researchers and psychometricians to respond to calls for social responsibility in assessment. The underlying assumption is that personalizing large-scale assessment improves the chances that assessment and the use of test scores will contribute to equity in education. This article describes a spectrum of standardization and personalization in large-scale assessment. Informed by a review of existing theories, models, and frameworks in the context of current and developing technologies and with a social justice lens, we propose steps to take, as part of assessment research and development, to contribute to the science of personalizing large-scale assessment in technically defensible ways.</p>","PeriodicalId":47345,"journal":{"name":"Educational Measurement-Issues and Practice","volume":"42 2","pages":"5-11"},"PeriodicalIF":2.7000,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Measurement-Issues and Practice","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/emip.12551","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: 1
Abstract
The article describes practical suggestions for measurement researchers and psychometricians to respond to calls for social responsibility in assessment. The underlying assumption is that personalizing large-scale assessment improves the chances that assessment and the use of test scores will contribute to equity in education. This article describes a spectrum of standardization and personalization in large-scale assessment. Informed by a review of existing theories, models, and frameworks in the context of current and developing technologies and with a social justice lens, we propose steps to take, as part of assessment research and development, to contribute to the science of personalizing large-scale assessment in technically defensible ways.