Paper short abstract:

Focusing on data analytic pedagogy, this paper shows how students learn to make sense of algorithmic output in relation to data, code, and prior knowledge. I showcase this by drawing out the relation and contrast between human and machine understanding of algorithmically outputted numbers.

Paper long abstract:

This paper conceptualizes data analytics as a situated process: one that necessitates iterative decisions to adapt prior knowledge, code, contingent data, and algorithmic output to each other. Learning to master such forms of iteration, adaption, and discretion then is an integral part of being a data analyst. In this paper, I focus on the pedagogy of data analytics to demonstrate how students learn to make sense of algorithmic output in relation to underlying data and algorithmic code. While data analysis is often understood as the work of mechanized tools, I focus instead on the discretionary human work required to organize and interpret the world algorithmically, explicitly drawing out the relation between human and machine understanding of numbers especially in the ways in which this relationship is enacted through class exercises, examples, and demonstrations. In a learning environment, there is an explicit focus on demonstrating established methods, tools, and theories to students. Focusing on data analytic pedagogy, then, helps us to not only better understand foundational data analytic practices, but also explore how and why certain forms of standardized data sensemaking processes come to be. To make my argument, I draw on two sets of empirics: participant-observation of (a) two semester long senior/graduate-level data analytic courses, and (b) a series of three data analytic training workshops taught/organized at a major U.S. East Coast university. Conceptually, this paper draws on research in STS on social studies of algorithms,sociology of scientific knowledge, sociology of numbers, and professional vision.