This term paper examines how digital education platforms such as Microsoft for Education reproduce and reinforce educational inequality in school contexts. In the course of the platformisation of education, digital learning environments have become central infrastructures of school organisation, structuring learning processes, assessment practices and social interactions through data-driven mechanisms. The paper draws on Bourdieu’s theory of habitus and capital, the concept of digital habitus, platform capitalism and postdigitality. The analysis is based on recent literature on digital infrastructure, usage and competences in Germany, as well as research on Microsoft for Education. The results show that, despite its formal cost-free access, the learning platform relies on unevenly distributed forms of capital. Access and effective use require material resources, digital competences and self-regulated learning skills, which are particularly limited among disadvantaged students. Moreover, algorithmic classification and data-based performance assessment contribute to the legitimation, stabilisation and reinforcement of existing educational inequalities.
Table of Content
List ofAids Used
1 Introduction
2 State of Research
3 TheoreticalFramework
3.1 Bourdieu’s Theory of Habitus and Capital
3.2 Digitaler Habitus nach Biermann
3.3 Platform Capitalism
3.4 Postdigitality
4 Analysis of Current Literature
4.1 Findings on Digital Access in German Schools
4.1.1 Equipment
4.1.2 Frequency of Use
4.1.3 Learning Resources
4.1.4 Digital Competences
4.2 Findings on Microsoft Education
4.2.1 Learning and Organisational Platform
4.2.2 Algorithmic Structuring
4.2.3 Cost-Free Access as a Platform Strategy
4.3 Answer tot he Research Question
5 Discussion
6 Conclusion and Educational Implications
7 Bibliography
Abstract
Die vorliegende Hausarbeit untersucht, wie digitale Bildungsplattformen wie Microsoft for Education Bildungsungleichheit im schulischen Kontext reproduzieren und verstärken. Im Zuge der Plattformisierung von Bildung werden digitale Lernumgebungen zu zentralen Infrastrukturen schulischer Organisation, die Lernprozesse, Leistungs-bewertungen und soziale Interaktionen datenbasiert strukturieren. Die Arbeit stützt sich auf Bourdieus Habitus- und Kapitaltheorie, den Ansatz des digitalen Habitus, des PlattformKapitalismus und der Postdigitalität. Die Analyse basiert auf aktueller Literatur zur digitalen Ausstattung, Nutzung und Kompetenz in Deutschland sowie auf Forschung zu Microsoft for Education. Die Resultate weisen darauf hin, dass die Lernplattform trotz formaler Kostenfreiheit an ungleich verteilte Kapitalformen anschließt. Zugang und Nutzung setzen materielle Ressourcen, digitale Kompetenzen und selbstregulative Lernfähigkeiten voraus, die insbesondere bei benachteiligten Schülerinnen eingeschränkt vorhanden sind. Algorithmische Klassifikationen und datenbasierte Leistungsbewertung tragen zudem zur Legitimation, Stabilisierung und Verstärkung bestehender Bildungsungleichheiten bei.
Schlagwörter: Bildungsungleichheit; digitale Bildungsplattformen; Habitus; PlattformKapitalismus; Datafizierung; Microsoftfor Education; Postdigitalität
Abstract
This term paper examines how digital education platforms such as Microsoft for Education reproduce and reinforce educational inequality in school contexts. In the course of the platformisation of education, digital learning environments have become central infrastructures of school organisation, structuring learning processes, assessment practices and social interactions through data-driven mechanisms. The paper draws on Bourdieu’s theory of habitus and capital, the concept of digital habitus, platform capitalism and postdigitality. The analysis is based on recent literature on digital infrastructure, usage and competences in Germany, as well as research on Microsoft for Education. The results show that, despite its formal cost-free access, the learning platform relies on unevenly distributed forms of capital. Access and effective use require material resources, digital competences and self-regulated learning skills, which are particularly limited among disadvantaged students. Moreover, algorithmic classification and data-based performance assessment contribute to the legitimation, stabilisation and reinforcement of existing educational inequalities.
Keywords: educational inequality; digital education platforms; habitus; platform capitalism; datafication; Microsoftfor Education; postdigitality
List of Aids Used
Illustrations are not included in the reading sample
1 Introduction
Digital platforms and data-driven learning environments are no longer merely supplementary tools of educational practice but have become constitutive components of pedagogical organisation. In a post-digital condition, where digital technologies are seamlessly integrated into school routines as a matter of course (Jandric & Knox, 2022, p. 783), learning processes, performance assessments, and social interactions are increasingly captured, analysed, and structured in the form of data (Erstad et al., 2023, pp. 2229). This transformation affects not only didactic methods but fundamentally changes how education is conceptualised and implemented (Knox, 2019, pp. 363-365). Learners increasingly appear as data profiles whose behaviour can be compared, predicted, and optimised. Efficiency, comparability, and optimisation of learning processes play a central role, and the advancing datafication1 of pedagogical practice contributes to understanding learning as a measurable, controllable, and predictable process (Leineweber, 2023, pp. 241-242).
The platformisation2 of education, in the context of platform capitalism, is also associated with considerable risks. Digital education platforms do not act merely as neutral infrastructures but as private-sector actors whose business models are based on the systematic extraction, processing, and commercial use of data (Srnicek, 2018, p. 45). Education is thus increasingly embedded within the economic logics of large technology corporations and becomes dependent on their technologies (Redecker, 2023, pp. 8587).
A concrete example of this development is Microsoft for Education. This is not a single application but a comprehensive digital ecosystem that includes, among other things, Microsoft Teams, Microsoft 365, OneDrive, learning analytics functions, and Al-based evaluation tools. The system structures school communication, task organisation, performance feedback, and learning processes in an integrated form, continuously generating data traces about the behaviour, interactions, and achievements of pupils (Microsoft, n.d. a). Critical voices have pointed out that this data collection allows far-reaching inferences about learners, while provider responsibilities concerning data use and data protection are only regulated in a limited and non-transparent manner (Holland-Letz, 2025; Kuketz, 2025). From an educational science perspective, this also raises the question of the social consequences of this development, since education systems are sites for the reproduction of inequality. Building on Pierre Bourdieu, school success cannot be understood solely as the result of individual performance, but must also be seen as an effect of social origin, reflecting unequally distributed forms of capital—particularly cultural, social, and economic capital (Bourdieu, 2025, pp. 143-161; Bourdieu et al., 1971, pp. 19-45).
Digital education platforms intervene in these reproduction mechanisms by presupposing and valuing specific forms of digital competence, access to technology, self-organisation, and family support. However, these prerequisites are unevenly distributed across society, meaning that digital learning environments may not only reflect existing inequalities but potentially amplify them (Zhao, 2023, p. 641). Particularly concerning is the fact that algorithmic bias does not merely passively reflect social inequalities but actively reproduces them through the use of historical datasets (Baker & Hawn, 2022, pp. 10581059). This can result in certain groups being denied access to opportunities or resources or in resources being distributed unfairly, further deepening the existing digital and social divide (p. 1055).
Against this backdrop, the present paper examines how digital platforms such as Microsoft for Education exacerbate educational inequality in the school context and how this can be explained using Bourdieu’s theory of habitus and capital.
To address this research question, the paper is divided into six chapters. Following the introduction, there is an overview of the current state of research on digital education platforms, datafication, learning analytics, and educational inequality. Chapter 3 presents the theoretical framework, and Chapter 4 evaluates current studies on digital education platforms and inequality while answering the research question. Chapter 5 discusses the findings, and Chapter 6 draws conclusions with educational implications.
2 State of Research
Academic engagement with digital education platforms has intensified significantly since the early 2020s. A central strand of research focuses on Microsoft for Education as a digital teaching and learning infrastructure. The platform has been investigated both from a functional-didactic perspective (Al-Qora’n et al., 2022, p. 1) and regarding its impact on students’ self-regulated learning processes (Al-Shboul, 2024, p. 4). Empirical findings generally attest to Microsoft for Education’s satisfactory functionality and effectiveness, as reflected in increased engagement, higher participation, and improved academic performance (Hazaymeh, 2025, pp. 6-7). Complementary studies on acceptance and perception show that teachers and learners largely regard Microsoft Teams as a practical and usable everyday tool (Elihami & Lobo, 2024, p. 248; Mitra & Wadegaonkar, 2025, pp. 538-539). At the same time, researchers emphasise that teachers’ and students’ expectations of learning analytics systems play a decisive role in their successful implementation (Fritz et al., 2024, pp. 13-14).
Alongside these utility-based studies, a critical-theoretical research perspective has emerged in parallel, analysing digital education platforms in the context of datafication, power, and economy. The edited volume Datafizierung (in) der Bildung conceptualises datafication not as a purely technical transformation but as a process deeply embedded in pedagogical practices, institutional logics, and social power relations. The volume highlights that the data-driven measurement and classification of learners do not occur in a neutral vacuum but are connected to the economic interests of major technology corporations. As a result, these dynamics can generate new forms of pedagogical control and social differentiation (Schiefner-Rohs et al., 2024, pp. 11-15).
This perspective is further refined by a comprehensive literature review from the Eras- mus+ project Agile-EDU. In this work, datafication is described as part of a complex, education-related data ecosystem involving learners, teachers, educational institutions, and commercial EdTech and BigTech actors with differing interests in educational data. The review particularly stresses that this constellation produces new challenges for inclusion and educational equity. Unequal access to digital devices, educational data, and data- and Al-related competences fosters the emergence of new digital divides, which not only overlap with but may also exacerbate existing social inequalities. Consequently, the systematic promotion of multiliteracies—particularly data, Al, and algorithmic literacy—among learners as well as within teacher education and professional development is identified as an essential prerequisite for a responsible approach to datafication (Erstad et al., 2023, pp. 33-35).
Another literature analysis demonstrates that, contrary to widespread expectations, digital learning resources have so far made no substantial contribution to reducing educational inequality. Although access to digital learning opportunities has improved in general, learners from resource-rich backgrounds benefit most, while socioeconomically disadvantaged groups continue to be hindered by limited Internet access, lower parental support, and weaker digital and self-regulatory skills. Digital learning resources therefore do not exert their influence independently of social conditions but rather reproduce existing inequalities by building upon unequally distributed forms of cultural and digital capital (Zhao, 2023, pp. 636-640).
A review of empirical literature on algorithmic bias in educational contexts further shows that algorithms within data-driven learning environments may not represent social inequalities neutrally but can systematically reproduce them. Algorithmic bias, accordingly, does not stem solely from technical models but primarily from the selection and operationalisation of variables, as well as the use of historical datasets in which existing inequalities are already inscribed. Marginalised groups—such as those differentiated along socioeconomic, ethnic, or gendered lines—are particularly affected, as algorithmic predictions and assessments tend to be more error-prone or disadvantageous for these groups (Baker&Hawn, 2022, pp. 1073-1075).
The current state of research makes it evident that a theoretically grounded examination is required to understand how digital platforms such as Microsoft for Education not only reflect but actively co-produce educational inequality. It is at this juncture that the present study positions itself.
3 Theoretical Framework
This chapter outlines the theoretical foundations of the present study. It delineates Bourdieu’s theory of habitus and capital as well as Biermann’s elaborations on the “digital habitus”. Additionally, Srnicek’s notion of platform capitalism and Jandric et al.’s concept of postdigitality are introduced.
3.1 Bourdieu’s Theory of Habitus and Capital
The analysis of inequality within the educational context presupposes an understanding that societal institutions such as the school are by no means neutral sites of performance assessment. Already in their seminal work Illusion der Chancengleichheit, Bourdieu and Passeron observed that the education system tends to reinterpret social privilege as individual merit and thus legitimises rather than reduces existing class-based differences (Bourdieu et al., 1971, p. 45). To deconstruct this process, however, a differentiated examination of the resources available to learners is necessary. Bourdieu defines capital as accumulated labour which exists either in material form or in an internalised embodied form, enabling actors to appropriate social energy (Bourdieu, 2012, p. 229). He distinguishes between economic, cultural, and social capital. Economic capital is readily convertible into money and formalised through property rights; cultural capital can, under certain conditions, be transformed into economic capital and institutionalised as educational credentials; while social capital—resources embedded in relationships—may also be converted into economic capital and formalised through social recognition or titles (p. 231).
According to Bourdieu, cultural capital exists in three states: as objectified cultural capital, for example in the form of books or data; as institutionalised cultural capital, for example in the form of academic titles; and as embodied cultural capital in the form of education. The latter is bound to the body as a lasting disposition. Its acquisition takes time and occurs through internalisation, thus becoming part ofthe person—namely, their habitus (pp. 231-233). This serves as the link between capital and practical conduct of life: according to Bourdieu, individuals occupy different positions within the social space, which in turn correspond to particular living conditions. These conditions shape and influence people’s patterns of thinking, perception, and action, thereby generating specific forms of habitus (Bourdieu, 1987, p. 98). For instance, members of the working class speak differently from academics—they laugh at different jokes and enjoy different kinds of food, art, and music. According to Bourdieu, habitus represents a general disposition toward the world that shapes consistent patterns of thought and action (Bourdieu et al., 2015, p. 31), while simultaneously defining the limits of what individuals perceive as possible or appropriate (p. 33).
Capital, habitus, and position within the social space mutually influence one another. Additionally, temporal factors and the specific forces of the field in which the social space is situated must be considered. In the field of economy, for example, different logics apply than in the field of art or education. Yet all fields share one feature: they are arenas of power struggles (Bourdieu, 2025, pp. 193-196). Bourdieu attributes unequal educational opportunities to the privileging and exclusion of certain classes within educational institutions—forms of power struggle. Here, social origin plays a central role (Bourdieu et al., 1971, pp. 19-31). By disregarding class-specific differences and instead assuming that all learners start from equal conditions, the education system obscures the effects of higher (inherited) capital among the privileged and reinterprets them as personal merit. The justification of success or failure in terms of “talent” or “fate” is underpinned by an ideology that serves as a framework for interpretation and evaluation—an ideology that is also adopted by the non-privileged (pp. 82-86). This reflects symbolic violence, a subtle form of domination that operates through everyday meanings and norms, shaping acceptance and internalisation of social hierarchies without appearing coercive (Moebius &Wetterer, 2011,pp.1-2).Symbolic violence, in turn, is an expression of symbolic power3, which may serve as a “weapon” in the struggle for symbolic domination (K. Schmidt, 2022, p. 110).
Against the backdrop of the increasing permeation of social practice by digital technologies, the question now arises as to how these inequality-relevant mechanisms can be theoretically conceptualised under digital conditions.
3.2 Digitaler Habitus nach Biermann
Biermann develops the concept of the digital habitus as a theoretical extension of Bourdieu’s framework. It is therefore not an entirely new phenomenon but a historically situated manifestation of the habitus that emerges under conditions of digitality and algorith- micity. Biermann builds on the notion of a medial habitus as a system of enduring media-related dispositions that form the basis for media practices (Biermann, 2023, p. 3). In contrast to earlier media configurations, the digital habitus, according to Biermann, is characterised by the growing involvement of algorithmic systems in the structuring of social reality. This alters the relationship between subject and structure: practices no longer arise solely from embodied dispositions but through the interplay with algorithmically governed environments (Biermann, 2023, pp. 8-10). Algorithms thus no longer function merely as tools but assume ordering, selecting, and evaluative roles, thereby becoming fundamentally enmeshed with human actions (pp. 14-16). Consequently, they increasingly shape social and cultural processes (p. 17). Decisions are increasingly made for us and complexity is simplified to help us navigate the world—yet the crucial question is what part of reality these processes allow us to see (Biermann, 2023, p. 18).
At the same time, inequalities are not resolved through the use of algorithms but persist and are, moreover, intensified (p. 24). Biermann concludes that habitus and field are exposed to the effects of digitalisation with varying intensity, which ultimately influences social practice itself.
The functioning of algorithms, in turn, must be analysed in light of the economic interests of the companies that develop and deploy platforms and algorithms.
3.3 Platform Capitalism
To understand the functioning and underlying logic of digital (educational) platforms, it is first necessary to situate them within the broader economic transformations of the twenty-first century. According to Nick Srnicek, capitalism underwent a fundamental shift following the 2008 financial crisis. With the growing availability of low-cost digital technologies, new business models emerged that rely on the systematic collection and exploitation of data. At the core of this “platform capitalism” lies the extraction, processing, and utilisation of data, which functions as a new raw material (Srnicek, 2018, p. 41). It is essential to distinguish between data and knowledge: while data merely indicate “that something has happened”, knowledge provides explanations for “why it has happened” (p. 42). For data to become economically valuable, however, they must first be cleansed and converted into standardised formats (p. 41).
In this context, platforms do not act as neutral intermediaries but as “new, powerful business forms” that systematically collect, analyse, and monopolise data (p. 45). Srnicek defines platforms as digital infrastructures that connect different user groups and function as intermediating entities (p. 46). They strategically position themselves between actors as sites of social and economic activity, enabling them to capture and evaluate these activities comprehensively (p. 47). A key feature of this process is the so-called “network effect”: the more people use a platform, the more valuable it becomes for everyone else, producing self-reinforcing growth cycles and fostering the emergence of monopolies. Furthermore, these actors employ strategies of “cross-subsidisation” to consolidate their market position by offering certain services free of charge, while offsetting the resulting losses through other business sectors (pp. 48-49).
Crucially, platforms do not constitute value-neutral spaces. Through their rules and algorithms, they embody specific forms of politics that determine which interactions are rendered visible and which are marginalised (p. 49). This entanglement of technical infrastructure and normative governance makes platforms central instruments of data collection and control (p. 51).
Platformisation has now become so profound that the boundary between the digital and the analogue is increasingly dissolving. The technological permeation of all areas of life is therefore no longer an exception but the “new” normality—a phenomenon accounted for in the concept of postdigitality.
3.4 Postdigitality
The reflections on platform capitalism make it apparent that the technological penetration of educational spaces extends far beyond the mere use of software. The ubiquity of the digital is aptly captured by Nicholas Negroponte, who predicted that it would become perceptible only through its absence—like air or drinking water—rather than through its presence (Negroponte, 1998). This idea encapsulates the essence of the concept of postdigitality, which has increasingly been discussed in educational research since 2019. It describes a condition in which digital technologies no longer exist as separate, “virtual” realms apart from “natural” human and social life (Jandric etal., 2018, p. 893). The prefix “post-” thus does not signify the end of the digital era but rather its complete and taken- for-granted integration into everyday life.
Jandric et al. characterise postdigitality as “[...] messy; unpredictable; digital and analog; technological and non-technological; biological and informational” (p. 895). At the same time, the concept operates as a critical response to the utopian visions of the early Internet, addressing the massive concentration of power within the Big Tech economy as well as the attendant processes of commercialisation, social inequality, and ecological externalities (p. 895).
In educational processes, postdigitality reveals itself in the fact that the digital no longer functions merely as a supportive tool but is fundamentally intertwined with pedagogical practices (Jandric&Knox,2022,p.785). Within this configuration, education operates under the considerable influence of the data economy, platform logics, and private-sector interests—making critical reflection on data politics and social inequalities indispensable (p. 786). Consequently, Jandric and Knox call for a “postdigital ecopedagogy” that places justice and responsibility at its centre and conceives education as part of a complex world in which human beings, technology, and nature form an inseparable unity (pp. 788-791).
With these theoretical foundations—from Bourdieu’s theory of habitus and capital, through the digital habitus, to platform capitalism and postdigitality—it becomes possible to analyse current empirical findings on digital platforms such as Microsoft for Education.
4 Analysis of Current Literature
This chapter analyses, on the basis of current empirical literature, how digital platforms such as Microsoft for Education reproduce and reinforce educational inequality within the school context. Section 1.1 examines general access barriers in German schools, section 1.2 discusses specific platform mechanisms, and section 1.3 answers the research question through theoretical synthesis.
4.1 Findings on Digital Access in German Schools
The following section examines the fundamental prerequisites for the successful use of digital platforms. Drawing on various studies, it analyses structural inequalities in equipment, usage, and skills that reproduce economic and cultural capital.
4.1.1 Equipment
A central finding in research on digital education platforms is that their use does not occur without preconditions but depends on specific material and infrastructural factors. Digital learning environments such as Microsoftfor Education require continuous access to suitable devices, stable internet connections, and physical learning environments that allow for focused work. Inequalities may arise in one or more of the following areas: individual access, institutional access, logistical conditions, or political-economic frameworks (Barragan Moreno & Lozano Galindo, 2025, pp. 3-5). These prerequisites are closely linked to the unequal distribution of economic capital at federal, state, and municipal levels—as well as to the varying financial capacities of educational institutions and families of origin (Wang, 2025, pp. 2-4).
The ICILS 2023 study revealed significant differences in the digital equipment of German schools compared with international standards—whether in terms of implementation strategies, the availability of innovative digital applications such as adaptive learning systems, or the use of pedagogical virtual and augmented reality tools (Niemann et al., 2024, p. 250). Germany also lags behind in the provision of digital textbooks. The results show that only a small proportion of pupils attend schools in which at least 76 percent of eighth-graders have access to mobile devices. Of these, merely 7.9 percent were allowed to use the devices at home. The authors refer to the PISA 2022 findings, which demonstrated a significant correlation between the availability of home IT resources and parents’ social background (Lewalteretal., 2023, p. 245). Although 94 percent of students reported having access to a computer at home, nearly one in ten pupils attending non-grammar schools lacked an adequate digital device in their household for school-related learning (p.244). Learning software was accessible to 69 percent of grammar-school pupils but only to 56 percent of pupils in other types of schools (p. 245). Beyond the mere availability of digital technologies, it is also decisive whether and how intensively these are actually used.
4.1.2 Frequency of Use
The frequency of use in teaching is a key indicator of how deeply digital technologies are embedded in educational processes. Findings from the ICILS 2023 study show that the use of digital media in classrooms has increased considerably in recent years. Nearly70percent of teachers in Germany reported using digital media at least once a day in their lessons. This represents a substantial rise compared with earlier survey cycles and reflects a clear trend toward the routine integration of digital tools into everyday teaching—placing Germany’s value above that of many other participating countries (Eickelmann et al., 2024, p. 39).
Despite this increase, usage frequency displays differentiated patterns that point to ongoing structural and professional challenges. International comparisons reveal that in countries with similar or better technical infrastructure, digital media are used more frequently on a daily basis than in Germany (p. 30). Moreover, the rate of daily use across subjects in Germany is 12.5 percentage points below the international average of57 percent (p. 32). However, the PISA2022results suggest that pupils’ acquisition of digital competence depends not only on how often digital tools are used but also on how they are meaningfully engaged with (Lewalteretal., 2023, pp. 254-255).
4.1.3 LearningResources
As part of the PISA2022study, German teachers were surveyed about how often and which digital tools they use in the classroom. Most teachers reported primarily using textprocessing and presentation software, with little engagement with other digital resources. A closer look showed that more than half of the teachers used learning software, practice programmes, digital games, online information sources such as wikis, and interactive materials like quizzes only for a few lessons throughout the school year (p. 247). Concept-mapping software, e-portfolios, and data-recording or monitoring tools were rarely used—only 20 percent of teachers reported employing them, while the remainder did not use them at all. Learning software and interactive digital materials were likewise used only to a minimal extent (p. 247). According to the PISA2022study, computer clubs (Computer-AGs) existed in 60 percent of schools, with grammar schools showing slightly higher rates than non-grammar schools (p. 248). The most commonly used devices among pupils were smartphones, followed by tablets. However, only 11 percent of pupils stated that they used learning software or tools daily in class, whereas38percent said they almost never used them. A similar pattern was observed for learning management systems (p. 249): only17 percent of pupils reported using one daily. In terms of usage types, Germany ranked significantly below the OECD average. Across almost all categories, however, grammar schools outperformed other school types (p. 251).
4.1.4 DigitalCompetences
Regarding the digital competences of eighth-grade pupils in Germany, the ICILS2023study provides important insights. Although students’ scores in computer- and information-related skills, averaging502points, are above the international mean, the data also indicate a marked decline compared with ICILS 2013and 2018. This suggests ongoing deficiencies in competence development (Eickel- mannetal.,2024,p.13). More than40percent of the respondents demonstrated only rudimentary digital skills—a paradox in light of the increasing integration of digital learning tools (p. 17).
Pupils attending grammar schools outperformed those from other school types by an average of87 points, with this gap widening over the ten-year period (p. 18). The findings reveal striking educational inequalities in the acquisition of digital competences in Germany, particularly to the disadvantage of students with migration backgrounds, non-German family languages, and socially disadvantaged origins. Only 50percent reached even the two lowest competence levels, and girls—despite a small overall advantage over boys—scored particularly low on average (p. 19).
4.2 Findings on Microsoft Education
The following chapter provides a closer examination of MicrosoftforEducation. Based on the company’s own statements and empirical studies, it explores the platform’s functions, algorithmic control mechanisms, and the supposed cost-free nature of the system.
4.2.1 Learning and Organisational Platform
In educational practice, MicrosoftforEducation is not used as an isolated digital tool but as a comprehensive infrastructure for organising teaching, communication, and performance feedback. The ecosystem integrates applications such as MicrosoftTeams, OneDrive, Word, PowerPoint, and learning analytics functionalities into a central platform, thereby enabling the continuous digital facilitation of learning processes in schools. From a functional perspective, this integration is said to simplify administrative processes, increase transparency regarding learning progress, and enhance collaboration between teachers and students (Microsoft, n.d. a).
Empirical studies generally attest to the platform’s high practical usability. In particular, MicrosoftTeams is regarded by both teachers and learners as a practical instrument for communication and feedback (Elihami & Lobo, 2024, pp. 246-248). Teachers reportedly manage lesson planning, task coordination, and classroom organisation well using the application, though they encounter challenges when employing interactive tools and online assessments. Students have assessed the platform’s usability and multimedia content positively, but they point to a lack of personal interaction and to evaluative and feedback strategies within MicrosoftTeams being inadequate (Mitra&Wadegaonkar, 2025, pp. 536-539).
A utility-oriented study highlights positive effects on participation, engagement, and perceived learning support when the platform is used regularly and in a structured manner (Hazaymeh, 2025, pp. 6-7).
Concerning attitudes and expectations toward learning analytics among students and lecturers at a German university, one analysis showed that both groups held generally positive views and anticipated potential benefits from data-driven learning analyses—for example, in supporting teaching-learning processes and improving study organisation. However, discrepancies emerged between idealised expectations and realistic usage scenarios. Moreover, variations were observed across groups and disciplines, suggesting that experience, disciplinary culture, and conceptions of one’s professional role strongly influence perceptions oflearning analytics (Fritzetal.,2024, pp. 12-13).
An often implicit feature of digital education platforms such as MicrosoftforEducation is the strong emphasis on self-regulated learning. A study conducted at a Jordanian university examined the impact of MicrosoftTeams on students’ academic performance and self-learning competences. The findings revealed significant improvements among students taught via MicrosoftTeams compared with a control group—both in test performance and in self-regulatory learning skills (Al-Shboul, 2024, pp. 19-20).
4.2.2 Algorithmic Structuring
According to a Microsoftsupport page (Microsoft, n.d. b), MicrosoftTeamsforEducation collects, processes, and visualises various types of usage data through the Education Insights function. This aims to provide teachers with insight into the digital learning behaviour of their students. The platform presents itself not merely as a communication or organisational tool, but as a data-driven support system for pedagogical decision-making: it extends traditional data sources—such as grades and quiz results—by incorporating digitally generated activity and interaction data (e.g. online presence, communication, completion behaviour), thereby helping teachers analyse performance and engagement.
Microsoft explicitly lists the data categories that Education insights collects and visualises, including: assignment-related data, interactions in channels, file usage, notebook edits, meeting data, reading and progress metrics, search behaviour, and check-ins. This data collection is event-based, meaning that activities are logged automatically as they occur within the system. Not only teachers but also students can view their own collected data through personal dashboards. Microsoft emphasises that this data collection is carried out in compliance with existing privacy and regulatory frameworks (Microsoft, n.d. b).
4.2.3 Cost-Free Access as a Platform Strategy
The core components of Office365Education—including Word, Excel, PowerPoint, OneNote, and MicrosoftTeams—are offered to students, teachers, and educational institutions without direct licensing costs, provided that a valid school-related email address is available. Microsoft states that this provision aims to equip all users with tools for learning and skill development and describes the software as a donation to educational institutions free of charge. Access remains available as long as users are enrolled in or employed by an eligible institution (Microsoft, n.d. c). However, although Microsoft promotes the free or low-cost use of certain services for schools, this portrayal conceals the material prerequisites necessary for actual access (Marone & Heinsfeld, 2023, pp. 45).
The notion of cost-free access should not be viewed as an altruistic act, but rather as a strategic element of digital platform economies. Within the theory of platform capitalism, it is argued that platform corporations aim to exploit network effects and maximise their user base by initially offering products free of charge to establish widespread adoption. The strategy of cross-subsidisation— providing core services at no cost while generating profit through other business areas—represents a key mechanism in scaling digital markets (Srnicek,2018, pp. 48-49). These economic characteristics of digital platform services are emblematic of platform-capitalist models, in which profit generation no longer depends primarily on the sale of individual products but on the collection and processing of vast amounts of data, which function as raw material (p. 90).
4.3 Answer tot he Research Question
The present analysis sought to determine how digital education platforms such as Micro- softforEducation reinforce educational inequality and how these processes can be explained using Bourdieu’s theory of habitus and capital within the broader framework of the platformisation of education. It became evident that MicrosoftforEducation aligns with specific forms of capital that are unequally distributed across society.
While access to the platform is formally free of charge, it presupposes economic capital in the form of devices, stable internet connections, and suitable home learning environments (Barragan Moreno & Lozano Galindo, 2025, pp. 3-5; Wang, 2025, pp. 2-4). Analysis of ICILS and PISA findings revealed that these material conditions are particularly limited among students attending non-grammar schools and those from socio-economi- cally disadvantaged households (Lewalteretal.,2023, pp. 244-245). The platform’s apparent low-threshold accessibility thus obscures structural exclusions tied to social background, which can be interpreted as manifestations of unequally distributed economic capital. Furthermore, effective use ofMicrosoftforEducation requires embodied cultural capital in the form of digital competences, self-organisation, and self-regulatory learning strategies. Yet empirical findings show that these competences are also unequally distributed and correlate significantly with social origin, school type, and familial support (Eickelmann et al., 2024, pp. 18-20; Zhao, 2023, pp. 636-640). The analysis also revealed that MicrosoftforEducation actively contributes to the formation of a digital habitus. Algorithmically structured learning environments guide attention, evaluate behaviour, and generate normative conceptions of “good” participation, productivity, and academic achievement. Learners whose practices align with these algorithmically shaped expectations receive affirmation and visibility, while deviations—such as lower online presence or differing learning rhythms—are marked as deficiencies. The digital habitus thus arises from the interplay between social origin, educational practice, and algorithmic control, reinforcing pre-existing inequalities (Biermann,2023,p.24;Microsoft,n.d.b). This mechanism becomes particularly visible in the algorithmic structuring produced by learning-analytics functions such as Education Insights . The continuous capture and visualisation of usage and performance data suggest objectivity and transparency but in fact reproduce social inequalities already embedded within the data (Ba- ker&Hawn,2022, pp. 1058-1075). In Bourdieu’s terminology, such data-driven classifications can be interpreted as new forms of symbolic violence that translate social differences into seemingly neutral metrics and thereby legitimise them.
The integration of MicrosoftforEducation into platform capitalism further demonstrates that these pedagogical effects cannot be viewed independently of economic interests. The platform’s free access serves as a strategic instrument for generating network effects and establishing long-term dependency of educational institutions on technology corporations. In this way, schools become sites where raw material—data—is produced for economic exploitation (Srnicek,2018, pp. 48-49). Within the postdigital context, this platform logic has become largely normalised: digital infrastructures such as MicrosoftforEducation no longer appear as external interventions but as self-evident components of school organisation (Jandricetal.,2018, pp. 895-896). Paradoxically, this very normalisation contributes to the invisibility of the platform’s inequality-producing effects. For disadvantaged learners, this can further amplify existing disadvantages (Redecker, 2023, p. 88).
Digital platforms such as MicrosoftforEducation therefore reinforce educational inequality by structuring learning processes along lines of economic, cultural, and digital capital that remain socially uneven. Through algorithmic classification, data-based evaluation, and the normalisation of platform-capitalist logics, existing inequalities are not only reproduced but newly legitimised. The platformisation of education thus represents not a neutral process of modernisation, but a profound transformation of the educational field with significant consequences for educational equity.
5 Discussion
The present study has shown that digital education platforms such as Microsoftfor Education cannot be understood as neutral technical tools but rather as socio-technical dispositifs that structure pedagogical practices, processes of subject formation, and logics of evaluation. Consistent with critical educational research on datafication and platformi- sation, it becomes evident that digitalisation in education does not automatically lead to more equality but, under certain conditions, even reinforces existing social inequalities. A central finding of the analysis is that the supposed cost-free nature of Microsoftfor Education creates an illusion of formal equality of opportunity, while the actual use of the platform remains tied to unequally distributed resources. Inequality thus does not emerge solely at the level of individual learning outcomes but already at the level of the prerequisites for participation. Digital devices, stable internet connections, quiet learning environments, and family support form implicit conditions of participation—conditions that cannot be assumed to be present for pupils from socio-economically disadvantaged households. The platform conceals these structural disparities by presenting learning outcomes primarily as the result of individual initiative and self-regulation. Moreover, the findings highlight that digital education platforms generate new forms of symbolic violence. Through data-driven performance visualisations, activity metrics, and algorithmic classifications, learners are continuously assessed and rendered comparable. These forms of assessment appear objective and neutral but are deeply embedded in normative assumptions about productive learning, continuous participation, and self-management. Learners whose habitus aligns with these expectations are more likely to benefit, while others are marked as deficient. Symbolic violence, once expressed through pedagogical evaluation, is thus displaced into technical processes that obscure their social dimension. The platform therefore shapes the perceptual, behavioural, and evaluative patterns of both learners and teachers, contributing to the consolidation of particular learning norms. Because digital platforms and their operational logics are perceived as self-evident features of everyday life in the postdigital condition, this form of subjectiva- tion among learners and teachers largely occurs without reflection. However, the increasing entanglement of pedagogical and economic logics—and the incorporation of educational institutions into data-economic value-creation chains—ought to be reflected upon both pedagogically and in educational policy, particularly with respect to their impacts on power relations and social inequality.
The present study, however, is subject to certain limitations. As the analysis is based on international secondary literature, the conclusions rely on existing studies conducted in different countries. Research specifically focused on Microsoft forEducation often centres on MicrosoftTeams alone. Moreover, differences in data usage, functionality, and institutional embedding limit the transferability of these findings to other learning platforms. Finally, results from the PISA and ICILS studies are confined to the perspectives of students and teachers, offering only a general orientation rather than comprehensive insight.
6 Conclusion and Educational Implications
This study has demonstrated that digital education platforms such as MicrosoftforEdu- cation do not reduce educational inequality but, under postdigital conditions, reproduce and transform it in specific ways. Drawing on Bourdieu’s theory of habitus and capital— supplemented by the concepts of digital habitus, platform capitalism, and postdigitality— it has been shown that digital education is not unconditional. Rather, digital platforms build upon unequally distributed forms of economic, cultural, and digital capital and amplify them through algorithmic structuring, data-based evaluation, and normative learning expectations.
From this, several implications emerge for educational research. There is a pressing need for a consistently inequality- and power-critical approach to digitalisation. Digital education should not be understood primarily as a process of technical modernisation but as a social process that reflects and stabilises existing power relations. Furthermore, digital competences should be taught not only as functional skills but also as critical and reflective capacities. Alongside operational skills, competencies in data, algorithm, and platform literacy must systematically become part of school education to enable learners to critically reflect on data-driven evaluation and algorithmic control.
In everyday school practice, this means that digital platforms should not be implemented without critical examination. Teachers require time, support, and institutional frameworks to select digital tools reflectively and embed them pedagogically and meaningfully. Schools should also assess which students are particularly burdened or excluded by digital demands and create targeted forms of support—for example, by providing loan devices, offering study time within the school setting, or ensuring one-to-one guidance. In the classroom, there is also the opportunity to turn digital platforms themselves into objects of critical inquiry. Learners can, for instance, analyse what data they generate, how these are visualised, and what conceptions of learning are embedded within them. Such reflexive media education not only strengthens digital competences but also fosters the capacity for social critique. In teacher education and professional development, issues of platform economy, datafication, and algorithmic assessment likewise need stronger anchoring. Teachers require not only technical knowledge but also theoretical orientation to situate digital education tools within the interplay of pedagogy, economy, and social inequality.
At the level of education policy, there is a clear need to establish transparent frameworks for the use of private-sector platforms. These should include obligations of transparency, data-protection standards, opportunities for participation and co-determination within schools, and the promotion of public, non-commercial alternatives. Equitable digitalisation of education requires sustained investment in infrastructure, personnel, and pedagogical development—not merely in software. The platformisation of education must therefore be understood as a profound societal transformation. Whether it can contribute to greater educational justice ultimately depends on the extent to which it is critically designed, regulated, and pedagogically reflected upon.
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1 “Datafication is the process through which actions, interactions and behaviours are translated into digital data that can be collected, sorted, analysed or commodified by governments and private companies” (Erstad et al., 2023, p. 6).
2 „“Platformisation refers to the widespread introduction of digital platforms into many aspects of everyday life, spanning social interaction and communication (social media platforms), online commerce, travel and transport (smartphone taxi booking services), and public services (‘Government as a Platform’)” (Erstad et al., 2023, p. 6).
3 „Symbolic power refers to the capacity to exercise symbolic violence, while symbolic domination describes forms of rule that are misrecognised yet accepted—both a precondition and a result of symbolic violence (R. Schmidt&Woltersdorff, 2018, p. 8).
- Arbeit zitieren
- Daniela Haindl (Autor:in), 2026, Economisation of Education in Platform Capitalism, München, GRIN Verlag, https://www.hausarbeiten.de/document/1697636
