Data Science, Algorithms, and Curriculum Studies

Abstract

Articles in this issue build on the growing body of humanities-based scholarship delving into the realm of data science and algorithms. This cutting-edge work should not be ignored by our field! Just as Curriculum Studies blossomed through interactions with 20^th-Century humanities, 21^st-Century engagements with data science and algorithms reveals new terrain and conceptual opportunity, elaborating science fields and associated, long standing sociological, historical, cultural and economic concerns. New perspectives on racism, patriarchy, heteronormativity, settler colonialism, and ethnocentrism, for example, potentially bring fresh and vibrant directions. We invite Curriculum Studies scholars to catch up on the growing literature of critical data science, and to begin probing the many ways that data and algorithms shape educational experiences at all levels and in all educational contexts. We also believe that Curriculum Studies can bring many insights to data science and algorithm studies, just as Educational Studies has pushed scholarship on any and all experiences to appreciate the role of power-knowledge relationships, designed environments, and institutions of education (family, religious communities, popular culture, public spaces, advertising, political truthiness, etc.). For us, the “issue” is not, “Should we join in the discourses around data, algorithms and social justice matters?” but rather, “Why has it taken Curriculum Studies so long to explore this work, and to join the fray?”