Big data, black boxes and bias: the algorithmic identity and educational practice

This dissertation adds to a burgeoning conversation in education about the implications of commercial platforms being embedded in classrooms and educational practice. Drawing on a postdigital Deleuzian perspective, the study explores how Australian K-12 teachers are negotiating their educational practice as part of a broader data-driven infrastructure which includes predictive analytics and algorithmic bias. It does this by considering the changing role of the teacher’s digital profile through a transdisciplinary lens derived from Education, Media and Communications, and Learning Analytics. Drawing on twelve months of data generation, consisting of an online survey (N=129), two phases of interviews (N=40) with 23 educators from across Australia, and a platform analysis (Edmodo), the study illuminates a startling correlation between the commercial profiling of teachers and relatively intangible workplace hazards. The findings show that teachers are negotiating commercial platforms as a form of psychosocial risk in the workplace, yet not discussing their concerns due to fears of workplace victimization. As such, the study uses the findings to offer theoretical and practical approaches, aimed at improving workplace conditions for teachers. Introducing the eMorpheus Theory - a series of practical recommendations for teachers, schools and Australian Departments of Education, the study details a National Strategy in K-12 Educational Settings, suggests Policy and Legislation, and advises methods for Co-regulation and Self-regulation of commercial platforms and data stewardship in schools. The study concludes by detailing recommendations for further research as a result of the workplace issues identified in Australian educational settings.