Attending to data: Exploring the use of attendance data within the datafied school

Abstract

Contemporary schooling is seen to be altering significantly in light of a combined ‘digitisation’ and ‘datafication’ of key processes. This paper examines the nature and conditions of the datafied school by exploring how a relatively prosaic and longstanding school metric (student attendance data) is being produced and used in digital form. Drawing on empirical data taken from in-depth qualitative studies in three contrasting Australian secondary schools, the paper considers ‘anticipatory’, ‘analytical’ and ‘administrative’ aspects of how digitally-mediated attendance data is produced, used and imagined by school staff. Our findings foreground a number of constraints, compromises and inconsistencies that are usually glossed-over in enthusiasms for ‘data-driven’ education. It is argued that these findings highlight the messy realities of schools’ current relationships with digital data, and the broader logics of school datafications.

Supporting the initial work of evidence-based improvement cycles through a data-intensive partnership

“…Rather, through describing the initial steps of working with school practitioners, our goal is to provide examples of how researchers and practitioners could work together to turn data into useful information for knowledge generation. Given the recent sobering research that has indicated that Step 2 of the data use cycle may be a problematic sticking point for many schools (Selwyn et al , in press; Farley-Ripple et al , 2021; Wayman et al , 2017), our results highlight how data science workflows (Agasisti and Bowers, 2017; Krumm et al , 2018) can be integrated into current ongoing improvement cycles within a school or district to help address the needs of practitioners. It is our contention that collaborative educational data science (Bowers et al , 2019; Bowers, 2017; Piety, 2019; Piety et al , 2014; Agasisti and Bowers, 2017) can and should be included within the logic model of data use in schools.…”

“…Recent examples include low engagement by educators in popular data systems such as NWEA MAP (Farley-Ripple et al , 2021), few if any significant relationships between teacher instructional clicks in a data dashboard and student outcomes in a large US school district (Wayman et al , 2017) and a tendency to reify problematic inferences about student knowledge and learning when observing teacher data team meetings to discuss individual student assessment answer data (Garner et al , 2017). An issue rarely noted throughout the literature on school data use was recently highlighted by Selwyn et al (in press) in that more often than not “data analytics” takes the form of having the “data person” in the school work in spreadsheets to create “simple frequency counts, color-coding and modest cross-tabulations” (p. 13). Moreover, technological tools that were intended to support more inspired data analysis were, according to Selwyn and colleagues:…”

“…Above all, was an overriding sense that people working within these schools (in contrast to any imagined possibilities) retained relatively limited ambitions for their actual use of data […]. Tellingly, any broader insights from these analyses were yet to translate into follow-up action within the school (p. 14) (Selwyn et al , in press).…”

“…These authors conclude in this study that the advocacy for data use in schools “[…] needs to be set against the observation that schools do not appear to be particularly motivated to respond to (or even look for) novel insights and unexpected patterns and correlations in their data. In short, school data is not a place for surprises, counterintuition and ‘outside-the-box’ thinking” (p. 15) (Selwyn et al , in press). Thus, for these reasons, in the present study, we focus exclusively on the second step of the data use cycle of organize, filter and analyze (Figure 1), as an under-researched area in which we aim to describe the case of bringing together the domains of data visualization and education data science in application to a data use cycle to potentially cultivate school capacity to identify novel insights, unexpected patterns, and correlations in the data that can create surprise and new ways for educators to see their own data from their school to make decisions.…”

“…Visual data analysis “blends […] computational methods with sophisticated graphics engines to tap the ability of humans to see patterns and structure in complex visual presentations (Johnson et al , 2010). Visual data analysis is designed to help expose patterns, trends and exceptions in very large heterogeneous and dynamic data sets collected from complex systems” (p. 15) (Bienkowski et al , 2012), which we draw on here as a means to organize, filter, analyze and visualize the data in an effort to help educators identify “novel insights and unexpected patterns and correlations in their data” (p. 15) (Selwyn et al , in press).…”