Machine Learning Introduces New Perspectives to Data Agency in K—12 Computing Education

Abstract

This innovative practice full paper is grounded in the societal developments of computing in the 2000s, which have brought the concept of information literacy and its many variants into limelight. Widespread tracking, profiling, and behavior engineering have set the alarms off, and there are increasing calls for education that can prepare citizens to cope with the latest technological changes. We describe an active concept, data agency, that refers to people's volition and capacity for informed actions that make a difference in their digital world. Data agency extends the concept of data literacy by emphasizing people's ability to not only understand data, but also to actively control and manipulate information flows and to use them wisely and ethically.This article describes the theoretical underpinnings of the data agency concept. It discusses the epistemological and methodological changes driven by data-intensive analysis and machine learning. Epistemologically the many new modalities of automation are non-reductionist, non-deterministic, and statistical; the models they rely on are soft and brittle. This article also presents results from a pilot study on how to teach central machine learning concepts and workflows in K-12 through co-creation of machine learning-based solutions.