Introduction

Time and time again, we see the risks of collecting huge amounts of personal data about students. In the news, cyberattacks and data breaches at schools make headlines, and there is a body of academic research dedicated to critical studies of education data (Williamson 2019; Jarke and Breiter 2019; Selwyn et al. 2018). However, in schools and education departments, the role of data is rarely questioned—it provides granular insights into student learning, it can help predict performance and incentivise students, and it can be used to personalise learning and educational experiences.

Data gathering became crucial to new regimes of ‘accountability’ and ‘new public management’ that emerged in the late 1990s, and aligns with neoliberal approaches to education that rely on performance measures and top-down surveillance of teachers and students (Ball 2003; Connell 2013). However, critical studies of EdTech are beginning to provide evidence that education data can cause harm. Some emerging issues are due to the fact that personal data has become such a valuable commodity (Couldry and Mejias 2019; Zuboff 2019), while the composition of datasets and the design of algorithms present further problems (Noble 2018). It is an important area of inquiry given children and young people in school are subject to the implications of data often without knowing and typically with few opportunities to speak back and resist.

For now, the harms of education data can be grouped into at least four (often overlapping) categories, which can be summarised as bias, student alienation, increased teacher labour, and the erosion of children’s digital privacy. While there are other conceptual or theoretical issues associated with education data, the four categories discussed here are impacting people in schools now and should be cause for concern. The goal of this commentary is to raise education data as a social justice issue for key stakeholders working in schools, schooling systems, and education ministries.

Bias

Bias in data-driven systems is typically caused by prejudiced assumptions in algorithm development and/or the composition of the dataset that the algorithm is trained on. It has both statistical and social dimensions and can lead to inaccurate measurements and representations (Perrotta 2023). A growing body of research in education is documenting the harms from the bias in data-driven systems. A study by Dixon-Roman et al. (2020) into a common writing app Essay Helper showed that the algorithm is trained on data from testing regimes and Common Core Standards in the US that already privilege dominant (white) ways of writing and thinking, placing students from diverse language groups at a disadvantage. If schools make use of this app compulsory then prejudice becomes embedded in the education system.

Similar findings have been reported by Clutterbuck and colleagues (2021: 103) into an administrative EdTech platform in Queensland, Australia, called One School, in which students’ indigenous languages could not be recorded ‘alienating some students and their identities in schooling settings’. Lu and colleagues (2021: 2) reported biases are embedded in the EdTech platform ClassDojo, which ‘disproportionately burden students experiencing marginalisation’ due to their racial backgrounds, meaning they were more likely to be ‘disciplined’ than their white classmates.

These studies highlight the problematic privileges and representations embedded in some datafied systems, which can either intensify issues that already exist or create new ones. Importantly, these issues have real-world implications. Without accurate representation, it is difficult for schools and teachers to meet the specific needs and abilities of students from diverse backgrounds, which effects their educational outcomes and future opportunities.

Student Alienation

The second data harm is the student alienation caused by increased monitoring and surveillance through EdTech platforms and data. While the drive to monitor and improve student performance via data tracking is advertised as a benefit of many EdTech platforms, only recently has the impact on students been examined. In particular, the rise of student activity monitoring software (SAMS) has raised surveillance to a new level. SAMS are typically installed on a student’s laptop by the school and enable teachers to view exactly which website a student is on at any time. Monitoring is in real-time and can be aggregated into class dashboards with simplified displays for teachers to check. Some SAMS enable teachers to turn off the Internet on student laptops or allow websites to be visited that might have been banned by the school. While SAMS enable greater control and oversight for teachers, students themselves report feeling powerless and despondent in relation to these technologies, which negatively impacts their feelings towards school, learning, and their teachers (Pangrazio et al. 2023a; Manolev et al. 2019).

SAMS is only one piece of software implemented by schools. Most schools also have Learning Management Systems (LMSs) that continuously collect attendance data, as well as Learning Delivery Platforms (LDPs) like Canvas and Moodle that track visits to the site, activities completed, and assignment submissions. Given this level of surveillance and monitoring, it is not surprising that students might feel that teachers and parents are suspicious of them. The constant reinforcement of data encourages teachers to see students as a set of numbers or performance indicators rather than people. Trust is crucial to building relationships between staff and students and this may be jeopardised when student’s feel like they are under surveillance with teachers and parents watching every move.

Increased Teacher Labour

The third category concerns the increased teacher labour involved in making datafied systems work. Despite the promises of increased efficiencies and detailed insights into learning and schooling processes, a strand of critical EdTech research has in fact found that a lot of additional teacher labour is required to make these systems work. This additional labour comes in a range of forms, such as the need for specialist skills and time to enable data interoperability between different platforms (Pangrazio and Sefton-Green 2024; Pangrazio et al. 2023b), the different types of labour involved in operating platforms (Selwyn 2021), the different data roles or ‘classes’ that exist in schools (Selwyn et al. 2022), and the new divisions of labour that are emerging (Perrotta et al. 2021).

Not all these findings are necessarily negative, but they do indicate the expanding set of skills required, as well as the increased demands on resources and time. Across these studies, teachers who had skills and experience working with data often had a career advantage and were given positions of power and authority in schools. Whether pre-service teacher education courses are preparing students for working in datafied system is unknown, but until they do, datafication will benefit teachers with statistical and/or data expertise or those with enough time and motivation to develop skills in these areas.

Erosion of Student’s Digital Privacy

The final category is the erosion of student’s digital privacy. While it might seem an obvious point, many of the digital products in schools are owned by third-party companies, like Alphabet, Microsoft, and Adobe. These digital products are not created or owned by schools or departments and are often commercial in nature. While this is not necessarily an issue, the bottom line is they are businesses that need to make money (even if their overarching goal is to help educate children and young people). Many EdTech companies rely on subscriptions; however, selling data as a second stream of income has become common practice (Kelly et al. 2023). Personal data can be aggregated and processed, enabling detailed profiles of children and young people to be constructed.

Human Rights Watch (2022) reported many EdTech companies engaging in practices that centre around the collection and use of vast amounts of children’s data—often beyond what is necessary to provide the product or service. In doing so, they undermine or put at risk children’s rights, including their right to privacy. This data can be used to target children and young people, or their families, for commercial and other purposes. The realities of how education data is currently processed and used stand in stark contrast to what is considered best practice when it comes to children’s rights. For example, Article 16 of the United Nations Convention on the Rights of the Child (UN CRC) states: ‘No child shall be subjected to arbitrary or unlawful interference with his or her privacy.’ (United Nations 1990) In addition, parents are opposed to their children using EdTech products that exploit their data for profiling and targeting purposes.

Conclusion

Of the four data harms discussed here, three directly impact children and young people, while the fourth adds additional burdens to teachers and school staff. Children and young people’s education data should only be used in ways that benefit their learning. However, it seems that the neoliberal logics that underpin contemporary education and the problematic business model of many EdTech companies are making this increasingly difficult. This will only be exacerbated with the continued embrace of emerging and unregulated technologies such as AI in Australia (Cassidy 2024), the US (Office of Education Technology 2023), and the UK (Acres 2023).

It is high time that EdTech companies, including BigTech, address the concerns of parents and families and are brought into line with what the United Nations and other international and national organisations are recommending. With 196 countries having signed the UN CRC (United Nations 1990), this should be a baseline expectation of EdTech internationally. This may run counter to the neoliberal logics that underpin the increasing and expanding datafication of schooling; however, it will ensure that the harms detailed above are minimised.

Little attention has been paid to the affective dimensions of data tracking and monitoring that takes place through EdTech platforms; however, some students are already reporting a sense of powerlessness in relation to EdTech platforms. It is therefore important that we ask what impact this might have on their sense of agency and trust in the long term. While data-driven systems may have become an inevitable feature of education, it is essential that we reveal the negative implications to those that work in schools and education authorities so that we can advocate for change.