Post-Humanism and Literacy Studies

So much of contemporary education reform centers on the individual. We use test scores to track students for admission to schools and courses (Ravitch, 2010), deploy learning management technologies to codify changes in an individual's performance (e.g., Nichols, 2012), and even tabulate data to hold teachers accountable for "value-added" measures that capture students' learning (Amrein-Beardsley, 2008). Such mechanisms for quantifying teaching and learning to better classify and evaluate individuals are not a new phenomenon (cf. Kliebard, 2004; Koretz, 2008); however, they take on new meaning in a time when key decision makers are increasingly enchanted with the promise of "Big Data" and its attendant technologies to address our long-standing social problems (Andrejevic, 2013; Morozov, 2013). From federal policies like the Every Student Succeeds Act (The White House, 2015) to a growing body of education research (e.g., Bambrick-Santoyo, 2010; Marsh, Pane, & Hamilton, 2006), "data-driven" schooling has emerged as a taken-for-granted ideal. This, perhaps, is no surprise, as researchers and policymakers generally agree that teachers ought to make decisions for practice based on carefully weighed evidence. But without a clear articulation of what counts as "evidence," data-driven education can quickly become a way to locate responsibility in individual students and teachers for the strengths and shortcomings of classrooms.Of course, these uses of data-driven metrics are not without their critics. Literacy scholars have long documented how such accountability measures are not neutral indicators, but rather they reshape curricula and instruction in their image (e.g., Hillocks, 2002; Zacher-Pandya, 2011) and often assign personal responsibility for systemic inequities (Stevens & Piazza, 2010). Others argue that these data-driven techniques in fact reproduce inequalities related to race, class, and ability and reduce the complexity of student potential to a number (Campano, 2007; Campano, Ghiso, & Sanchez, 2013; Dutro & Selland, 2012). Even common sense can poke holes in the internal logic of individualized, data-driven calculation: any teacher knows that factors as simple as the time of day, the temperature in the room, and the availability of specific materials can dramatically alter students' dispositions, performances, and engagement. But variables like these are not-and, indeed, cannot-be easily folded into rubrics that place the locus of success, failure, and growth on individuals alone.To broaden conceptions of what counts as "data" and to address the limitations of a focus on individuals as the singular, determining factor in teaching and learning, researchers have increasingly looked to theories of materiality as a way to account for the complexities of classroom activity (Jones et al., 2016; Taguchi, 2009; Taylor, 2013). Unlike human-centered perspectives, which foreground individual agency and intention in explaining social outcomes, a material stance is sometimes called post-humanist because it begins from the premise that humans never act in isolation, but rather in concert with changing networks of people, objects, histories, and institutions. For example, instead of assuming that a high-stakes assessment is simply a matter of frictionless transfer between an individual student's mind and an answer key, a post-human approach works to identify the layered dimensions that mediate this exchange: the biological (synapses firing in the brain), physical (cramps in the writing hand), environmental (temperature and condition of the testing room), cultural (relation between student identity and the assumed background knowledge in exam questions), institutional (the web of standards, curricula, and instruction that underpins a student's formal learning to date), and so on. From a post-human perspective, all of these dimensions work together in a contingent interplay to produce any literacy event-be it taking a test, reading a book, or completing a class project. …