Stanford Education Data Archive (SEDA)

Racial, socioeconomic, and gender disparities in academic performance and educational attainment are stubborn features of the U.S. educational system. These disparities are neither inevitable nor immutable, however. They have been produced by—and so may also be reduced by—a welter of social and economic policies, social norms and patterns of interaction, and the organization of American schooling. The Stanford Education Data Archive (SEDA) is an initiative aimed at harnessing data to help us—scholars, policymakers, educators, parents—learn how to improve educational opportunity for all children. We are making the data files public so that anyone who is interested can obtain detailed information about American schools, communities, and student success. We hope that researchers will use these to generate evidence about what policies and contexts are most effective at increasing educational opportunity, and that such evidence will inform educational policy and practices. SEDA includes a range of detailed data on educational conditions, contexts, and outcomes in schools and school districts across the United States. It includes data at a range of institutional and geographic levels of aggregation, including schools, districts, counties, commuting zones, metropolitan areas, and states. It includes measures of academic achievement, achievement gaps, school and neighborhood racial and socioeconomic composition, school and neighborhood racial and socioeconomic segregation patterns, and other features of the schooling system. The data will be regularly updated as more data become available. The SEDA 2022 dataset is unique from other versions of SEDA. The most recent version, SEDA 2022 2.0, includes district-level data for a larger subset of states on the average math and reading achievement in 2019 and 2022 respectively, and the change in math and reading achievement between 2019 and 2022 relative to the national average in grades 3-8 in 2019. DATA USE AGREEMENT: You agree not to use the data sets for commercial advantage, or in the course of for-profit activities. Commercial entities wishing to use this Service should contact Stanford University’s Office of Technology Licensing ([e-mail]). You agree that you will not use these data to identify or to otherwise infringe the privacy or confidentiality rights of individuals. THE DATA SETS ARE PROVIDED “AS IS” AND STANFORD MAKES NO REPRESENTATIONS AND EXTENDS NO WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED. STANFORD SHALL NOT BE LIABLE FOR ANY CLAIMS OR DAMAGES WITH RESPECT TO ANY LOSS OR OTHER CLAIM BY YOU OR ANY THIRD PARTY ON ACCOUNT OF, OR ARISING FROM THE USE OF THE DATA SETS. You agree that this Agreement and any dispute arising under it is governed by the laws of the State of California of the United States of America, applicable to agreements negotiated, executed, and performed within California. You agree to acknowledge the Stanford Education Data Archive as the source of these data. In publications, please cite the data as: Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., Jang, H., & Chavez, B. (2021). Stanford Education Data Archive (Version 4.1). Retrieved from http://purl.stanford.edu/db586ns4974. Reardon, S. F., Fahle, E. M., Ho, A. D., Shear, B. R., Kalogrides, D., Saliba, J., & Kane, T. J. (2023). Stanford Education Data Archive (Version SEDA2022 2.0). Retrieved from http://purl.stanford.edu/db586ns4974. Subject to your compliance with the terms and conditions set forth in this Agreement, Stanford grants you a revocable, non-exclusive, non-transferable right to access and make use of the Data Sets.美国教育体系中长期存在着学业表现与受教育程度层面的种族、社会经济地位及性别差距，这已然成为其难以根除的痼疾。然而这类差距并非不可避免，亦非一成不变。它们由纷繁复杂的社会经济政策、社会规范与互动模式，以及美国学校教育的组织架构所塑造，因此也可通过这些因素加以缩小。 斯坦福教育数据档案（Stanford Education Data Archive, SEDA）是一项旨在依托数据助力学者、政策制定者、教育工作者与家长群体，探索如何为全体儿童改善教育机会的研究项目。项目团队现已公开全部数据文件，以便所有感兴趣的人士获取美国学校、社区与学生学业成就的详细信息。我们期望研究人员能够借助这些数据，就哪些政策与环境最能有效提升教育机会生成实证依据，并期待此类研究成果能够为教育政策与实践提供参考。 SEDA收录了全美范围内各级学校与学区的教育现状、环境与成果的多维度详细数据。数据涵盖了不同机构与地理聚合层级，包括学校、学区、县、通勤区、都会区与州等多个维度。其内容包含学业成就、成就差距、学校与社区的种族及社会经济结构、学校与社区的种族及社会经济隔离模式，以及教育体系的其他相关特征。随着更多数据的收集完成，该数据集将定期进行更新。 SEDA 2022数据集与SEDA其他版本存在显著差异。其最新版本SEDA 2022 2.0新增了更多州的学区层级数据，涵盖2019年与2022年的数学、阅读平均学业成就，以及2019年3至8年级相较于全国平均水平的2019至2022年数学与阅读学业成就变化量。 数据使用协议： 您同意不得将本数据集用于商业牟利或任何营利性活动。有意使用本服务的商业实体，请联系斯坦福大学技术许可办公室（邮箱：[e-mail]）。 您同意不得使用本数据集识别个人，或以其他方式侵犯个人的隐私或保密权利。 本数据集按"现状"提供，斯坦福大学不作任何明示或暗示的陈述与担保。对于您或任何第三方因使用本数据集而产生的任何损失或其他主张，斯坦福大学概不承担任何责任。 您同意本协议及因本协议产生的任何争议，均受美利坚合众国加利福尼亚州法律管辖，且适用于在加利福尼亚州谈判、签署与履行的协议。 您同意在引用数据来源时，需标注斯坦福教育数据档案。如用于出版物，请按以下格式引用： Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., Jang, H., & Chavez, B. (2021). Stanford Education Data Archive (Version 4.1). Retrieved from http://purl.stanford.edu/db586ns4974. Reardon, S. F., Fahle, E. M., Ho, A. D., Shear, B. R., Kalogrides, D., Saliba, J., & Kane, T. J. (2023). Stanford Education Data Archive (Version SEDA2022 2.0). Retrieved from http://purl.stanford.edu/db586ns4974. 若您遵守本协议的各项条款与条件，斯坦福大学将授予您一项可撤销、非排他、不可转让的权限，以访问并使用本数据集。

搜集汇总

背景与挑战

背景概述

Stanford Education Data Archive (SEDA) 是一个专注于美国教育机会改善的数据集，提供全国范围内学校、学区、县等多层次的教育条件、学术成就、成就差距以及种族和社会经济构成等详细数据。其独特之处在于SEDA 2022 2.0版本扩展了学区级数据，覆盖更多州的2019年和2022年数学和阅读平均成就及其变化，旨在支持研究和政策制定以减少教育不平等。