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.