Haunted Data: The Colonial Residues of Transnational School Reforms in Kenya^*

Abstract

The coming of Big Data is offered as a salve that will reduce global inequalities and grow national economies. The chapter pursues how notions of progress have traveled into schooling through technology and generate differences and exclusions in the past and present. The chapter explores how transnational school reforms during the colonial era were directed to adapting education to “the African,” which connected expertise in the U.S., UK, and Africa through a shared set of standards, principles, and values about what constituted civilization and development. In school reforms today, the “African” has disappeared today in favor of the “all”; however, residues of educational values and judgments that made up the African as a peculiar and pathological target of colonial schooling still haunt the present. The chapter argues that today’s transnational school reforms continue to presume target communities are passive, pathological objects whose transformation depends upon their learning to act rationally. Whereas in the past this was envisioned as individuals’ and communities’ assimilation through surveys and questionnaires, today rationality is managed through integration in systems and optimizing users’ choices through data mining and algorithms. The narrative of data as grounding rational thought and action is a seductive one that offers optimism to schooling; however, faith in the coming of technology impairs historical reflection and ethical reflexivity toward schooling’s values and judgments, and the differences and exclusions they generate.

Citation

Kirchgasler, C. (2019), "Haunted Data: The Colonial Residues of Transnational School Reforms in Kenya^*", Jules, T.D. and Salajan, F.D. (Ed.) The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education (International Perspectives on Education and Society, Vol. 38), Emerald Publishing Limited, Leeds, pp. 215-232. https://doi.org/10.1108/S1479-367920190000038013