Critical Discussion: The Political Economy of Datafication in EdTech

 Introduction

The expansion of educational technology (EdTech) is frequently lauded for enhancing access, personalising learning, and improving efficiency within complex educational systems. However, these narratives often obscure the underlying political economy shaping these technologies. Central to this issue is the datafication of education, which involves transforming student activity into quantifiable, analysable, and economically valuable data. This section critically examines how data-driven business models reshape educational priorities, redistribute power, and challenge established ethical norms.

Building on critical scholarship in digital sociology, political economy, and education, this analysis contends that the contemporary EdTech ecosystem is not solely pedagogical but is fundamentally commercial. The widespread adoption of "free" or low-cost platforms often depends on the extraction and utilisation of student data, raising fundamental questions regarding privacy, autonomy, and the broader purpose of education.

Datafication as a Structural Feature of EdTech

Datafication refers to the process by which social interactions are rendered into digital data that can be stored, analysed, and monetised (van Dijck, 2014). Within educational environments, this manifests through learning management systems, adaptive platforms, and assessment tools that continuously capture student behaviour.

These systems generate granular datasets, including:

• Clickstream interactions
• Time-on-task metrics
• Error patterns and revision histories
• Engagement indicators

Such data are not incidental but are integral to platform design. Williamson (2017) asserts that contemporary education is increasingly organised through "data infrastructures" that reshape both the understanding and governance of learning. As a result, learning becomes visible primarily when it is measurable, privileging quantifiable indicators over complex cognitive and social processes.

This development reflects broader trends in digital capitalism, where data is regarded as a key economic resource. Within this context, students are positioned not only as learners but also as data producers, contributing to the ongoing generation of valuable informational assets.

Surveillance Capitalism and the Commodification of Student Data

The economic logic underpinning many EdTech platforms can be understood through the concept of surveillance capitalism (Zuboff, 2019). This framework describes how user data is extracted and repurposed to generate predictive products and behavioural insights.

In educational settings, this involves:

• Collecting detailed behavioural data from students
• Analysing patterns to predict performance or engagement
• Using insights to refine algorithms and platform functionalities

Although companies frequently claim that they do not "sell" student data, value is nonetheless generated through its aggregation and application. For instance, large datasets facilitate the training of machine learning systems, which may be deployed across various markets and products.

The commodification of student data raises significant ethical concerns, particularly in light of the compulsory nature of schooling. Unlike other digital environments, students typically cannot opt out of platform use, which limits their agency in data-sharing practices.

Furthermore, the use of such data extends beyond immediate educational purposes. As Srnicek (2017) notes, platform capitalism relies on the accumulation of data to maintain a competitive advantage, suggesting that educational data may contribute to broader corporate strategies.

The Illusion of “Free” and Platform Dependency

A defining characteristic of the EdTech landscape is the widespread availability of platforms offered at minimal or no cost to educational institutions. Although this may seem economically advantageous, it conceals the underlying exchange in which data serves as currency.

This model generates several structural consequences:

1. Vendor lock-in
   Schools become dependent on specific platforms due to integration with existing systems and workflows.
2. Ecosystem expansion
   Companies extend their influence across multiple aspects of schooling, from communication to assessment.
3. Long-term monetisation
   Data collected today may inform future products, services, or partnerships.

Selwyn (2016) argues that such dynamics reflect the increasing privatisation of education, in which corporate actors play a growing role in shaping educational infrastructure. This raises concerns about the erosion of public control over education and the alignment of schooling with commercial interests.

The concept of "free" EdTech therefore warrants critical examination. Rather than eliminating costs, it redistributes them into less visible forms, such as data extraction and institutional dependency.

Algorithmic Governance and Decision-Making

The integration of data analytics into EdTech platforms has facilitated the rise of algorithmic governance within education. Algorithms analyse student data to generate predictions, recommendations, and automated interventions.

These systems are commonly used to:

• Identify students at risk of underperformance.
• Personalise learning pathways
• Provide automated feedback

While these tools may enhance efficiency, they also introduce significant challenges. Kitchin (2017) highlights the opacity of algorithmic systems, noting that their decision-making processes are often inaccessible to users. In educational contexts, this lack of transparency limits the capacity of teachers and institutions to critically evaluate or contest algorithmic outputs.

Additionally, algorithmic systems may reproduce existing inequalities. If trained on biased datasets, predictive models can reinforce patterns of disadvantage, disproportionately affecting marginalised groups (Eubanks, 2018).

From a pedagogical perspective, reliance on algorithmic systems may narrow the scope of learning. Biesta (2015) argues that education encompasses not only measurable outcomes but also the development of critical thinking, subjectivity, and democratic participation. These dimensions often resist quantification.

Surveillance and the Transformation of Educational Environments

The continuous monitoring facilitated by EdTech platforms represents a significant transformation in educational environments. Traditional assessment practices, which are periodic and context-specific, are increasingly supplemented or replaced by real-time data collection.

This shift has several implications:

• Normalisation of surveillance: Continuous monitoring becomes an accepted feature of schooling.
• Behavioural modification: Students may alter their behaviour in response to perceived observation.
• Erosion of privacy: The boundaries between public and private aspects of learning become blurred.

Lupton and Williamson (2017) contend that these developments contribute to the emergence of "datafied students," whose identities are constructed through digital traces. This raises concerns regarding the categorisation and evaluation of students, especially when data informs high-stakes decisions.

Moreover, surveillance practices may disproportionately affect certain groups, exacerbating existing inequalities. For example, students in highly digitised environments may be subject to more intensive monitoring, while those with limited access may be excluded from data-driven interventions.

Power Asymmetries and Educational Sovereignty

The concentration of power within the EdTech sector has significant implications for educational governance. A small number of large technology companies dominate the market, providing infrastructure that is integral to school operations.

This creates asymmetries in which:

• Companies control data infrastructures.
• Schools rely on proprietary systems.
• Students have limited agency over their data.

Williamson (2017) describes this as a shift toward "platform governance," in which educational processes are mediated by corporate technologies. This development raises questions about educational sovereignty, defined as the ability of institutions and societies to determine the aims and practices of education independently from external influence.

The reliance on corporate platforms may also constrain innovation, as schools adapt to the affordances and limitations of existing systems rather than developing alternative approaches.

Ethical Gaps and Governance Challenges

Despite the extensive data collection associated with EdTech, governance frameworks frequently remain underdeveloped. Although data protection regulations offer certain safeguards, they are often insufficient to address the complexities inherent in data-driven systems.

Key ethical challenges include:

• Informed consent: Students and parents may lack understanding of data practices.
• Transparency: Limited visibility into how data is used and processed.
• Data minimisation: Collection of data beyond what is necessary for educational purposes.
• Accountability: responsibility across institutions and providers.

Floridi et al. (2018) emphasise the necessity of ethical frameworks that address the societal impacts of AI and data technologies. In educational contexts, this entails not only regulatory compliance but also proactive engagement with ethical principles.

However, institutional capacity to address these issues is often limited. Schools may lack the expertise or resources to conduct thorough evaluations of EdTech platforms, leading to reliance on vendor assurances.

Epistemological Implications: Redefining Learning

The datafication of education has significant implications for the conceptualisation of learning. By prioritising measurable indicators, EdTech systems implicitly determine what is considered valid knowledge.

This results in:

• Quantification of learning: Emphasis on metrics and performance indicators.
• Standardisation: Alignment of learning pathways with algorithmic norms.
• Marginalisation of the unmeasurable: Reduced attention to creativity, criticality, and ethical reasoning.

Biesta (2015) critiques the "learnification" of education, in which complex educational aims are reduced to simplified notions of learning outcomes. Data-driven systems may intensify this trend by aligning educational practices with technological capabilities instead of pedagogical values.

The risk is that education becomes oriented towards what can be easily measured, rather than what is most meaningful.

Institutional Complicity and the Politics of Adoption

The adoption of EdTech is influenced by various practical considerations, such as cost, efficiency, and policy pressures. However, these factors may contribute to the uncritical acceptance of data-driven systems.

Selwyn (2016) notes that technological adoption in education is often driven by narratives of inevitability and progress. Schools may feel compelled to adopt new technologies to remain competitive or to demonstrate innovation.

Simultaneously, structural constraints such as limited funding and high workloads diminish the capacity for critical engagement. This dynamic fosters a form of institutional complicity, in which problematic practices are perpetuated through routine adoption.

Toward Ethical and Sustainable EdTech

Addressing the challenges associated with datafication requires a multi-layered approach:

1. Robust data governance
   Clear policies on data collection, storage, and use, aligned with ethical principles.
2. Critical digital literacy
   Empowering educators and students to understand and question data practices.
3. Ethical design practices
   Incorporating privacy, fairness, and transparency into platform development.
4. Regulatory innovation
   Developing policies that address the specific challenges of AI and data analytics in education.
5. Reassertion of public values
   Ensuring that technological adoption aligns with broader educational goals.

These measures require collaboration between educators, policymakers, and technology developers.

Conclusion

The data-driven business models underlying contemporary EdTech represent a fundamental transformation of education. Rather than functioning solely as tools for learning, these technologies are embedded within broader economic systems that prioritise data extraction and monetisation.

This analysis contends that the "hidden" political economy of EdTech is central to understanding its impact. The commodification of student data, the rise of surveillance, and the consolidation of corporate power challenge traditional conceptions of education as a public good.

It is essential to critically engage with these dynamics and to develop frameworks that prioritise the rights and well-being of learners. Without such efforts, education risks becoming increasingly subordinated to the imperatives of the data economy.

References

Biesta, G. (2015). Good education in an age of measurement: Ethics, politics, democracy. Routledge.

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.

Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29.

Lupton, D., & Williamson, B. (2017). The datafied child: The dataveillance of children and implications for their rights. New Media & Society, 19(5), 780–794.

Selwyn, N. (2016). Education and technology: Key issues and debates (2nd ed.). Bloomsbury.

Srnicek, N. (2017). Platform capitalism. Polity Press.

van Dijck, J. (2014). Datafication, dataism and dataveillance. Surveillance & Society, 12(2), 197–208.

Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE.

Zuboff, S. (2019). The age of surveillance capitalism. Profile Books.