1 Center for Information Technology Policy, Princeton University , Princeton, New Jersey.
2 Information Society Project, Yale Law School , New Haven, Connecticut.
Big Data. 2017 Jun;5(2):164-172. doi: 10.1089/big.2016.0061.
Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.
教育工作者和评论员在评估大数据驱动的学习环境时，关注的是一些具体的问题：自动化教育平台是否能提高学习成果，是否侵犯学生隐私，以及是否能促进教育公平。本文抛开了具体技术的具体效果方面一些尚未解决的、也许无法解决的问题。相反，它考察了大数据驱动的工具如何改变学校教学决策的结构，并由此改变了美国教育事业的一些基本方面。在学习环境中，技术中介和数据驱动的决策具有特别重大的影响，因为教育过程主要由动态信息交流组成。在这篇概述中，我强调了学校依赖于执行核心学校功能的数据驱动教学平台时伴随而来的三个重大结构转变：教学、评估和认证。首先，虚拟学习环境创建了信息技术基础设施，其特点是持续的数据收集、连续的算法评估和可能无限的记录保留。这破坏了课堂传统的知识隐私和安全。其次，这些系统将教育决策从服务公共利益的教育者转移到了私人、往往是营利性的技术提供商。它们限制了教师的学术自主权，模糊了学生评估，并降低了家长和学生参与或质疑教育决策的能力。第三，大数据驱动的工具通过映射概念、创建内容、确定指标和设定期望的教学成果来定义什么是“有价值的”教育。这些转变将重要的决策权让渡给了没有公众监督或教学审查的私人实体。与伴随教科书选择而来的公开和激烈辩论形成鲜明对比的是，学校往往会临时采用教育技术。鉴于教育对个人和集体成功的关键影响，教育工作者和政策制定者必须积极、明确地考虑数据驱动教育的影响。
