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Education, privacy, and big data algorithms: Taking the persons out of personalized learning

机译：教育，隐私和大数据算法：使人员脱离个性化学习

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In this paper, we review the literature on philanthropy in education to provide a larger context for the role that technology company foundations, such as the Bill and Melinda Gates Foundation and Chan Zuckerberg Initiative, are playing with respect to the development and implementation of personalized learning. We then analyze the ways that education magazines and tech company foundation outreach discuss personalized learning, paying special attention to issues of privacy. Our findings suggest that competing discourses on personalized learning revolve around contested meanings about the type of expertise needed for twenty-first century learning, what self-directed learning should look like, whether education is about process or content, and the type of evidence that is required to establish whether or not personalized learning leads to better student outcomes. Throughout, privacy issues remain a hot spot of conflict between the desire for more efficient outcomes and a whole child approach that is reminiscent of John Dewey’s insight that public education plays a special role in creating citizens.
机译：在本文中，我们将回顾有关教育慈善事业的文献，以便为比尔和梅琳达·盖茨基金会和Chan Zuckerberg Initiative等技术公司基金会在开发和实施个性化学习方面发挥的作用提供更大的背景。 。然后，我们分析教育杂志和科技公司基金会的外展讨论个性化学习的方式，特别注意隐私问题。我们的研究结果表明，关于个性化学习的竞争性论述围绕着二十一世纪学习所需的专业知识类型，自我导向的学习应像什么样，教育是关于过程还是内容以及证据类型的有争议的含义展开的。确定个性化学习是否可以带来更好的学生成绩所必需的。在整个过程中，隐私问题仍然是寻求更有效结果的愿望与整个儿童方法之间发生冲突的热点，这让人想起约翰·杜威（John Dewey）的见解，即公共教育在培养公民方面发挥着特殊作用。

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