ABSTRACT

While much has been written about the personal, social, and democratic benefits of networked communities and participatory learning, critics have begun to draw attention to the ubiquitous data collection and computational processes behind mass user platforms. Personal and behavioral data have become valuable material for statistical and machine learning techniques that have the potential to profile, infer, and predict people’s needs, values, and behavior. As a response, researchers are calling for data literacies and computational thinking to facilitate people’s capacity and volition to make informed actions in their digital world. Yet, efforts and curricula towards a greater understanding of computational mechanisms of new media ecology are sorely missing from K12-education as well as from teacher education. This paper provides an overview of tensions that teachers and educators will face when they attempt to bridge participatory learning with a more robust understanding of machine learning and algorithmic production of social and cultural practices.

摘要

尽管关于网络社区和参与式学习对个人，社会和民主的好处已写了很多文章，但批评家已开始引起人们对大规模用户平台背后无处不在的数据收集和计算过程的关注。个人和行为数据已成为用于统计和机器学习技术的有价值的材料，这些技术有可能剖析，推断和预测人们的需求，价值和行为。作为回应，研究人员呼吁进行数据知识和计算思考，以提高人们的能力和意志，以在其数字世界中做出明智的行动。然而，在K12教育以及师范教育中，都缺少为更好地理解新媒体生态的计算机制而付出的努力和课程。