Datafication of education
Datafication as a ‘metatrend in the context of digitisation’ (Breiter & Bock, 2023, p. 5) is understood as ‘the transformation of social action into online quantified data […] allowing for real-time tracking and predictive analysis’ (Van Dijck, 2014, p. 198), that ‘introduces new means to measure, capture […] and represent social life in numbers’ (Jarke & Breiter, 2019, p. 1). In that manner, datafication in education (Bock et al., 2023; Selwyn & Gašević, 2020) is increasingly shaping the futures of education. Education organisations such as K-12 schools rely heavily on big amounts of data being made available through school information systems and educational software to fulfil their educational mandate. To produce and make use of educational data, many actors are expected to engage in various data practices. Data practices are required, for instance, to organise teaching and learning processes, e.g., in the form of timetable planning, to measure, evaluate, and visualise learning outcomes and organisational data (e.g., rankings, learning assessment charts) (Hillman, 2023; Krein & Schiefner-Rohs, 2021; Oosterhoff et al., 2023), and as a basis for educational decision making in and out of the classroom (Bradbury, 2019; Grant, 2022; Hartong et al., 2021; Selwyn et al., 2022; Troeger et al., 2022).
For example, education agencies draw on data from schools, such as the number of cancelled classes or the number of students and teachers, to evaluate the quality of education or distribute financial resources. Parents of prospective schoolchildren might consult rankings of schools published by educational agencies or the media when choosing which school to pick for their child. A data-driven school ranking, however, might rely on a limited number of data points so that, i.e., extracurricular activities become written out and do not flow into the assessment, therefore presenting certain schools in a more negative light than their actual schooling practice. Moreover, e.g., in Germany much of the data used by schools and education agencies are inputted into school information systems by school secretaries (Zakharova & Jarke, 2022). It is required to enroll children into schools or assign them to classes. Secretaries, however, are not always considered as data workers by software producers and have little control over the systems they use in their everyday work. So, research on datafication of education shows that while data have become an inherent part of (organisation of) educational processes, it is often unclear to many involved actors how which kinds of educational data are produced and used in practice (Bock et al., 2023). These moments, in which various constellations of actors and their data-enabled relations produce in/visiblities in educational settings, are why we attend to studying data practices.
As data move from a school secretary to the principal to education authorities, they pass various publicly and commercially provided information systems. As they move, data change their types, forms, and formats – e.g., from a note that a math class was cancelled in the tenth grade to an aggregate summary of all cancellations of math classes in a year in a given school. Data also change their meanings – from a simple act of organising the timetable and sending ten-graders home instead of a math class to a politically-laden indicator of educational quality. The situatedness, fluidity, and vagueness of data make it difficult to empirically grasp them with what could be called ‘conventional’ methods or approaches (see Law, 2004). Educational actors might lack information about their own and others’ data practices or ascribe divergent meanings to the same data. For example, some aspects of data production and usage practices in educational contexts often remain opaque due to educational technology design, while others are being invisibilised alongside with some actors’ precarious working conditions (Decuypere et al., 2023; Yu & Couldry, 2022; Zakharova & Jarke, 2022, 2023). The question for educational researchers interested in data, then, is how to approach this methodologically, when data practices are related to in/visibilities in practice. In this contribution, we consider what is made in/visible in educational settings and elaborate on methodologies for studying data practices in relation to in/visibilities.
To do so, 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. An elucidation of these four kinds of in/visibilities goes along with a scoping of some examples of research that helps capture aspects of invisibility and visibility theoretically and methodologically. We conclude with an outlook on the further development of research approaches to in/visible data practices.
Studying in/visibilities and data practices
Current research on digital data is widely concerned with identifying and understanding the hard-to-see aspects of datafication processes (Zakharova, 2022). A growing body of work elaborates on diverse methodological approaches to researching and conceptualising data practices: “wide data analysis” (Manovich, 2017), big-thick-blending of data (Bornakke & Due, 2018); “data fusion” (Manovich, 2013) or the “engaging in digital practice” (Rieder & Röhle, 2017). It specifically suggests adapting established methods and thinking of creative ways or new methodologies to research invisible or invisibilising data practices (e.g., Bornakke & Due, 2018; Rogers, 2017). Yet others, for example, Neumayer et al. (2021), interrogate visibility as a process and ask how invisibilities are created in research. Neumayer and colleagues developed a four-dimensional framework of the agents and processes making data in/visible, including such dimensions as people and intentionality, accessibility and form, technology and tools, and lastly, meaning and imaginaries. This framework sensitises us towards multiple socio-technical arrangements in which research on digital data takes place. It provides a context for careful consideration of in/visibilities stemming from design of research processes. In a similar vein, Fahimi et al. (in press) reflect on uncovering in/visibilities by using different kinds of methodologies (such as mapping and tracing of data journeys), tools, and interventions (e.g., filing freedom of information requests to access data). Here, in/visibilities are emphasised as political and in relation to power to conceal or foreground certain things or the lack thereof. These methodological considerations take up and develop further the arguments widely elaborated by scholars critically interested in data and software: to pay attention to the contexts and interpretive practices of data production (generation and meaning ascription) alongside with data usage (Kitchin & Lauriault, 2014) or software design, development, and maintenance processes (e.g., Chun, 2005; Galloway, 2006; Manovich, 2013).
In the context of education, scholars also seek to understand and critically reflect on the manifold entanglements of in/visibilities and data practices (e.g., Bock et al., 2023; Macgilchrist, 2019). For example, Fenwick and Landri (2012, p. 6) point out that certain educational practices produce in/visibilities: “important everyday learning achieves an unusual visibility when it is appreciated as material enactments: this visibilisation throws up critical questions about how ‘pedagogic authority’ codifies and values some knowledges and overlooks others”. Drawing on that, we could say that digital data and data practices in education are, too, intertwined with in/visibilities of certain kinds of knowledges, learning outcomes (e.g., through software dashboards), power relations, values etc.
In this contribution we are, therefore, interested in the relation of data practices and in/visibilities in the empirical field of education. Based on an overview of literature in critical data studies in education, we derive four kinds of such relations: decontextualisation, in/visibilising of data work, black-boxing and fore- or backgrounding. In what follows, we discuss these four in/visibilities in relation to data practices, how they are studied methodologically, and the possible conceptual underpinnings of such research.
Building on the body of work in critical data studies, we identified the first way in/visibilities are produced in relation to data practices in educational settings as decontextualisation. Decontextualisation addresses how the complexity of the empirical site is being reduced as it gets datafied, e.g., school life is translated into digital data and these data require re-contextualisation to be further used. In this sense, anything that is not translated into digital data can be invisibilised and written out. The practices of recontextualising such digital data, then, might lead to foregrounding only certain aspects of the invisibilised context. For example, Hardy & Lewis (2018) discuss this in relation to school performance data and argue that while digital data might make certain students – in this case, the average-performing ones – more visible to the educators, these students become visible as data points that can be improved rather than as learning subjects. Similar observations have been made by other researchers of data in education, e.g., Bradbury (2019), Selwyn et al. (2022) and Grant (2022). Methodologically, the authors draw on studies of policies defining which aspects of education are important, artefact analysis of documents used in schools and observations of how school actors negotiate the recontextualisation of digital data.
These studies also illuminate how educators’ work becomes more visible to school administrators or education agencies through data practices (Grant, 2022). For other educational actors such as e.g., school secretaries, however, data work creeps into their everyday work tasks but often remains unacknowledged by colleagues, authorities, or technology providers (Zakharova & Jarke, 2022). This in/visibilising of data work addresses how work required to fill digital data with meanings and to translate them between different educational actors (e.g., educational authorities and teachers) is rendered visible and accountable or not (see also Gorur & Dey, 2021). One way to theoretically conceptualise this is through feminist theories, in which in/visibility is tied to power and precarious work remains invisible, as Zakharova and Jarke do in their empirical study. Another way could be to use the topological conceptual approach and digital methods as tools of intervention. For example, van de Oudeweetering et al. (2023) visualise the practices of feedback in MOOCs in university settings, illustrating the careful and tedious work of instructors that departs from but goes beyond the use of digital dashboards for data analytics and is not necessarily visible for all.
Another strain of recent research attends to the complexity of socio-technical arrangements in which mechanisms of invisibility and visibility are made to work. In terms of in/visibility, we suggest to address it as black-boxing. It sheds light on how the design of educational technologies, policies, and politics regulating data practices conceal some data practices from certain actors and configure what is known about digital data and their meanings. For example, taking the visualisation of invisible practices as a point of departure for their conceptual analysis, Jarke and colleagues (2023) use data journeys as an analysis method for exploring data-based school management and administration. At the core of the study is an analysis of different visions of educational software, including the visions of software designers, combined with insights from actors within and outside schools, allowing for the reconstruction of data practices not visible to school actors. Hillman (2023) specifically studies kinds of data tracked and gathered through educational technologies to emphasise the role of policy and governance in producing in/visibilities. She argues, for example, that much of the data about students or even educators and schools is siloed and owned by technology providers, not always accessible even for oversight. While pursuing different goals, what is common to these studies is not particular methods but conceptual and methodological attention to non-human actors – e.g., technologies and data – alongside human actors such as software providers.
Finally, some scholars of datafication in education increasingly turn to studying software design via dashboards, visual narrations, walkthroughs, and other methods of interrogating visual artefacts involved in or produced through data practices (Jarke & Macgilchrist, 2021; Ratner & Ruppert, 2019; Troeger & Bock, 2022). These studies highlight how interfaces of technological systems or data visualisations either allow or constrain certain actions and agencies by making some elements of the interface visually more prominent than others. A typical example would be a traffic light colouring used in a learning management system to indicate different levels of task completion or educational ‘risks’ which induce certain assumptions of urgency or success (Zakharova & Jarke, 2023). Drawing on the work of a technoscience scholar, Lucy Suchman (2007), we suggest addressing this relation between data practices and in/visibilities as fore- and backgrounding. Suchman argues that technologies overall are perceived through staging: fore- and backgrounding of certain aspects and practices that represent technologies in a particular way. “Our task as analysts is then to expand the frame, to metaphorically zoom out to a wider view that at once acknowledges the magic of the effects created while explicating the hidden labors and unruly contingencies that exceed its bounds”, Suchman (2007, p. 283) contends. We find a further conceptual underpinning for this rendering of in/visibility by Galloway (2006, p. 320) and Chun (2005), who describe how software ‘hides’ material human actors and technologies (hardware), whereas the intangible data and their representations (in the form of texts, ranks, or visualisations) appear on the surface. For example, an analysis of the five most broadly used learning management systems (LMS) shows that these LMS render invisible learning and any aspects of students’ lives relevant to learning which take place outside of the digital system (Zakharova & Jarke, 2023). Mostly, only students’ behaviour registered through the LMS, such as time spent in the system, is made visible to teachers via dashboards and data visualisations. Through the lens of fore- and backgrounding, it is possible to address analytically both how visualisations of digital data foreground certain aspects of learning – namely, learning within LMS, and move to the background the human and material aspects of learning that take place outside the LMS.
Concluding thoughts
Taking a closer look at the invisible data practices that are entangled with datafication in education helps us to carve out “ambivalences, tensions and ruptures” (Breiter & Bock, 2023, p. 2) that become visible in-situ of educational data practices. The notion of in/visibility is helpful to connect data with other elements of the socio-technical educational arrangements, which contain these data (in the schooling domain, this can be school reports with data on students) or which consist of them (such as education statistics), which exist in the context of these data (depending on the language skills data students receive special courses or not) or which are there so that the data exists (such as Excel spreadsheets, tables, school information systems etc.). The four kinds of in/visibilities discussed here provide responses to the questions of what and how is being translated into digital data in education and what and how is known about education (or learning) through data practices. At first glance, that approach may seem technology-deterministic. However, what we are aiming to demonstrate here is how data and technologies, together with material artefacts and human actors, mutually constitute themselves and each other’s in/visibilities. At the same time, previous research by one of the authors shows that in some cases, invisibility in relation to data might also be welcomed by educational actors and allows them more agency and flexibility in deciding which data to make visible to whom, why, and when (Zakharova & Jarke, 2022).
Against this background, we discussed here four kinds of relations between data practices and in/visibilities which are tackled differently by educational scholars. They draw on different conceptual approaches, from critical data studies to feminist theories, to topological lens, to design studies and on varying methods – from traditional to new and digital ones like walkthroughs or data journeys – to study how data practices relate to in/visibilities. Therefore, we envision further methodologies that look at the ‘in-between’, the tensions and fractures in which the invisible can be made visible. Among the questions exploring these practices and relations in greater depth are the following: What is or is made invisible in the educational setting? What educational agencies or actors are included in or excluded from the datafication processes? Which technologies, practices, or values are prioritised, and which are marginalised through these processes? Our contribution illustrates how different scholars interested in datafication of education respond to these questions.
Drawing on our brief scoping of this research, we suggest that future studies on the datafication of education should foster research that draws on software studies to investigate the role of software (Manovich, 2013), critical data studies to engage with implications of massive datasets in the educational domain (Boyd & Crawford, 2012; Jones & McCoy, 2019; Kitchin, 2014), and studies on the materiality of data to define the physical limitations of data practices (e.g., Pink et al., 2018). The proposed connection of different approaches aims to turn the focus from the context of invisible data use (Breiter et al., 2018; Hutchinson, 2016) to the context in which data is produced and made in/visible by different actors. While our concern here has not been on the role of researchers in in/visibilising certain aspects of educational practice, we acknowledge alongside other scholars like Neumayer et al. (2021) and Zakharova (2022) that research methods also have a performative role. Hence, this paper contributes to the discussion of education research methods in times of deep mediatisation, digital transformation and datafication, specifically addressing research on the human and beyond-human components of invisible data practices (Bock et al., 2023; Breiter & Hepp, 2018; Fahimi, in press; Rieder & Röhle, 2017). First promising examples of empirical studies could be outlined in this scoping piece, which will hopefully be followed by further exciting approaches and in-depth discussions in the future.
References
Bock, A., Breiter, A., Hartong, S., Jarke, J., Jornitz, S., Lange, A., & Macgilchrist, F. (Eds.). (2023). Die datafizierte Schule. Springer VS.
https://doi.org/10.1007/978-3-658-38651-1
Bornakke, T., & Due, B. L. (2018). Big–thick blending: A method for mixing analytical insights from big and thick data sources. Big Data & Society, 5(1).
https://doi.org/10.1177/2053951718765026
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
https://doi.org/10.1080/1369118X.2012.678878
Bradbury, A. (2019). Datafied at four: The role of data in the ‘schoolification’ of early childhood education in England. Learning, Media and Technology, 44(1), 7–21.
https://doi.org/10.1080/17439884.2018.1511577
Breiter, A., & Bock, A. (2023). Datafizierte Gesellschaft | Bildung | Schule. In A. Bock, A. Breiter, S. Hartong, J. Jarke, S. Jornitz, A. Lange, & F. Macgilchrist (Eds.), Die datafizierte Schule (pp. 1–35). Springer VS.
https://doi.org/10.1007/978-3-658-38651-1_1
Breiter, A., & Hepp, A. (2018). Die Komplexität der Datafizierung: Zur Herausforderung, digitale Spuren in ihrem Kontext zu analysieren. In C. Katzenbach, C. Pentzold, S. Kannengießer, M. Adolf, & M. Taddicken (Eds.), Neue Komplexitäten für Kommunikationsforschung und Medienanalyse: Analytische Zugänge und empirische Studien (pp. 27–48). Digital Communication Research, Vol. 4.
https://doi.org/10.17174/DCR.V4.2
Breiter, A., Hepp, A., & Friemel, T. N. (2018). Digital traces in context: An introduction. International Journal of Communication, 12, 439–449.
https://doi.org/10.5167/uzh-148589
Chun, W. H. (2005). On software, or the persistence of visual knowledge. Grey Room, 18, 26–51.
https://doi.org/10.1162/1526381043320741
Decuypere, M., Alirezabeigi, S., Grimaldi, E., Hartong, S., Kiesewetter, S., Landri, P., Masschelein, J., Piattoeva, N., Ratner, H., Simons, M., Vanermen, L., & Broeck, P. V. (2023). Laws of edu-automation? Three different approaches to deal with processes of automation and Artificial Intelligence in the field of education. Postdigital Science and Education, 5(1), 44–55.
https://doi.org/10.1007/s42438-022-00360-x
Fahimi, M., Falk, P., Kinder-Kurlanda, K., Light, E., McGeachey, E., Gray, J. W. Y., Medina Perea, I., Poechhacker, N., Poirier, L., Röhle, T., Sharon, T., Stevens, M., van Gastel, B., White, Q., & Zakharova, I. (in press). In/visibilities in data studies: Methods, tools and interventions. In J. Jarke & J. Bates (Eds.), Dialogues in data power. Bristol University Press.
Fenwick, T., & Landri, P. (2012). Materialities, textures and pedagogies: Socio-material assemblages in education. Pedagogy, Culture & Society, 20(1), 1–7.
https://doi.org/10.1080/14681366.2012.649421
Galloway, A. R. (2006). Language wants to be overlooked: On software and ideology. Journal of Visual Culture, 5(3), 315–331.
https://doi.org/10.1177/1470412906070519
Gorur, R., & Dey, J. (2021). Making the user friendly: The ontological politics of digital data platforms. Critical Studies in Education, 62(1), 67–81.
https://doi.org/10.1080/17508487.2020.1727544
Grant, L. (2022). Reconfiguring education through data: How data practices reconfigure teacher professionalism and curriculum. In A. Hepp, J. Jarke, & L. Kramp (Eds.), New perspectives in critical data studies (pp. 217–241). Springer International.
https://doi.org/10.1007/978-3-030-96180-0_10
Hardy, I., & Lewis, S. (2018). Visibility, invisibility, and visualisation: The danger of school performance data. Pedagogy, Culture & Society, 26(2), 233–248.
https://doi.org/10.1080/14681366.2017.1380073
Hartong, S., Förschler, A., & Dabisch, V. (2021). Data infrastructures and the (ambivalent) effects of rising data interoperability: Insights from Germany. In C. Wyatt-Smith, B. Lingard, & E. Heck (Eds.), Digital disruption in teaching and testing: Assessments, big data, and the transformation of schooling (pp. 136–151). Routledge.
https://doi.org/10.4324/9781003045793-8
Hillman, V. (2023). Bringing in the technological, ethical, educational and social-structural for a new education data governance. Learning, Media and Technology, 48(1), 122–137.
https://doi.org/10.1080/17439884.2022.2052313
Hutchinson, J. (2016). An introduction to digital media research methods: How to research and the implications of new media data. Communication Research and Practice, 2(1), 1–6.
https://doi.org/10.1080/22041451.2016.1155307
Jarke, J., & Breiter, A. (2019). Editorial: The datafication of education. Learning, Media and Technology, 44(1), 1–6.
https://doi.org/10.1080/17439884.2019.1573833
Jarke, J., Hartong, S., Raabe, T., Dabisch, V., Breiter, A., Lange, A., & Zakharova, I. (2023). Zur Erfassung und Modellierung der „Hinterbühne“ von Datenflüssen: Das Beispiel Unterrichtsausfall. In A. Bock, A. Breiter, S. Hartong, J. Jarke, S. Jornitz, A. Lange, & F. Macgilchrist (Eds.), Die datafizierte Schule (pp. 61–91). Springer VS.
https://doi.org/10.1007/978-3-658-38651-1_3
Jarke, J., & Macgilchrist, F. (2021). Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education. Big Data & Society, 8(1).
https://doi.org/10.1177/20539517211025561
Jones, K. M. L., & McCoy, C. (2019). Reconsidering data in learning analytics: Opportunities for critical research using a documentation studies framework. Learning, Media and Technology, 44(1), 52–63.
https://doi.org/10.1080/17439884.2018.1556216
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences. Sage.
Kitchin, R., & Lauriault, T. P. (2014). Towards critical data studies: Charting and unpacking data assemblages and their work. The Programmable City Working Paper 2. The Programmable City.
Krein, U., & Schiefner-Rohs, M. (2021). Data in schools: (Changing) Practices and blind spots at a glance. Frontiers in Education, 6.
https://doi.org/10.3389/feduc.2021.672666
Law, J. (2004). After method: Mess in social science research. Routledge.
Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77–86.
https://doi.org/10.1080/17439884.2018.1556217
Manovich, L. (2013). Software takes command: Extending the language of new media. Bloomsbury.
Manovich, L. (2017). Cultural analytics, social computing and digital humanities. In M. T. Schäfer, & K. Van Es (Eds.), The datafied society: Studying culture through data (pp. 55–68). Amsterdam University Press.
Neumayer, C., Rossi, L., & Struthers, D. M. (2021). Invisible data: A framework for understanding visibility processes in social media data. Social Media + Society, 7(1).
https://doi.org/10.1177/2056305120984472
Oosterhoff, A., Thompson, T. L., Oenema-Mostert, I., & Minnaert, A. (2023). En/countering the doings of standards in early childhood education: Drawing on actor-network theory to trace enactments of and resistances to emerging sociomaterial policy assemblages. Journal of Education Policy, 1–22.
https://doi.org/10.1080/02680939.2022.2161639
Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging world. Big Data & Society, 5(1).
https://doi.org/10.1177/2053951717753228
Ratner, H., & Ruppert, E. (2019). Producing and projecting data: Aesthetic practices of government data portals. Big Data & Society, 6(2).
https://doi.org/10.1177/2053951719853316
Rieder, B., & Röhle, T. (2017). Digital methods. From challenges to Bildung. In M. T. Schäfer, & K. Van Es (Eds.), The datafied society: Studying culture through data. Amsterdam University Press.
Rogers, R. (2017). Foundations of Digital Methods: Query Design. In M. T. Schäfer, & K. Van Es (Eds.), The datafied society: Studying culture through data (pp. 75–94). Amsterdam University Press.
Selwyn, N., & Gašević, D. (2020). The datafication of higher education: Discussing the promises and problems. Teaching in Higher Education, 25(4), 527–540.
https://doi.org/10.1080/13562517.2019.1689388
Selwyn, N., Pangrazio, L., & Cumbo, B. (2022). Knowing the (datafied) student: The production of the student subject through school data. British Journal of Educational Studies, 70(3), 345–361.
https://doi.org/10.1080/00071005.2021.1925085
Suchman, L. A. (2007). Human-machine reconfigurations: Plans and situated actions (2nd ed). Cambridge University Press.
Troeger, J., & Bock, A. (2022). The sociotechnical walkthrough – a methodological approach for platform studies. Studies in Communication Sciences, 22(1), 43–52.
https://doi.org/10.24434/j.scoms.2022.01.3064
Troeger, J., Lüpkes, J., & Bock, A. (2022). In software we (do not) trust. In E. Fuchs, & M. Otto (Eds.), In Education We Trust? Vol. Band 153 (pp. 255–264). V&R unipress.
https://doi.org/10.14220/9783737015097.255
Van De Oudeweetering, K., Knox, J., & Decuypere, M. (2023). Problematizing feedback loops: ‘On’, ‘with’, and ‘beyond’ analytics dashboards in MOOCs. Learning, Media and Technology, 1–16.
https://doi.org/10.1080/17439884.2023.2264188
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
https://doi.org/10.24908/ss.v12i2.4776
Yu, J., & Couldry, N. (2022). Education as a domain of natural data extraction: Analysing corporate discourse about educational tracking. Information, Communication & Society, 25(1), 127–144.
https://doi.org/10.1080/1369118X.2020.1764604
Zakharova, I. (2022). Understanding data studies — A conceptual and methodological inquiry into research on datafication. Universität Bremen.
https://doi.org/10.26092/elib/1675
Zakharova, I., & Jarke, J. (2022). Educational technologies as matters of care. Learning, Media and Technology, 47(1), 95–108.
https://doi.org/10.1080/17439884.2021.2018605
Zakharova, I., & Jarke, J. (2023). Do predictive analytics dream of risk-free education? The politics of risk mitigation. Postdigital Science and Education.
https://doi.org/10.1007/s42438-023-00411-x
Recommended Citation
Zakharova, I., & Bock, A. (2023). Researching in/visibilities and data practices in education. On Education. Journal for Research and Debate, 6(18).
https://doi.org/10.17899/on_ed.2023.18.4
Do you want to comment on this article? Please send your reply to editors@oneducation.net. Replies will be processed like invited contributions. This means they will be assessed according to standard criteria of quality, relevance, and civility. Please make sure to follow editorial policies and formatting guidelines.
