This chapter reviews actionable data use—both as an umbrella term and as a specific concept—developed in three different traditions that data/information can inform and guide P–20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning analytics (LA). DDDM is grounded in K–12 settings, has a social orientation, and is shaped by policy. EDM and LA began in higher education using data provided by instructional tools. This review of more than 1,500 publications traced patterns in these communities revealing disciplinary disconnects between DDDM and EDM/LA. Recognizing information’s systemic nature, this review expanded the analysis from teacher practice to educator practice. While methodological progress has been made in all areas, studies of impact were concentrated in DDDM. EDM and LA focus on tools for current/future educational settings and leveraging data harvested for basic research while reconceiving learning practices. The DDDM impact studies did not support a directly beneficial model for data use. Rather, long timescale capacity factors, including cultural and organizational processes that impact data use were revealed. A complementary model of components, infrastructure, and capacity is advanced with recommendations for scholarship in education’s sociotechnical future.

中文翻译：

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组件、基础设施和能力：探索可操作的数据使用对 P-20 教育工作者实践的影响

本章回顾了可操作的数据使用——作为一个总括术语和一个特定概念——在三种不同的传统中发展起来，数据/信息可以为 P-20 教育实践提供信息和指导，以取得更好的结果。所审查的文献被称为数据驱动决策 (DDDM)、教育数据挖掘 (EDM) 和学习分析 (LA)。DDDM 以 K-12 环境为基础，具有社会导向，并受政策影响。EDM 和 LA 开始于高等教育使用教学工具提供的数据。对 1,500 多份出版物的审查追踪了这些社区中的模式，揭示了 DDDM 和 EDM/LA 之间的学科脱节。认识到信息的系统性，本综述将分析从教师实践扩展到教育者实践。虽然在所有领域都取得了方法上的进步，影响的研究集中在 DDDM 中。EDM 和 LA 专注于当前/未来教育环境的工具，并在重新构思学习实践的同时利用为基础研究收集的数据。DDDM 影响研究不支持直接有益的数据使用模型。相反，揭示了长期容量因素，包括影响数据使用的文化和组织过程。组件、基础设施和能力的互补模型得到了推进，并提出了教育社会技术未来奖学金的建议。揭示了长期容量因素，包括影响数据使用的文化和组织过程。组件、基础设施和能力的互补模型得到了推进，并提出了教育社会技术未来奖学金的建议。揭示了长期容量因素，包括影响数据使用的文化和组织过程。组件、基础设施和能力的互补模型得到了推进，并提出了教育社会技术未来奖学金的建议。