In this series, we have been drawing on Charles Taylor’s concept of the social imaginary, the shared background understandings that make certain institutions, norms, and ways of acting seem natural and legitimate (Taylor, 2004). These imaginaries are not just abstract ideas or ideologies. They are embedded in everyday practices, routines, and material infrastructures. They provide the pre-reflective scaffolding that sustains social life. For Taylor, the modern social imaginary is shaped by the rise of secularity, individualism, and instrumental rationality, features that extend well beyond politics into institutions such as education.

This post focuses on a crucial but often overlooked dimension of the social imaginary: its moral order. Taylor argues that market society rests on a distinctive moral background, one that celebrates autonomy, productivity, and fairness as individual achievements within a competitive framework (Taylor, 2012). This moral background is not neutral. It shapes assumptions about what counts as success, who deserves reward, and how justice is measured. These assumptions are closely tied to the mechanisms of merit, ranking, and individualised accountability that increasingly define contemporary education. Educational technologies, far from being neutral tools, actively encode and reproduce this moral order. The rise of learning analytics, predictive algorithms, and AI-supported assessment is often justified in terms of objectivity, efficiency, and fairness. But as critical scholars have shown, these technologies are never value-free: they carry embedded assumptions about learners, responsibilities, and institutional priorities (Williamson, 2017). Prinsloo and Slade (2017) argue that learning analytics systems create an ethical imperative, an “obligation to act”, based on inferences about learner behaviour. Yet this obligation is often framed in ways that individualise risk and responsibility, reducing students to data subjects whose success or failure appears solely their own. Such systems promote a vision of the learner as a self-managing individual, whose moral worth is made visible through dashboards, badges, and behavioural indicators. In this way, data-driven education does not simply measure performance, it moralises it.

The metrics and rankings embedded in educational platforms reflect and reinforce the moral logic of market society. Notions of merit and fairness are increasingly configured around algorithmic comparison, presented under the guise of neutrality. Yet these systems often obscure the social and structural inequalities that shape learner experience. What appears to be a level playing field is frequently skewed by factors such as socioeconomic background, racialised access to resources, and differential forms of cultural capital. As Taylor points out, the moral background of modernity becomes most powerful when it is least visible, when it is taken for granted as common sense (Taylor, 2012).

In what follows, we examine how this moral order is naturalised through the design and deployment of educational technologies. We explore how meritocracy, behavioural analytics, and platform governance serve to legitimise inequality, all while maintaining a veneer of fairness. Finally, we consider how alternative moral sources, such as care, mutual recognition, and democratic participation, might help us reimagine assessment and accountability beyond the competitive logic of market reason (Taylor, 2007; Biesta, 2010).

Charles Taylor’s concept of the moral background helps illuminate how institutions are sustained not only by explicit rules or policies but by deeper, often unexamined assumptions about what is right, desirable, or legitimate. In Modern Social Imaginaries, Taylor (2004) argues that social orders rely on these background moral frameworks, not expressed through formal doctrine, but embedded in the taken-for-granted understandings that make certain practices intelligible. They form the underlying grammar of our institutions, making some actions feel natural while rendering others unthinkable.

Market society, in Taylor’s account, is shaped by a moral background that privileges instrumental reason, individual autonomy, and performative success. This background emerged alongside the rise of modern capitalism and secularism, fostering a view of individuals as self-directed agents whose flourishing depends on productivity, competition, and rational planning. As Taylor explains in A Secular Age (2007), this development marks the shift to an immanent frame, a context in which meaning is derived from within worldly activity, rather than from transcendent sources. Within this frame, moral worth becomes closely aligned with economic contribution and measurable achievement.

This shift gives rise to what Taylor (2012) terms a “background of mutual benefit”, a moral logic that underwrites market exchanges by presuming that social goods arise from the free pursuit of individual interests. In this worldview, success is earned, effort is measurable, and inequality is justified so long as the rules of competition are fair. The normative ideal within this order is the enterprising, self-optimising subject who rises through merit. Structural conditions are backgrounded; agency and achievement are foregrounded.

Once institutionalised in education, this moral background reshapes both the aims of learning and the criteria by which it is judged. The telos of education shifts from the cultivation of intrinsic goods, such as understanding, critical thinking, or ethical reflection, to the production of quantifiable outcomes. Liberal traditions of Bildung give way to measurable performance indicators, competency frameworks, and labour market alignment. Educational success is no longer judged by the character it cultivates, but by the outputs it produces. Value becomes synonymous with efficiency, and legitimacy with accountability.

Crucially, this moral background becomes most powerful when it is least visible. Students are encouraged to see themselves as human capital; educators are tasked with meeting targets; institutions compete through rankings and audit scores. As Taylor warns, moral orders do not disappear when they are no longer named, they often operate more effectively through their silent incorporation into expectations and infrastructures (Taylor, 2012). While some critics argue that Taylor’s framing of market society risks overlooking its contradictions or internal resistances, his account remains valuable for diagnosing the implicit norms that shape contemporary education.

Recognising the moral background of market society is essential if we are to reimagine educational values. It allows us to ask not only what we measure, but why we measure it, and whose interests such measurement ultimately serves.

In A Secular Age, Charles Taylor (2007) describes the emergence of the immanent frame as a defining feature of modern modernity. This frame refers to a social and intellectual context in which meaning is sought entirely within the bounds of worldly experience, rather than grounded in transcendent or divine sources. It marks a shift from an enchanted cosmos, imbued with sacred significance, to a disenchanted world structured by empirical rationality, instrumental reason, and human-centred explanation. Crucially, the immanent frame is not the absence of belief, but the condition under which belief and unbelief alike are understood as human possibilities within a secular moral horizon.

This transformation reshapes how modern societies imagine value, purpose, and legitimacy. Within the immanent frame, moral significance becomes increasingly tied to procedural goods: autonomy, productivity, efficiency, and self-optimisation. Taylor argues that this background enables new moral orders rooted in performativity and self-realisation (Taylor, 2007). Institutions gain legitimacy not through appeals to higher purpose, but through their capacity to improve outcomes. The ethic becomes one of continuous enhancement.

This secular ethic of optimisation is powerfully expressed in the design and operation of educational platforms. While often framed as neutral tools, such platforms concretise a particular moral order, one grounded in performance tracking, behaviour management, and perpetual improvement. As Williamson (2017) argues, the infrastructure of educational technology is never ideologically neutral: it encodes normative assumptions through its design, privileging what can be measured, compared, and controlled.

User dashboards, ranking systems, and behavioural analytics operationalise what Biesta (2010) calls the “learnification” of education, the reduction of educational purpose to the measurable progress of individual learners. These systems embody what we might call a platform morality: a moral economy encoded through interface design, algorithmic feedback, and optimisation routines. Students are nudged toward behaviours deemed productive; educators are assessed through impact metrics; institutions are ranked by performance indicators. Morality is enacted not through explicit instruction but through the logics embedded in data visualisations, behavioural cues, and default settings.

This moral order of performativity privileges what is visible, quantifiable, and improvable. It rewards engagement over reflection, compliance over critique, and efficiency over depth. As Beer (2016) argues, metrics do not merely represent performance, they actively produce it, shaping how individuals come to understand themselves and their responsibilities. Educational platforms thus deepen the immanent frame: they reconfigure moral meaning through technologies that measure, compare, and normalise conduct.

In foregrounding only what can be counted, these systems obscure what cannot, care, curiosity, dissent, and wonder. These qualities resist procedural optimisation but remain vital to a pluralistic and humane vision of education. To recover that vision, we must first recognise how the immanent frame and platform morality constrain what we imagine education to be, and how those constraints come to feel inevitable.

Educational platforms are not merely administrative tools or learning aids; they are socio-technical systems that encode particular assumptions about learners, learning, and institutional value. Among the most pervasive of these assumptions are those of meritocracy and behavioural optimisation. Drawing on long-standing liberal narratives, platforms often present educational success as a function of individual effort, measurable engagement, and self-management, legitimised through metrics that appear neutral or objective.

This faith in data-driven meritocracy builds on a broader social imaginary in which effort is presumed to translate into reward, and outcomes are viewed as just reflections of merit. As Littler (2017) argues, meritocracy is less a level playing field than a powerful cultural myth, one that obscures inequality by individualising success and failure. Platforms inherit and amplify this myth by translating complex educational processes into standardised data points.

Central to this logic are forms of data capture that function as proxies for merit. Completion rates, time-on-task, engagement scores, and AI-generated feedback reframe learning as a series of discrete, observable behaviours. These metrics are not passive reflections. As Kitchin (2014) notes, data reshape reality by structuring what is visible and actionable. In education, this means that learners become legible through the production of specific signals, and those signals become the grounds for evaluation.

In this context, metrics act as instruments of judgement. They sort and classify learners according to perceived diligence, responsiveness, and productivity. As contributors to Grek, Maroy and Verger’s (2020) World Yearbook of Education 2021 observe, the rise of accountability regimes has been closely tied to the development of data infrastructures that govern education. These infrastructures translate performance into numerical representations that are used to allocate praise, discipline, or support. While often framed as objective and neutral, such representations are shaped by institutional priorities and embedded platform logics, and risk obscuring the social and structural conditions that underpin educational outcomes.

If metrics define what counts as success, behavioural features work to ensure that learners act in ways that align with these definitions. Nudges, reminders, gamified rewards, and adaptive learning pathways reinforce particular norms of student conduct: punctuality, responsiveness, perseverance, and continuous self-optimisation. As Williamson, Bayne and Shay (2020) argue, the datafication of teaching and learning does not simply represent activity, it reshapes it, embedding specific values and expectations into the pedagogical environment. Through these systems, educational platforms operate as soft instruments of governance, directing behaviour in ways that align with institutional logics of accountability and performance.

Yet this system rests on a fiction of equality. It assumes a level playing field in which all students are judged by the same criteria and have equal capacity to succeed. But as Eubanks (2018) and Noble (2018) have shown in other algorithmic contexts, such systems routinely reinforce existing social hierarchies. In education, access to technology, familiarity with digital tools, stable home environments, and cultural capital all shape how learners appear in the data.

To be a “good” student in such a system is to be recognisable to it, to generate the right signals, at the right time, in the right format. Those whose circumstances or identities do not align with platform expectations risk being misrecognised, marginalised, or pathologised. Resisting this logic requires not only better metrics, but a fundamental rethinking of how value is defined, recognised, and distributed in digital education.

In data-driven education, fairness is often framed as equal treatment through uniform measurement. Predictive analytics, engagement dashboards, and performance rankings operationalise fairness through the principle that all students are measured by the same tools and assessed against identical benchmarks. This framing appears inclusive, offering transparency and standardisation. But this procedural notion of fairness conceals deeper structural inequities.

As Benjamin (2019) argues, the neutrality of algorithms is a seductive illusion. When systems assume that all inputs are equal, they erase the unequal social, economic, and cultural conditions from which learners begin. Differences in access to technology, prior educational opportunity, language proficiency, and cultural capital are flattened into supposedly objective scores. The result is what Eubanks (2018) calls a form of “digital punishment,” in which individuals are judged not by context but by conformity to system norms.

This issue becomes particularly acute when fairness is treated as the absence of statistical bias in the algorithm rather than the presence of justice in the learning environment. Noble (2018) shows how infrastructures, even those presented as neutral, often encode dominant assumptions that reproduce systemic inequalities. Educational platforms do this subtly: by rewarding behaviours aligned with normative expectations of punctuality, productivity, and self-regulation, they marginalise students who cannot or do not conform. Bias here is not only in the data but in the architecture of the system itself, in how it defines success, flags risk, and allocates support.

Metrics are often taken at face value, treated as transparent indicators of merit. Yet as boyd and Crawford (2012) remind us, data do not speak for themselves, they are always interpreted through cultural frames and institutional logics. In the context of educational platforms, these logics tend to privilege efficiency, self-management, and responsiveness, thereby reinforcing a narrow conception of what it means to be a successful learner. Students whose behaviours or needs fall outside these frames risk being pathologised, overlooked, or penalised.

These systems thus reframe structural inequality as personal failure. Learners come to see low scores as reflections of individual shortcomings rather than symptoms of systemic disadvantage. Institutions use these scores to justify intervention, reward, or exclusion. In this way, data infrastructures participate in the naturalisation of inequality, making deeply contingent educational outcomes appear inevitable or deserved.

Importantly, this logic is not only unjust but pedagogically reductive. It diverts attention from the relational, affective, and political dimensions of learning, privileging what can be captured over what truly counts. As Tsai and Gasevic (2017) argue, achieving meaningful fairness in learning analytics requires not only technical fixes but a sustained engagement with context, diversity, and student voice.

Resisting this reduction demands a reimagining of fairness itself, not as uniform comparison but as attentiveness to difference. A just educational system must begin with the recognition that equity is not achieved by treating everyone the same, but by designing for the conditions that shape and constrain learners differently.

Charles Taylor has long argued that modern moral life is shaped by a tension between dominant procedural moral frameworks and a deeper, often submerged commitment to value pluralism, the recognition that human flourishing takes many forms and cannot be reduced to a single metric or ideal (Taylor, 1991; Taylor, 2007). Procedural ethics prioritise formal consistency, measurability, and neutrality over substantive moral commitments, reducing justice to fairness of process rather than equity of outcome. In resisting this narrowing, Taylor insists on the legitimacy of multiple goods: care, solidarity, authenticity, creativity, and moral depth. These values, he argues, must be brought into public life not as private preferences, but as foundational sources for institutional design and collective flourishing.

In education, value pluralism offers a powerful alternative to the prevailing moral order of efficiency, competition, and optimisation. The metrics-driven culture of contemporary platforms encourages a reductive view of learning as the pursuit of measurable outcomes, engagement scores, completion rates, and learning objectives, while sidelining relational, affective, and ethical dimensions. Reclaiming pluralism means reimagining educational practices that resist this narrowing and affirm the multiplicity of what learning can mean.

A pluralistic ethic begins with care and relational pedagogy. Tronto (2013) argues that care is not simply an emotional orientation but a political and ethical practice rooted in attentiveness, responsibility, and mutuality. In education, this is embodied in dialogic teaching, restorative approaches to classroom conflict, and mentoring models that privilege connection over compliance. These pedagogies challenge the background assumption of isolated individualism by foregrounding interdependence and the shared work of meaning-making.

Imagination and creativity also serve as vital counterpoints to instrumentalism. Greene (2000) emphasises that aesthetic experience and imaginative engagement are central to expanding moral perception and opening new vistas of possibility. When curriculum design encourages divergent thinking, artistic exploration, or speculative inquiry, it supports forms of learning that defy standardisation. For instance, portfolio-based assessment allows students to reflect on their development over time, integrating personal, creative, and ethical dimensions often excluded from standardised evaluation.

Ethical judgment further deepens this pluralistic account. Biesta (2010) argues that the purpose of education is not merely to deliver content or instil behavioural compliance, but to support the formation of autonomous subjects capable of judgment. Assessment practices that foreground reflection, dialogue, or peer review help students articulate values, navigate ambiguity, and take responsibility for meaning, outcomes difficult to quantify but central to democratic life.

Together, these practices suggest a broader vision of educational value. They resist the procedural moral background of market logic and invite educators to legitimate a wider range of goods. As Taylor reminds us, modernity has not abolished moral diversity, it has obscured it through the dominance of narrowly framed ideals. To reclaim education as a space of ethical, relational, and imaginative possibility is to make room once again for plural and contested visions of the good.

This post has explored how data-driven education not only measures but moralises, embedding assumptions about merit, fairness, and value drawn from the moral background of market society. Building on Charles Taylor’s account of the social imaginary, we have seen that educational technologies reflect and reinforce a specific moral order: one that prizes individual autonomy, measurable performance, and procedural equality, all situated within a competitive, meritocratic frame.

This moral background, as Taylor (2007) argues, often operates beneath the surface of explicit reasoning. It is embedded in everyday practices, institutional norms, and technical systems, making certain values, efficiency, accountability, optimisation, appear self-evident. Educational platforms inherit this background and translate it into code, interface, and metric. They enact what Beer (2016) calls “metric power”: the capacity of metrics not just to represent, but to shape and govern behaviour. Through this lens, assessment shifts from a tool for understanding learning to a mechanism of moral sorting.

Yet these systems do more than encode assumptions, they also obscure conditions. By treating learners as data subjects and presenting fairness as uniform comparison, platforms mask the social and structural disparities that shape educational experience. As Benjamin (2019) and Noble (2018) show, ostensibly neutral technologies can reproduce deeply embedded inequities, particularly when designed without attention to context or power. In education, the outcomes presented as individual achievements or failures are often the result of intersecting forms of advantage and exclusion.

This is why reimagining assessment and platform design requires more than technical innovation. It demands that we uncover and interrogate the moral frameworks baked into the systems we use. As Biesta (2010) reminds us, education is not reducible to measurable outcomes. It involves the formation of subjects capable of judgment, care, and ethical responsibility, capacities that exceed what platforms can capture but are central to what education is for.

Such a reimagining begins with questions: What do our technologies assume about learners? Whose values do they serve? What forms of knowledge, agency, and relationality do they enable, or foreclose? It also demands institutional courage: to legitimate pluralistic practices, resist reductive metrics, and design infrastructures that honour complexity, difference, and shared inquiry.

If metrics encode the moral values of market society, then platform visibility reflects its politics, deciding not just what counts, but who counts. In the next post, we turn from the moral order of measurement to the political imaginaries of the digital public sphere. We explore how the transformation of the university through platform infrastructures reshapes who is visible, who is heard, and whose knowledge is made legible in the mediated spaces of higher education.

• Beer, D. (2016) Metric Power. London: Palgrave Macmillan.
• Benjamin, R. (2019) Race After Technology: Abolitionist Tools for the New Jim Code. Newark: Polity Press.
• Biesta, G. (2010) Good Education in an Age of Measurement: Ethics, Politics, Democracy. Boulder, CO: Paradigm Publishers.
• boyd, danah and Crawford, K. (2012) ’Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon’, Information, Communication & Society, 15(5), pp. 662–679. https://doi.org/10.1080/1369118X.2012.678878.
• Eubanks, V. (2018) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press.
• Greene, M. (2000) Releasing the Imagination: Essays on Education, the Arts, and Social Change. San Francisco, CA: John Wiley & Sons.
• Grek, S., Maroy, C. and Verger, A. (eds) (2020) World Yearbook of Education 2021: Accountability and Datafication in the Governance of Education. London: Routledge.
• Kitchin, R. (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: SAGE Publications Ltd. https://doi.org/10.4135/9781473909472
• Littler, J. (2017) Against Meritocracy: Culture, Power and Myths of Mobility. 1st edn. Abingdon: Routledge. https://library.oapen.org/bitstream/20.500.12657/103260/1/9781317496045.pdf
• Noble, S.U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
• Prinsloo, P. and Slade, S. (2017) ’An elephant in the Learning Analytics room: the obligation to act’, in Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK17), New York, NY, 13–17 March 2017, pp. 46–55. https://doi.org/10.1145/3027385.3027406
• Taylor, C. 1992. The Ethics of Authenticity. Cambridge, MA and London, England: Harvard University Press. https://doi-org.manchester.idm.oclc.org/10.4159/9780674237117
• Taylor, C. (2004) Modern Social Imaginaries. Durham, NC: Duke University Press.
• Taylor, C. (2007) A Secular Age. Cambridge, MA: Harvard University Press.
• Taylor, C. (2012). ’What Was the Axial Revolution?’ In R. N. Bellah & H. Joas (Eds.), The Axial Age and Its Consequences (pp. 30–46). Harvard University Press. https://doi.org/10.2307/j.ctt2jbs61.5
• Tronto, J.C. (2013) Caring Democracy: Markets, Equality, and Justice. New York: NYU Press.
• Tsai, Y.-S. and Gašević, D. (2017) ’Learning analytics in higher education - challenges and policies: A review of eight learning analytics policies’, in Proceedings of the 7th International Conference on Learning Analytics & Knowledge (LAK ’17), Vancouver, BC, 13–17 March 2017. pp. 233–242. https://doi.org/10.1145/3027385.3027400
• Williamson, B. (2017). Big Data in Education: The Digital Future of Learning, Policy and Practice. London: SAGE Publications Ltd.
• Williamson, B., Bayne, S. and Shay, S. (2020) ’The datafication of teaching in Higher Education: Critical issues and perspectives’, Teaching in Higher Education, 25(4), pp. 351–365. https://doi.org/10.1080/13562517.2020.1748811