Heather M. Buzick, Jodi M. Casabianca, Melissa L. Gholson
{"title":"在实践中个性化大规模评估","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":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"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\":null,\"pages\":null},\"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}","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}
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.