Blinding Data

我的研究将在这样的社会和政治环境中进行，在这种环境中，数字设备、全球在线平台、传感器和闭路电视摄像头的激增使人们能够捕获海量信息，为其赢得了“大数据”的称谓，并被警句描述为“新石油”(经济学人，2017)。在学校，数据来自监控出勤的系统，从学生与应用程序和学习环境的互动，从基线和总结性测试和考试。人们对源自面部识别技术、可穿戴设备和数据记录器的行为、社会和情感信息的兴趣日益浓厚。教育技术系统收集学生数据，以模拟学生的能力和行为，从而预测成绩和个性化学习。他们使用人工智能(AI)和机器学习(ML)技术，在一个数字很难挑战但仍然服从于神秘算法干预的社会里，阐明了一个“数字孩子”的概念。我的研究将探索这些技术如何通过它们构建、测量和控制教师和学生的方式，代表并构成一种强大的“授权观看”和治理模式(Jasanoff，2015)，从而在教育领域提出关于学习和知识的紧迫问题。这项工作将引入急需的跨学科视角，以了解密集数据处理对新一代学生的社会和伦理影响。虽然越来越多的关键工作关注高等教育中的数据通信，但我的研究将通过考察爱丁堡地区中学对数据的无情强调的影响来解决一个缺口。方法论我的本体论立场可以概括为这样一种信念，即没有任何东西是与它被认识和赋予价值的方式截然不同的。这一信念对于追踪构成大数据及其至高无上地位的复杂社会物质网络非常重要。我的立场需要一种方法来解决离散和客观数据概念的问题，我将采用后定性方法，因为它致力于重新思考传统的研究方法、数据和分析。我将从唐娜·哈拉韦、凯伦·巴拉德和伊丽莎白·圣皮埃尔等人的作品中获得灵感。我将与爱丁堡地区三所中学的老师和学生合作，开展类似Ruppert的“Para-Sites”的实验和参与性项目，这些项目“将研究、反思和报告与参与者的组合结合起来”(2018年，第25页)，使用三种定制的方法：“数据时代的生活”、“iVersify”和“邪恶！”。从研究经验中以响应和负责任的方式出现了定性后的方法。他们的特点包括人种学上的敏感，对怀疑、困难、假设和意外保持警惕。我将注意理解的分解，以避免研究材料的固定框架，并保持对构成有效和重要数据的情感和体现的敏感。后定性方法论旨在将被遗漏和边缘化的东西浮出水面，创造性地产生新的可能性。《参考经济学家》(2017)。世界上最有价值的资源不再是石油，而是数据。经济学家[网站]。可在：https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data Jasanoff，S.(2015年)获得。不完美的未来：科学、技术和现代性的想象。在S.Jasanoff和S.-H.Kim(编辑)中，《现代性的梦境：社会技术想象和权力的制造》，(第1-35页)。伊利诺伊州芝加哥：芝加哥大学出版社。Ruppert，E.(2018)。“不同数据未来的社会技术想象：公民数据的实验”。荷兰和佛兰芒社会学协会会议和第三次Van Doorn讲座。荷兰鹿特丹。2018年6月。

My research will take place in a social and political climate in which a proliferation of digital devices, global online platforms, sensors and closed-circuit cameras has enabled the capture of vast amounts information, earning it the appellation "Big Data" and epigrammatic depiction as the "new oil" (Economist, 2017). In school, data is harvested from systems monitoring attendance, from pupils' interaction with apps and learning environments and from baseline and summative testing and examination. There is a burgeoning interest in behavioural and social and emotional information derived from facial recognition technology, wearable devices and data-loggers. Education technology systems harvest student data for modelling aptitude and behaviours in order to predict achievement and personalise learning. Using Artificial Intelligence (AI) and Machine Learning (ML) techniques, they articulate a "child-by-numbers" in a society in which numbers are hard to challenge yet remain amenable to inscrutable algorithmic intervention. My research will explore how these technologies both represent and constitute a powerful mode of "authorised seeing" and governance (Jasanoff, 2015) by the ways in which they construct, measure and control teachers and pupils, raising pressing questions about learning and knowledge in the educational domain. The work will introduce much-needed interdisciplinary perspectives to understand the social and ethical impact of intensive data processing on new generations of students. Whilst there is a growing body of critical work focusing on datafication in Higher Education, my research will address a gap by examining the impact of an unrelenting data-emphasis in secondary schools in the Edinburgh region. Methodology My ontological position is summarised by the belief that nothing is known distinct from the way in which it is known and given value. This belief is important for tracing the complex sociomaterial networks that constitute big data and its claims for supremacy. My position calls for a methodology that troubles notions of discrete and objective data and I will employ a postqualitative approach because of its commitment to rethink traditional research methods, data and analysis. I will draw inspiration from the work of Donna Haraway, Karen Barad and Elizabeth St Pierre among others. I will collaborate with teachers and pupils in three secondary schools in the Edinburgh area on experimental and participatory projects akin to Ruppert's "para-sites" which "combine research, reflection and reporting and a mix of participants" (2018, p.25) using three bespoke methods: "A life in the day of data", "iVersify" and "Wicked!". Postqualitative methods emerge in responsive and responsible ways from the research experience. Their hallmarks include ethnographic sensibilities and being alert to doubt, difficulty, assumption and surprise. I will attend to breakdowns in understanding to avoid a fixed framing of the research material and remain sensitive to the affective and embodied as constituting valid and important data. A postqualitative methodology aims to surface that which is elided and marginalised and to creatively engender new possibilities. References Economist (2017). The world's most valuable resource is no longer oil, but data. Economist [Website]. Available at: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data Jasanoff, S. (2015). Future imperfect: Science, technology, and the imaginations of modernity. In S. Jasanoff & S.-H. Kim (Eds.), Dreamscapes of modernity: Sociotechnical imaginaries and the fabrication of power, (pp. 1-35). Chicago, IL: University of Chicago Press. Ruppert, E. (2018). 'Sociotechnical imaginaries of Different Data Futures: An experiment in citizen data'. Dutch and Flemish Sociological Association Conference and the Third Van Doorn Lecture. Rotterdam, Netherlands. June 2018.