A pedagogy of data and Artificial Intelligence for student subjectification
Mary Loftus, Michael G. Madden
Abstract
How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on ‘subjectification’ i.e. ‘the coming into presence of unique individual beings’? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher education, is a hands-on approach to building an open, collaborative pedagogy of data literacy, based on Bayesian Networks (BNs) (Pearl, J. 1985. Bayesian Networks: A Model of Self–Activated Memory for Evidential Reasoning. Los Angeles: University of California (Computer Science Department)). BNs can be used to merge subjective views of the learning process with objective data analysis from the learning environment; BNs are visual data constructs and, unlike other Machine Learning approaches that obfuscate and complexify, BNs can be developed to reveal relationships from observations. In this paper, we share ways in which teachers and students can work together in a praxis approach to use data to ‘read the world’ around them (Freire, P. 1970. Pedagogy of the Oppressed. New York: Continuum. 125).