datafication of education

Siemens: the biggest challenges facing education now and ways to meet them

Artificial Intelligence in schools, Education policy, Teachers, Educational Data and Evidence-informed Practice, ,

The AARE 2022 conference opens this year with a keynote from George Siemens. Here are some of his thoughts.

We have been hearing about fundamental change in education, often driven by technology, for several decades. Previous theorists, like Illich and Freire, similarly advocated for systemic education change, but their concerns were driven by economics, inclusion, and impact. When “education must change” is now advanced as a narrative, it’s often driven by a motivation to drive use of technology or the outsourcing of some core service of universities or schools. In response to the steady drum beat of calls for change, educators have become somewhat immune and even sceptical. Where is this new reality? Why has covid produced a longing for in-person learning, rather than a great drive for online learning? In our professional lives, the appeal of space and place interactions, while increasingly augmented with online engagement, remains strong.

In this talk, I present three dynamics to consider regarding our future education systems. First, I address the education landscape and the many additional stakeholders now prominently providing some core function. Secondly, I’ll address the conflicting space between data-centric research and complexity-science orientations. Thirdly, I’ll discuss the system of education itself. I believe we are facing a systemic challenge and when looking a decade into the future, it’s apparent that a fundamental change in role and responsibility will unfold for education. 

Before I begin, I want to set context for perhaps the most substantive challenge facing education. Trends can be seen as primary or secondary in terms of impact. Secondary impacts include state government mandates and even national level testing and assessment. A secondary trend may change parts of how teaching happens, the content taught, or how students are assessed. Often, the trend has a short timeline and is connected to the interests and motivations of the party in power. Primary trends, in contrast, are those that fundamentally and structurally change the systems of learning and education. In order to keep the system as it currently is, external pressure must be exerted to keep a primary trend from taking over. Unlike the rollout of national testing, which requires mandates to make things happen, a primary trend requires policy and intervention for it to NOT take over. 

We’ve seen numerous primary trends over the last decade, including the rise of social media and mobile technology. The primary trend confronting education, however, has a long history, dating back to the 1950’s, and is now beginning a rapid and alarming ascent to prominence in all areas of our lives: artificial intelligence. AI presents humanity with a unique challenge that we have not faced before: an agent with intelligence that rivals our own in a growing range of domains. 

Educationally, this presents a significant problem. In 2022, Generative AI has grown in influence and prominence. AI can now generate and create in domains that we have previously seen as exclusively our own: art, literature, and scientific discovery. DALL-E 2 and Stable Diffusion have created art that has won state art competitions. Moonbeam can create writing that has surprising coherence. LaMBDA can carry on conversations that are human-like. After being promised for decades that we would give up routine and mundane tasks to AI while retaining creative activities for ourselves, AI is emerging as an active competitor for our most human skills. Research and scientific discovery is now a pairing of human and artificial cognition. The entanglement that happens at the intersection of the two is spilling over into non-technical domains and sociologists, educators, and psychologists are evaluating how this interplay occurs and how it should be managed and supported.

Education, and all of society, moves forward with the looming AI trend in the background as the overarching development of the current era. The education landscape itself is undergoing significant commercialization and reliance on external stakeholders. Schools and universities are no longer primarily self-contained ecosystems. Instead, the fragmentation of function that defines globalisation has arrived. Online program managers support the development, marketing, and recruiting of students. International programs rely on a global recruitment network. Behind the scenes, consulting firms who had previously mainly addressed the needs of big business and large government now provide services to university and school leaders. Policy papers and guidance documents are produced by every major consulting firm in Australia and the prospect of big economic gains through innovation is a salivating prospect. Big technology is increasingly managing core university computing and security and privacy are now off loaded to these firms. Underpinning all of these transitions is the digital revolution and the data it produces as each student movement and interaction and engagement is logged and recorded. 

Digitization produces data and data produces analytics. For researchers, a conflict is unfolding reminiscent of the science wars of the 1990s. Data has won. All research – quantitative, qualitative, mixed – is digital in capture or analysis or publication. To this end, the quantitative side has resolutely and decisively ascended to the throne. The real space of debate now is on how to move data-centric research from focusing on isolated studies to instead begin assessing and evaluating holistic systems. The “science war” emerging is one where the expression of data is the primary concern. Systemic modelling and holistic assessment sits in conflict with NAPLAN and standardised testing. Research conducted is now increasingly focusing on digital spaces or at least spaces that have a digital component: AI predicting how protein folds,  sensors capturing remote environmental data,  psychologists evaluating the mental health of students in digital settings. A complexity science approach to research moves from granular and limited scope research that occurs in sanitised or limited context settings to including multi-faceted and nuanced contextual data.  

When systems change, inefficiencies are created. Organisations and individuals who evaluate and exploit those efficiencies reflect Gould’s punctuated equilibrium (or Kuhn’s paradigm shift): a sudden and significant phase change. This has been experienced in many sectors already, including the move from physical state music and movies to digital, the shift to on demand rather than broadcast media, and the move to networked media rather than centrally controlled. The accrual of inefficiencies – doing the things afforded by previous philosophies and technologies – is confronting education. How should we teach when AI is better at many cognitive tasks than we are? What should we teach when we can find and access the world’s information from our phone?

Looking a decade into the future, international organisations such as OECD see a world where technology is central to learning, where systems of education are dramatically different from what we see today, where AI is a co-learner, where focus on wellness and wellbeing are increasingly important. Educators have long been the end recipients of government initiatives, quasi-scientific pedagogical approaches, and somewhat short-sighted policy changes. The real work of education leadership is the work of systems change. Systems makers – those who create the structures that others work within – needs to be claimed by organisations such as AARE. The future of education is one that will only emerge to serve the broadest range of stakeholders when all participants have the ability to have a voice and to shape the conversation. Finding points of leverage in shaping learning systems through policy, research, funding, and planning landscape is the critical work of today for educational leaders.

Professor George Siemens is the professor and director of the Centre for Change and Complexity in Learning UniSA Education Futures. He researches networks, analytics, and human and artificial cognition in education. He has delivered keynote addresses in more than 40 countries on the influence of technology and media on education, organisations, and society. He has served as PI or Co-PI on grants with funding from NSF, SSHRC (Canada), Intel, Bill & Melinda Gates Foundation, Boeing, and the Soros Foundation. Professor Siemens is a founding President of the Society for Learning Analytics Research. In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs).

How to recognise an attack of the zombie (data)

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​​The authors are presenting their research on recognising ‘Zombie Data’ across its lifecycle in education systems at the AARE conference today PPIE SIG 3 Concurrent Session 5

“We collect it [the data] all throughout the year and I’ve never actually seen what happens with it. Where does it go and what is it used for?” (Primary Teacher)

We inhabit a world infiltrated by zombie data. Check your phone – contact details of forgotten people, games you have long stopped playing (although impressive high score!), photos of places you don’t remember, and downloaded TikToks you will never watch again. These data, gathered with little or forgotten purpose, that are no longer relevant to current lives exist as zombie data. In our education systems, zombie data devour time, space and energy. 

We seek to provide recognition of how such data are generated and the consequences of their existence. Vast amounts of data are created in classrooms and schools, retained in physical and virtual files. Individual’s digitalised data becomes part of the representation of populations and even when anonymised their datafied doppelgangers continue to walk in the world informing policy, practices and propaganda.

Defining Zombie data

We found excessive, purposeless and redundant data – ‘zombie data’. Those in the technology, economics, business, and “regtech” fields indicate an awareness that zombie data, while considered dead, ‘lurks around…waiting to be called to life again” (Datastreams, 2017). Such data has also been referred to as “huge waves of numbers without meaning or relevance” (Balleny, 2013) that create datasets “without any purpose or clear use case in mind” (Kaufmann, 2014 in D‘Ignazio & Klein, 2020).  

Zombie data reside in school systems, lurking in the infrastructures used to manage student and school data. These data are called to life and used as evidence to inform practices and policy beyond their original purpose. Study A found that policies enacted in classrooms are informed by, and result in, the production of data by students. Many of these data are deidentified, stripped of context and become publicly available on government open data sites, as reported in Study B. They remain disconnected from their previous lives ready to walk through the world at the drop of a politician’s bright idea or reporter’s query. Zombie data are seldom recognised, and we offer this warning to all, to consider the role we have in creating and maintaining such beings. 

While we were expecting zombie data to be situational, that is situated within the specific conditions of particular sites within an education system, we heard the same concerns raised across the panoramic view of our combined studies.

How do you recognise Zombie data?

The first criteria of zombie data is excessiveness. When it came to the collection of data in classrooms, a secondary student commented, “Make less… don’t try and get any extra information if you don’t need it.” A classroom teacher said, “data for data’s sake is what is killing education and killing the learning process.” A school leader reflected this view when they acknowledged that, “I have collected data this year that was a complete waste of my time and everyone else’s.” Senior bureaucrats also recognised the problem of excessive data, “What do you do with all that data, that ever-growing amount of information and pattern recognition processes, and how do you serve that up and consume that as a principal with reducing limited amounts of free time – how do you consume that?” At every level, from student through to bureaucrat, excessive data was both recognised and refuted. As an identifiable form of zombie data, such ‘excess’ carries considerable implications that are vital to consider.  

The second criteria of zombie data is that it is without purpose. Students were frustrated by their participation in the creation of purposeless data. “I was super angry that they were making me do this test for no good reason. There’s no reason for this test, it’s just so the government can keep bragging rights.” Teachers were likewise frustrated, “I already know what they [students] can’t do. I don’t need to keep pre-testing, pre-testing; I know where the kids are at, I know where they’re struggling. So, I purely do them [pre-tests] to tick a box.” Bureaucrats recognised the issue of the misalignment of data and purpose, or lack of purpose, “We are interested in getting all this information and making meaning out of it, generating knowledge – the problems that stand in the way are the quality of the data for that purpose.” The lines of tension in relation to the purpose(less) of data, are indicative of how zombie data manifest across its own lifecycle from classroom through to system contexts.

Redundancy is the third criteria used to identify zombie data in education. Data that may have had an initial function but is no longer of use. It is perhaps the most problematic of zombies, the ongoing “obsessiveness that we have about data” was explained by ‘Roger’, a senior bureaucrat who said that ‘if you torture numbers long enough they’ll confess to anything.” These data are then used for “confirmatory evidence”. As another senior bureaucrat explained, “Another way in using data is that confirmatory evidence scenario where a position is believed to be true or otherwise and then data is found to support that.” Such data may have been made redundant, devoid of its original function and then ‘tortured’ back to life. Once brought back from the dead, zombie data can threaten to haunt those in schools as this school leader expressed, “If the data is not good and it’s released out into the community, then that impact comes back onto our school.”

Combatting zombie data

While we have fun with the term ‘zombie data’ we do so with acknowledgment to the tragic history of the term zombies from 18th Century Haitian slave culture. We draw on the modern evolution archetype ‘zombie’ that developed through Haiti’s folklore and contemporary pop culture.  

We encourage educational practitioners to recognise the problematic creation and use of zombie data across the different stages of data’s life. Data gestate within evidence-informed policies prior to coming into being. The ‘birth’ of data is identified by looking to the actions of those who ‘do’ the assessments, perform behaviours, and arrive at school – identified in study A as the ‘data producers’, that is, the students. Teachers record assessment, behaviour, attendance, and enrolment data, in data infrastructures and by their actions (and those of the technocrats) data become digitalised. Once digitalised, data can lurk, in legislative archives, open data sites and old newspapers – and wait. To combat zombie data, we need to ensure all data are not excessive, they are purposeful, and they are allowed to rest in peace.

Where did we get our data? We determined the moments of data zombification across data’s lifecycle across our two research projects. By doing so, a panoramic view from classrooms to the governing bureaucratic centres of state education in Queensland, Australia, is created. In study A (Rafaan’s), data were gathered from 52 students from across Years Three to Nine in 19 focus group discussions, interviews with 27 teachers, seven school leaders and year-long observations from within a range of participants’ classrooms, professional learning communities (PLCs) and teacher preparation days. These data were then considered in conjunction with the 68 interviews conducted throughout study B (Jennifer’s) with school leaders and senior bureaucrats in Queensland’s state government run, public education system.

Dr Jennifer Clutterbuck is a sessional academic, her educational career spans early childhood classrooms, school leadership and policy roles throughout the public education system in Queensland. Dr Clutterbuck’s research focuses on the inhabitants (human and non-human), and the happenings within the topological spaces created as policy, data and digital infrastructures interact. In 2020, Jennifer received the Grassie and Bassett Prize in Educational Administration from The University of Queensland for her doctoral thesis. Her recent publications focus on the role of data in shaping the lives of those within education. Twitter: @Jenclutterbuck

Rafaan Daliri-Ngametua commenced her career as a middle years classroom teacher, working in both public and private schooling contexts. She is currently a PhD Candidate at the School of Education, The University of Queensland, Brisbane, Australia where she teaches assessment and pedagogy in undergraduate and postgraduate courses. Her research focuses on the nature and effects of the datafication of assessment and learning on student, teacher and school practices. In 2021, Rafaan was the recipient of the Carolyn D Baker memorial research prize as well as the UQ Humanities and Social Sciences Faculty Tutor Award for sustained excellence in teaching. She was also selected as a Global Change Scholar for the UQ Global Change Institute. Twitter: @RafaanDNgametua

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Education shaped by big data and Silicon Valley. Is this what we want for Australia?

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The recent banning of smart phones in public schools by several state governments shows Australian policymakers are concerned about children’s use of technology and social media in school time. But what about the way our schools use digital technologies and, in particular, how the data collected by schools about our children is being used?

We believe the frenetic data collection activity taking place in schools today is transforming education. Australia may be heading towards an educational future designed by Silicon Valley not by educators and school communities. The developers of educational technologies have a growing influence in our classrooms, and we are witnessing a shift of public education from a democratic controlled system to one designed and run by corporations.

Is this the future we want for our students, teachers and schools?

What data is being collected on students and what is the impact?


First they said they needed data
about the children
to find out what they’re learning.
Then they said they needed data
about the children
to make sure they are learning.
Then the children only learnt
what could be turned into data.
Then the children became data.

(Michael Rosen, 2018)

There is more data being collected about this generation of students than any previous generation. Beyond test results such as from NAPLAN, students’ educational progression from preschool to further and higher education can be tracked; their physical activity, use of digital devices, social media, school absences, behaviour infringements and physical locations can be recorded in perpetuity as well as tracked in real time. This can be done via software such as Sentral, currently used in over 3000 schools in Australia.

The ready availability of this wealth of data has generated new norms against which students are measured, new moral codes and social expectations, and defined students against data-derived categories. Learning analytic platforms  and personalised learning apps are being introduced to classrooms in a bid to overcome the limitations of traditional classroom arrangements. Such products measure not just students’ learning choices but keystrokes, progression and motivation. Additionally, students are increasingly being surveilled and tracked.

Students’ rights to privacy has been subsumed by the desire to maximise learning potential and to create the illusion that they are being kept safe.

How is big data transforming what teachers do?

The nature of teachers’ work is being changed by data. Firstly, they must collect more data than ever about the students in their care. Not just educational results and progress, but data is collected on the minutiae of daily student life: behaviour; demerits; uniform infractions; homework, etc.; combining to create detailed data-driven histories of students’ educational life-course. The collection of data becomes the indication that teaching has occurred.

Data has become a part of the way the teaching profession is now governed. The datafication of teaching makes teachers countable, measurable and able to be ranked. And not just through data generated about their students, but against the data that they themselves must produce about their professional development. Accreditation against the Australian Professional Standards for Teachers requires teachers to not only demonstrate their teaching via their students’ learning, but also their own ongoing professional learning and development must be documented and assessed; a process that codifies and domesticates the profession.

Educational systems are being reconfigured by data-based technologies

Learning analytics platforms and personalised learning apps are based on behaviourist theories designed to change learner’s behaviour. While behavioural modification is not a new feature of schooling, traditional discipline techniques (such as the classroom, timetables, uniforms, etc.) are being supplemented by digital technologies such as ClassDojo (the most popular educational app in use at the moment) that collect data about students’ behaviour and, significantly, also their emotional/psychological, and cognitive/neurological states. ClassDojo, for example, is used to help children develop psychological traits such as ‘grit’ and a ‘growth mindset’. Data is collected about students’ demonstrations of these traits and behavioural profiles are generated for each student. This data is not merely gathering information but also being used to govern and shape students’ bodies, emotions and thinking. Certain behaviours are rewarded through gamification techniques, while other behaviours which can’t be measured are ignored. This is an instrumental focus on behaviours and mindsets that are deemed appropriate which ignores complexity and nuance in children’s learning and behaviour.

Following on the success of ClassDojo. Silicon Valley is seeking the development of further “innovative” digital technologies that can shift more educational authority into the hands of the programmers. Such a move could potentially change the nature of teachers’ work, (from designers of educational experiences, to data administrators), and subject students to further datafication.

School students around the globe, regardless of the education system in which they are physically located, could be learning from the same apps or techno-education programs. These Silicon Valley technologies would determine what, when, and how students learn – with curriculum and assessment determined algorithmically based on students’ prior engagement and achievement.

Technologies such as learning analytics platforms replace teacher expertise with the pattern detection abilities of data analytics algorithms, risking students’ opportunities being narrowed by the assumptions encoded in algorithmic logic. 

In this potential educational future, not only are teachers bypassed (with their experience and professional judgement removed from the learning setting) but local and national educational systems lose authority to those who design such technologies.

The risks of a big data future

We have described the increasing use of data in education so that the future trajectory of schooling can be considered. The potential role of Silicon Valley in designing the educational methods of the future is highlighted. Further infiltration of educational technologies into classroom means that not just students’ results but also their behaviours and mindsets become quantifiable sources of data. Students will become more enmeshed in intensifying surveillance networks, where the teachers’ expert judgement is displaced by disembodied algorithmic and adaptive decision-making technology.

The risk is that such processes shut down educational possibility and that students’ prior actions determine the future learning made available to them. The algorithms that undergird such educational technologies are always based on past data, and not only limit students opportunity based on the programming of those that design the products, but constrain future opportunity because of the inherent bias in the data upon which the calculations are based on.

Reliance on such technologies also limits the opportunity for student-teacher relationships; without these relationships’ education is at risk of further alienating students.

We need to leave the future behind

In considering what can about this situation, we follow Australian educational researcher, Sam Sellar’s lead and argue that we need to leave the future behind.

Sellar argues that built into education is the idea that we are striving for the future, and that through education the future will be better. This optimism is built into policy, which is the political means by which we try to improve the system. It is also built in to practice, this belief that with more information, more data, we can improve the education system and through the education system improve lives.

The use of data as a means of measuring educational progress has become an end in itself. This constant quest for improvement, has led us down the path of measurement and datafication. Sellar’s suggestion of leaving the future behind:

would not mean refusing the direction of time, but rather abandoning ‘the future’ as a psychological attitude with a relatively brief history. As a hopeful disposition toward a time to come, the future has provided a basis for modern educational thought: education is oriented by desire for progress.

Sam Sellar

There are alternatives to the idea of progress as a means of evaluating education. If we stop seeking progress as the goal and start to determine the value of education via other means, we no longer need be trapped in the thrall of future thinking.

Dr Rachel Buchanan is a Senior Lecturer in Education at the University of Newcastle. She researches into the equity and social justice implications of education policy and the increased deployment of digital technologies within the education sector. She can be contacted via Rachel.Buchanan@newcastle.edu.au or found on twitter: @rayedish. 

Dr Amy McPherson is a Lecturer in Education Studies at the Australian Catholic University. She researches in the areas of philosophy of education and youth and childhood studies. She can contacted via Amy.McPherson@acu.edu.au or found on twitter: @AmyMak1601

This blog post is a condensed version of our paper ‘Teachers and learners in a time of big data’ published in the ‘Future Education’  special issue of the Journal of Philosophy in Schools.

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Vast amounts of data about our children are being harvested and stored via apps used by schools

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Electronic data is increasingly being collected in our schools without people being fully aware of what is happening.

We should be concerned about the amount of data being collected via apps and commercial software used by schools and teachers for varying reasons. We need to ask questions such as:

  • How is that data being stored and used?
  • How might the data be used in the future, particularly sensitive data about the behaviour of children?

We also need to ask about data being collected on teachers and schools.

  • Is the collection of data on individual students in fact allowing data to be collected on teachers and schools?
  • How might that electronic collation of data be used in the future?

Potential misuse and consequences for children

Recent times have brought issues about data and privacy to the public eye. A number of ‘data controversies’, including breaches from global giants like Facebook, Google and Amazon, as well as a security slipup from the huge education platform Schoolzilla, that exposed test scores of up to 1.3 million students. These issues reveal the risks of collecting human data and its potential misuse by the companies that store and use it.

A recent report published by the UK Children’s Commissioner also highlights the potential consequences for children. It reported that,

‘we do not fully understand yet what all the implications of this is going to be when they are adults. Sensitive information about a child could find its way into their data profile and used to make highly significant decisions about them, e.g. whether they are offered a job, insurance or credit’.

Many companies already use psychological profiling data to make decisions about who they employ. In the future they might find it valuable to view a behaviour profile developed through schooling to help assess an employee’s suitability.

An example: ClassDojo is accumulating sensitive data profiles on students, teachers and schools

ClassDojo is an extremely popular classroom management app designed to help teachers with school discipline and communication. What isn’t clear to many is its voracious appetite for student data or what happens to that data. Also, it’s not clear that data on teachers and schools is being collected.

New research examining ClassDojo is raising concerns about how student data about behaviour may be collected, accumulated and then used.

Much like the traditional behaviour chart ClassDojo is designed to give feedback to students about their behaviour. Students are awarded positive and negative points to reinforce or discourage particular pre-selected behaviours.

However, unlike traditional behaviour charts, ClassDojo creates a long-lasting record of the data it collects. With the ease of generating a behavioural report with the click of a button, it makes creating a permanent electronic or printed behaviour record simple for busy teachers.

As teachers monitor student behaviour by keeping electronic records, they are also creating a data set on their own behaviour over time. Collectively such student and teacher data records could be compiled for a school.

What data does ClassDojo collect?

Student behaviour in the classroom

The data gathered by ClassDojo to shape student behaviour includes:

  • behaviour performed (default behaviours are psychological character traits i.e. grit)
  • how many times a particular behaviour has been performed
  • the date when the behaviour feedback was awarded
  • the point value that comes with the behaviour
  • who gave the feedback
  • how many ‘positive’ points a student has
  • how many ‘needs work’ points a student has, and
  • a calculated percentage score representing the per cent of positive points compared to total points received.

All this data is compiled and analysed to create behaviour reports about individual students and the whole class. Reports contain red and green colour coded donut charts showing a comparison between the ‘positive’ versus ‘needs work’ behaviours. They also provide numbered statistics based on the data mentioned above, the main one being a percentage score referred to in the above list designed to represent the behaviour quality of a student or class.

The big problem with ClassDojo reports on students

A major problem with creating reports like this is that they only judge students on a small number of behaviours that ‘count’. They ignore, and even deter, diversity. For example, teachers have to identify behaviours they want students to exhibit so they can monitor them using ClassDojo. Default options include working hard, on-task, and displaying grit. This list has to be limited to a number of behaviours that is manageable by the teacher to track. The selected behaviours end up being the ones that count, others are ignored, thus promoting conformity.

Resembling a psychometric report, there is a concern that these ClassDojo reports may be collected by schools to create student behaviour profiles that follow students throughout their schooling.

Such reports could be used to make highly significant decisions about students, e.g. whether their ‘character’ profile is suitable for leadership roles, or whether they should take certain subjects.

Ultimately there is the potential that profiling in this way could influence decisions that limit or enhance future educational opportunities. We know from decades of research on the power of teacher expectations that this is an important consideration.

The vast amount of data collected by the company is a concern for all caught in the net

ClassDojo also collects a vast amount of personal data about its users including students, teachers, parents and school leaders. This data includes

  • First and last names
  • Student usernames
  • Passwords
  • Students’ age
  • School names
  • School addresses
  • Photographs, videos, documents, drawings, or audio files
  • Student class attendance data
  • Feedback points
  • IP addresses
  • Browser details
  • Clicks
  • Referring URL’s
  • Time spent on site
  • Page views
  • Teacher parent messages

Moreover, ClassDoJo says it ‘may also obtain information, including personal information, from third-party sources to update or supplement the information you provided or we collected automatically’.

The ClassDojo messaging function

ClassDojo’s also has a messaging function.  The company describes its ClassDojo’s messaging function as a ‘safe way for a teacher and a parent to privately communicate’ but this messaging function raises further concerns for us about data privacy and profiling. ClassDojo Messaging enables teachers to send text, photos, stickers, or voice notes to parents who can respond using text.

To add to our concerns over the messaging function is ClassDojo states ‘The content of all messages (including photos, stickers and voice notes) are stored. [and] … cannot be deleted by either the teacher or the parent.’

It remains unclear just how private such communication really is. While ClassDojo says it does not read these messages, it declares that school ‘district administrators can request [access to] messaging histories (plus Class/School/Student Story posts) by emailing [the company].

How safe is all of this?

So where does all this data collected by ClassDojo go?

Two of the third party service providers involved are Amazon Web Services and MLab. They are companies used by ClassDojo to store data about its users. Amazon Web Services has a less than ideal record of keeping data stored on its servers secure. Data breaches within Amazon Web Services have exposed sensitive information about thousands of GoDaddy and Accenture customers.

Because ClassDojo stores the data it collects outside of Australia, it is not subject to Australian Privacy Law. A point of difference being that US law states that companies can be forced to hand over hosted data to the government, and to do so secretly.

It’s time to take stock of the electronic data that is being collected in schools

So whilst apps like ClassDojo might be easy to use and friendly, schools need to carefully consider the potential consequences.

Too much sensitive data is being collected about our students and we need to stop and critically reflect on what is happening in schools.

We also need to be aware that by collecting data on students we are also creating data sets on teachers and schools. We do not know how such data sets could be used in the future.

For those interested in our research:  Jamie Manolev, Anna Sullivan & Roger Slee (2019) The datafication of discipline: ClassDojo, surveillance and a performative classroom culture, Learning, Media and Technology

Jamie Manolev currently studies and works at the School of Education, University of South Australia. Jamie does research in School Discipline, Digital Technologies and Primary Education. His current PhD research is investigating ClassDojo as a school discipline system. Jamie also works on the ‘School Exclusions Study’ and as a Research Assistant on the ARC Linkage funded ‘Refugee Student Resilience Study’.

Dr Anna Sullivan is an Associate Professor of Education at the University of South Australia. A/Professor Anna Sullivan is a leading expert in the fields of teachers’ work and school discipline. She is committed to investigating ways in which schools can be better places. She has extensive teaching experience having taught in Australia and England and across all levels of schooling. A/Professor Sullivan has been a chief investigator on numerous Australian Research Council Linkage grants.

Roger Slee is Professor of Inclusive Education at the University of South Australia. He is the former Deputy Director-General of Queensland Department of Education, Founding Editor of the International Journal of Inclusive Education and Journal of Disability Studies in Education, and held the Chair of Inclusive Education at the Institute of Education University of London.