epistemologies of data
no. 18_december 2023
This issue of On_Education addresses data as the foundational means of research practice, ethical and social implications of data, as well as the governance of data by highlighting the recent past of data (and its bias), methodological implications of data, ontologies of data, and the political relevance of data. The seven articles offer thought-provoking perspectives to deepen our understanding of the complex relationship between data and educational research. With these perspectives, the editors hope to contribute to and stimulate ongoing discussions about the meaning and relevance of data use in and for education.
The creation of international large-scale assessments in the 1960s and 1970s was inevitably a product of the movement of data. Large amounts of information about school performance circled the globe: first collected in individual schools, then aggregated at the national level, then sent to an international centre, and finally returned for further national analysis and dissemination. While the movement of data was a prerequisite for international testing, there was an inherent risk in how the work was organised. The concepts of data friction and precarious knowledge are used to describe how the pace of information transfer was slowed or how information was in danger of being lost as it circled the globe. The movement of data brought with it the double possibility of creating and destroying knowledge.
This paper examines critical design factors that influence data quality in educational research, using international large-scale educational assessments as an example. We will focus on statistical challenges related to sampling, measurement, and causality. While international assessments employ rigorous random sampling techniques, deviations such as exclusions and non-participation can introduce bias and affect representativeness. In terms of measurement, although these assessments excel in core domains, there is a growing call for broader assessment areas, such as environmental literacy and civic education. Additionally, concerns are emerging about the quality of context surveys. Causality remains a central concern, and despite the challenges posed by the cross-sectional design, combining data and applying sophisticated analytical methods can help address causal questions. Recognising the interconnectedness of sampling, measurement, and causality is essential for conducting robust research and informing evidence-based policies and practices.
In the age of data-driven research, data visualisations have emerged and proliferated as indispensable tools for understanding complex phenomena. Visualisations can be compelling. They communicate objectivity, efficiency and authority. However, data visualisations are not neutral. They embody and convey particular epistemological perspectives, shaping how knowledge is produced, circulated and understood (D’Ignazio & Klein, 2020; Ratner & Ruppert, 2019; Williamson, 2016). In scholarship, visualisations are, like other scientific practices, ‘designed to make the invisible visible, the evanescent permanent, the abstract concrete’, and in this sense, visualisations are ways in which scholarship ‘discovers the world anew’ (Daston & Lunbeck, 2011, p. 1). This article explores the epistemologies inherent in data visualisations when they are produced within critical studies of the datafication of education. It takes up the call for researchers to interrogate the tensions that arise when the ‘digital…
What is made in/visible in datafied educational settings? How are in/visibilities entangled with data practices in schooling? In this contribution, we provide an overview of recent literature on digital technologies and datafication of education and consider four kinds of interrelations between in/visibilities and data practices: decontextualisation, in/visibilising of data work, black-boxing, and fore- or backgrounding. Scoping some examples of research that capture aspects of invisibility and visibility, we reflect on conceptual and methodological underpinnings of how to study in/visibilities and data practices in education.
This is an experimental application of the Jaina seven-valued logic on the question of dataism, the problematic, chiefly Euro-US-centric, ideology for data-driven technosolutionism, in the context of higher education. Based on my own teaching experience and on this underrepresented onto-epistemological framework from India, I show the simultaneous validity of seven seemingly contradictory arguments, suggesting that this may be a practical way to escape unnecessary disputes within the field of critical data studies. These arguments focus on the interplay between different contexts where data are considered meaningful (or meaningless) and/or existentially relevant (or irrelevant).
Dealing with the tensions between scientific claims and those of political-administrative actors marks a central task of municipal educational monitoring. However, empirical research has not systematically examined the relationship between these significant references. Our contribution focuses on how the tensions are addressed and balanced in practice. The results reveal different relations between the two perspectives, producing various practical forms of local educational monitoring. In this way, establishing data infrastructures in municipalities becomes visible as a complex and situational process. The results reflect educational monitoring’s theoretical and practical development needs and implications for education research.
This essay problematizes the so-far absence of broader critical debate on the governing dynamics around research data infrastructuring (not only) in education scholarship. More specifically, it refers to the growing role of large-scale data infrastructures as well as complexes of Research Data Management (RDM) that seek to foster ‘open’, reusable, and increasingly machine-readable (education) science. While research data infrastructuring in general, and RDM in particular, have so far been predominantly discussed from solutionist-positivist perspectives, this essay discusses the underestimated risks that lie in the governance dynamics of research data infrastructuring, including: (1) the gradual narrowing of what kind of education, but also what kind of education research become visible in and through data infrastructures (namely those that build on already dominant hierarchies in the field); (2) a gradual shift from doing research towards ‘doing data’, including a shift of political attention and research funding, as well as (3) effects of normalization, also triggered through extensive dataveillance, shareveillance, and ‘ethical monitoring’ of researchers. This particularly endangers genuinely qualitative and critical research in education.
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