Introduction In the last ten years digitalized data have permeated our lives in a massive way. Beyond the internet ubiquity and cultural change outlined in what Castells (1996) called the network society, we are now witnessing a datafied society, where large amounts of digital data—the DNA of information—are driving new social practices. The most enthusiastic discourses on this abundance of data have emphasized the opportunity to generate new business models, with professional landscapes connected to data science and open practices in science and the public space (EMC Education Services 2015; Scott 2014). However, more recently, the rather naïve logic of data capture and its articulation through various algorithms as drivers of more economical and objective social practices have been the object of criticism and deconstruction (Kitchin 2014; Zuboff 2019). The university as an institution fell into this paradigm somehow abruptly, while striving to survive its crisis of credibility. The digitalization of processes and services was considered a form of innovation and laid the foundations for the later phenomenon of datafication (Williamson 2018). Initially, fervent discourses embraced data-driven practices as an opportunity to improve efficiency, objectivity, transparency and innovation (Daniel 2015; Siemens et al. 2013). The two main missions in higher education (HE)—teaching and research—went through several processes of digitalization that encompassed data-intensive practices. In teaching, the data about learning and learners collected on unprecedented scales gave rise to educational data mining and particularly to learning analytics (LA) (Siemens and Long 2011). While some argued about the value of learning analytics in informing teachers’ decision-making about pedagogical practices as well as learners’ self-regulation (Ferguson 2012; Roll and Winne 2015), research also uncovered naïve or even poor pedagogical assumptions on the power of algorithms to predict, support and address learning, which were connected to techno-determinist approaches to data (Ferguson 2019; Perrotta and Williamson 2018; Selwyn 2019). The studies in the field have pointed out how few connections there are between LA models and pedagogical theories (Knight et al. 2014; Nunn et al. 2016), the lack of evaluation in authentic contexts, the scant uptake by teachers and learners (Vuorikari et al. 2016a, b) and the social and ethical issues connected to the topic (Broughan and Prinsloo 2020; Slade Open Access

中文翻译：

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支持高等教育中关键数据素养的发展：社会公平数据文化的基石

简介 在过去的十年中，数字化数据已经大量渗透到我们的生活中。除了 Castells（1996）所称的网络社会中概述的互联网无处不在和文化变革之外，我们现在正在目睹一个数据化社会，其中大量的数字数据（信息的 DNA）正在推动新的社会实践。关于如此丰富的数据最热烈的讨论强调了产生新商业模式的机会，将专业领域与数据科学以及科学和公共空间的开放实践联系起来（EMC Education Services 2015；Scott 2014）。然而，最近，数据捕获的相当幼稚的逻辑及其通过各种算法的表达作为更经济和客观的社会实践的驱动因素一直是批评和解构的对象（Kitchin 2014；Zuboff 2019）。大学作为一个机构，在努力渡过信誉危机的同时，突然陷入了这种范式。流程和服务的数字化被认为是一种创新形式，并为后来的数据化现象奠定了基础（Williamson 2018）。最初，热烈的讨论将数据驱动的实践视为提高效率、客观性、透明度和创新的机会（Daniel 2015；Siemens et al. 2013）。高等教育 (HE) 的两项主要任务——教学和研究——经历了多个包含数据密集型实践的数字化过程。在教学中，以前所未有的规模收集的有关学习和学习者的数据引发了教育数据挖掘，特别是学习分析（LA）（Siemens 和 Long 2011）。虽然一些人争论学习分析在为教师做出有关教学实践的决策以及学习者的自我调节方面的价值（Ferguson 2012；Roll 和 Winne 2015），但研究也发现了关于学习分析的力量的幼稚甚至糟糕的教学假设。预测、支持和解决学习问题的算法，与数据的技术决定论方法相关（Ferguson 2019；Perrotta 和 Williamson 2018；Selwyn 2019）。该领域的研究指出，洛杉矶模型与教学理论之间的联系非常少（Knight et al. 2014；Nunn et al. 2016），缺乏真实情境中的评估，教师和学习者的理解很少（Vuorikari） et al. 2016a, b) 以及与该主题相关的社会和道德问题（Broughan 和 Prinsloo 2020；Slade 开放获取）