大数据在美国教育评价中的应用路径分析

Path Analysis of the Application of Big Data for Education Evaluation in the United States

实施教育评价是教育教学决策的重要前提，而有效的教育评价依赖于全面的、可靠的评价依据。大数据重在对多维、大量数据的深度挖掘与科学分析以寻求数据背后的隐含关系与价值，有助于将教育评价从基于小样本数据或片段化信息的推测转向基于全方位、全程化数据的证据性决策。该文从三个方面分析美国在整体层面如何规划与推进大数据在教育评价中的深入应用：一是“大数据为何而用”——探析美国在教育评价中应用大数据的目标指向；二是“大数据从何而来”——介绍美国教育评价所依托的立体化、高质量教育数据网络；三是“大数据如何而用(包括如何用好)”——分析美国如何通过精心设计评价本身、选择适当的大数据处理技术、提升教育工作者尤其是教师的数据素养、提供多方位支持等多种方式在实践维度切实推进大数据在教育评价中的有效应用。

Educational evaluation is one of the most important prerequisites for education policy-making and instructional decisions， while effective educational evaluation depends on overall and reliable proofs. Based on deep mining and scientific analysis of multidimensional， large amounts of data in search of hidden relationships and value， big data is promising to help educators transform educational evaluation from small samples or incomplete information based suppositions into full data based evidences. This research discusses the United States how to plan and implement the deep application of big data for educational evaluation on the macro level from the following aspects. The first question is about “why”， focusing on the recognition of the application objectives of big data for US educational evaluation. The second question is about “where”， introducing how the big data for educational evaluation are collected by use of three-dimensional， high-quality education data networks. The third question is about “how”， exploring how the United States practices big data application for educational evaluation by deliberate design， choosing appropriate big data processing technologies， improving educators’ data literacy， and providing multiple supports.