Reimagining and demystifying data: a storytelling approach

Abstract

In this article, we contest globalised notions of data as ‘universally’ beneficial, necessary and ‘evidence-based’. We do so by drawing upon narrative accounts of the problematic ways data impact educators researching and working in university and schooling settings over time and in varied national contexts. We reveal how data are transient and often erroneous, even as data appear omnipresent and omnipotent. Employing an auto-ethnographic storytelling approach, we draw upon our diverse experiences as educators working within and across multiple national and subnational contexts – in England, Singapore, Bangladesh and Australia – to reflect on how data have reconstituted and recalibrated our experiences in school and university settings. We seek to break the ‘myth’ of data – that we cannot live without the supposedly complete construction of work and life that dominant, reductive assemblages of data provide. In doing so, we argue for the reimagination and demystification of broader data regimes.

Full text

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Reimagining and demystifying data: a
storytelling approach
Ian Hardy, Louise Phillips, Vicente Reyes & M. Obaidul Hamid
To cite this article: Ian Hardy, Louise Phillips, Vicente Reyes & M. Obaidul Hamid
(2023): Reimagining and demystifying data: a storytelling approach, Comparative
Education, DOI: 10.1080/03050068.2023.2189677
To link to this article: https://doi.org/10.1080/03050068.2023.2189677
Reimagining and demystifying data: a storytelling approach
Ian Hardy
a
, Louise Phillips
b
, Vicente Reyes
c
and M. Obaidul Hamid
a
aSchool of Education, The University of Queensland, Brisbane, Australia; bFaculty of Education,
Southern Cross University, Gold Coast, Bilinga, Australia; cSchool of Education, The University
of Nottingham, Nottingham, UK
Keywords: Globalisation; datafication; schooling performance; storytelling; auto-ethnography
关键词!全球化;"数据化;"学业表现;"讲故事;"自传式民族志
Abstract: In this article, we contest globalised notions of data as ‘universally’ beneficial,
necessary and ‘evidence-based’. We do so by drawing upon narrative accounts of the
problematic ways data impact educators researching and working in university and schooling
settings over time and in varied national contexts. We reveal how data are transient and often
erroneous, even as data appear omnipresent and omnipotent. Employing an auto-
ethnographic storytelling approach, we draw upon our diverse experiences as educators
working within and across multiple national and subnational contexts – in England, Singapore,
Bangladesh and Australia – to reflect on how data have reconstituted and recalibrated our
experiences in school and university settings. We seek to break the ‘myth’ of data – that we
cannot live without the supposedly complete construction of work and life that dominant,
reductive assemblages of data provide. In doing so, we argue for the reimagination and
demystification of broader data regimes.
摘要
在本文中，我们质疑数据“普遍”有益、必要且“基于证据”这种全球化的观念。通过不
同国家高校和学校教育从业者讲述数据长久以来给研究和工作带来的问题和影响，
我们提出这种质疑。尽管数据看似无处不在且无所不能，我们揭示它如何多变并经
常出错。我们采用自传式民族志的叙事方法，借助我们作为教育从业者在英格兰、
新加坡、孟加拉和澳大利亚等多个国家和地区开展工作的不同经历，反思数据如何
重构并规范我们在学校及高校环境中的体验。我们试图打破数据“神话”——脱离数据
主导并简化集合提供的所谓工作与生活之完整结构，我们无法生存。基于此，我们
主张对更广泛的数据体制重新想象并揭开其神秘面纱。

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Introduction
In this article, we reflect on our experiences as academics working in varied
institutional settings – universities and schools – that are increasingly influenced by
data and datafication. Specifically, we seek to make sense of how our work has become
increasingly influenced by attention to data, and has undergone what Harvey
(1989) referred to earlier as ‘time-space’ compression. Drawing upon our own
experiences of new modalities of time and space in school and university settings in
very varied national settings (‘north- ern’, ‘eastern’, and ‘southern’ contexts), we take
an auto-ethnographic approach to com- prehend more deeply how data are currently
construed in educational settings, and how such data recalibrate our work. Our
reflections provide insights into the nature of a broader global datascape (Lingard
2021a), revealing the interplay of space, time and relations/sociality, and time-space
compression.
Importantly, our stories represent alternative forms of data – data that can contest
and challenge more reductive, reified conceptions of performance that do not
adequately account for the complexity of actual learning. We begin by reflecting upon
the literature on data and datafication processes in institutionalised educational
settings, followed by recent theorising into the nature of time and space, and the
relations between the two, and associated notions of storytelling. We then present
our individual stories as reflections upon data and compressed datafication processes
in recent times. We con- clude with insights from an analysis across these stories
about our engagement with data in varied places and moments.
Datafication processes in institutionalised education settings
Notions of data have evolved over time and been conceptualised in varied ways
(Kitchin 2014). Data may be conceived of rhetorically (Rosenberg 2013) but they may
also be understood ‘as socially constructed, as having materiality, as being
ideologically loaded, as a commodity to be traded, as constituting a public good’
(Kitchin 2014, 4). The literature on datafication processes draws upon a variety of
perspectives and approaches explaining how data are conceptualised and engaged in
educational settings. Such datafication processes have also become increasingly
associated with standardisation of practice, often involving elaborate collections of
quantified and standardised data in both school (Hardy 2021) and university settings
(Williamson, Bayne, and Shay 2020).
Much has been written about the nature of data and datafication processes in
school- ing settings in western settings. Takayama and Lingard (2018) argue there is
a need to understand datafication in schooling beyond Anglo-American perspectives;
what is hap- pening in western contexts is not reflective of what is occurring in other
settings. At the same time, these datafication processes are also made possible
through various data infrastructures (Sellar 2019), which are increasingly
interconnected with private industries and organisations that form part of a larger
matrix of public-private partnerships; in this way, commercialisation is increasingly

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embedded within public/government schooling today (Hogan and Thompson 2021).
Datafication processes are also evident in the higher education space. Universities
have become increasingly dominated by neoliberal logics, manifested in often
unreflective and conservative leadership practices, including competing for
publications and metric profiles to bolster national and international standings. Smyth
(2017) captures this well in his aptly titled The toxic university: Zombie leadership,
academic rockstars and neoliberal ideology. Relatedly, Williamson, Bayne, and Shay
(2020) refer to the datafication of university teaching to capture ways in which various
kinds of Artificial Intelligence (AI) technologies, data ana- lytic approaches and various
kinds of ‘big data’ have influenced practice and been taken up in Higher Education (HE)
institutions. Such technologies have been used in a similar vein in schooling to instill
new forms of surveillance and enhance and intensify performance man- agement and
governance processes (Castaneda and Selwyn 2018).
Literature on datafication also problematises the decontextualization of education
and the push towards the quantification and metrification of education. In relation to
PISA, the use of test scores to identify effective school governance tools (such as
strengthening school management and increasing accountability) has been critiqued
(Münch and Wiec- zorek 2023). Xiaomin and Auld (2020) problematise PISA for
Development and the Learn- ing Framework 2030 as part of the expansionist work of
the OECD, particularly in the context of increased push for SDGs (Sustainability
Development Goals). In relation to PISA for Schools, the ‘flat ontology’ of PISA ‘by-
passes’ the specific local contexts in which schools are embedded and ‘decouples’
them from their national and subnational contexts, instead foregrounding
international comparisons of schooling performance (Lewis and Lingard 2022).
While such literature provides important insights into the nature of educational
pro- cesses in institutionalised settings, there is insufficient attention to how such data
are expressed through space and over time. There is also inadequate attention to
educators’ felt experiences of engagement with data in these varied spaces and
moments.
Journeys through space and time
Datafication processes operate in both physical and virtual spaces, with the latter
under- stood as characterised by more topological relations. That is, there is a new
spatio-tem- poral ordering of society in which various expressions of movement are
not tied to traditional Euclidean notions of space but are instead understood as based
on relation- ships between those involved. Indeed, ‘movement – as the ordering of
continuity – com- poses the forms of social and cultural life themselves’ (6). Lury, Parisi
and Terranova (2012) argue that culture itself is becoming topological. Such processes
entail a whole range of indices and algorithms recalibrating how work and life are
enacted and the ways these are constituted. They also entail real-time, continuous
data surveillance and collection, oriented towards an ascribed future.
To understand the nature and complexity of spatial relations, we draw upon
Appadur- ai’s (1996) work, highlighting the cultural elements of globalisation.
Appadurai (1990) spoke about the rise of a ‘new global cultural economy’ that has led

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to increased world- wide interconnectedness entailing the increased prevalence of
various global cultural flows or ‘scapes’ (295). The five dimensions of cultural flows to
which Appadurai refers are: ‘ideoscapes’ (various ideas, symbols, stories),
‘mediascapes’ (images), ‘technoscapes’ (technological innovations), ‘ethnoscapes’
(people) and ‘financescapes’ (money). He asserted that these scapes allow ‘us to point
to the fluid, irregular shapes of these land- scapes’ (Appadurai 1990, 297), which thus
becomes a useful heuristic for making sense of these global flows. In the first quarter
of the twenty-first Century and in the midst of a global pandemic, the widespread
use of technology has also led to the ubiquity of data flows and an increasing
global phenomenon of what has been described, inspired by Appadurai, as
‘datascapes’ (Lingard 2021a), particularly in education.
Such datascapes reflect a time and space compression that has become
increasingly more fluid and dynamic as technological infrastructures have become
more advanced. We would also argue such datascapes could be ‘real’, tangible,
imagined (cf. Anderson 1983/2016), virtual, felt or ‘affective’ spaces’ (Beer 2016). Such
datascapes also present as something of an ‘imaginaire’ (Appadurai 1996),
characterised by a sense of something sig- nificant but that may also be largely
illusionary, even as their effects may be viscerally felt. At the same time, space is also
temporal in orientation and reflects processes of com- pression and acceleration
(Lewis and Hartong 2021). This focus on time is reflective of Lin- gard’s (2021b)
exhortation to examine multiple temporalities within critical policy sociology in
education today. Namely, this entails attention to the changing spatio-tem- poralities
and timespaces of policy as they unfold within a broader global context. Lingard
(2021b) argues that globalisation not only challenges the methodological
nationalism characteristic of research in many educational settings but that it also
reconstitutes and ‘recalibrates’ notions of time in ways that intersect and interleave
with the multiple geographies and scales of educational policy and practice.
Arguably, this also entails more traditional processes of ‘accelerationism’ that have
come to characterise and dominate our engagement with time, including in education
(Sellar and Cole 2017). This is expressed and enhanced by what Brynjolfsson and
McAfee (2014) describe as the ‘second machine age’ describing rapid technological
growth and inno- vation, particularly in computing power. Buddeberg and Hornberg
(2017) refer to ‘school- ing in times of acceleration’ to capture how the ‘speeding up’
of time can be expressed through how schools are managed, how results are used to
market schools, and how there is an ever-increasing focus upon performance in
schools; these processes are enabled by data generated and expressed locally,
nationally and internationally, and the narrowing of the data of most value as related
to literacy, mathematics and science. Again, such acceleration resonates with Harvey’s
(1989) earlier notion of ‘time-space’ com- pression, although of a qualitatively different
kind under current conditions.
This accelerationism is also influenced by more topological ‘information-centric’
approaches to educational provision (Sellar and Gulson 2019). Various kinds of
machine learning and algorithmic technologies have the potential to disrupt more
traditional con- ceptions of time and space and flag more indeterminate modes of
working and to bring into being more information-centric approaches to decision-

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making (Sellar and Gulson 2019). The introduction of such technologies also reflects
what Leaton Gray (2017) refers to as the social construction of time – how time is
recalibrated by technological innovations into something of a ‘hot’ chronology.
These relationships between time and space are intimately interconnected. As
Lingard (2021b) argues, ‘[s]pace and the temporal are closely related, imbricated with
each other; there is a significant temporal element to mobility; movement and mobility
are productive of both time and space’ (5). However, we would argue that it is through
our own experi- ences of this overlap between the temporal and the spatial that we
can better understand what is occurring in educational settings in relation to myriad
forms of data, including how there is simultaneously a ‘speeding up’ of time alongside
an increased ‘compression’ of space. There is also an accompanying topological
surveillance culture that encourages col- lection and engagement with more
standardised expressions of data.
Auto-ethnographic storytelling
To make sense of these experiences with data as influenced by such spatial and
temporal processes, we draw upon notions of storytelling. We analyse our personal
(auto) experiences to understand the cultural – place, time, and social – experience
(ethno) of educational performance data as an instance of auto-ethnographic
storied research (Ellis 2004). We purposefully express our personal experiences
as a stimulus to challenge sanctioned ways of doing research and representing
others (e.g. see Spry 2001). In this way, our stories represent forms of ‘data’
beyond more dominant accounts.
Such a standpoint recognises research as a political, socially-conscious act (Adams
and Stacey Holman Jones 2008), and necessitates that we foreground our
positionalities and values (Bochner 1994). We work with stories as we recognise stories
offer rich detailed ways of thinking and feeling about ‘complex, constitutive,
meaningful phenomena’ (Ellis, Adams, and Bochner 2011, 2). The stories we share are
affective moments that cat- alysed our inquiries into educational performance data
usage and how data were deployed in school and university settings more broadly.
We analyse these stories within datascapes as real, tangible, imagined, virtual, felt or
‘affective’ spaces in which per- formance data play out.
We open our stories by locating ourselves – who we each are within the scapes of
data, research and institutionalised education. Locating ourselves in this work,
following Phil- lips and Bunda (2018) propositions for storytelling, we declare our
research positionally so that readers know what position we each bring to
understanding education perform- ance data. We locate ourselves in terms of
heritage, education traditions and place of work. In storytelling, people, places and
time are alive.
Such an approach is also in keeping with Clandinin and Michael Connelly’s (2000)
standpoint that narrative inquiry involves ‘collaboration between researcher and par-
ticipants, over time, in a place or series of places, and in social interaction with
milieus’ (Clandinin and Michael Connelly 2000, 20). It focuses on ‘stories lived
and told’ (20) within these environments/milieus, and which are characterised

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by certain ‘commonplaces’ of temporality, sociality and place as dimensions of an
inquiry space. Such ‘commonplaces’ reflect our attention to the present as always
imbued with a history and projection into the future, of the interplay between the
personal and the social in context, and of events unfolding in particular, but also
potentially multiple places, at the same time. Just as the interaction between time
and space matters, so too does the interplay between temporality, sociality and
place to understand participants’ stories.
These stories also give cogency to aspects of affect that are not always ade-
quately acknowledged or represented in accounts of educational practice and pol-
icyscapes more broadly (cf. Carney and Madsen 2021). Irving Epstein’s (2019)
volume on affect theory and discourses of comparative education seeks to
capture the intensity of encounters, power of assemblages, meaning-making, and
contingency that characterise the human condition, influenced as it is by more
globally constituted and commodified contingencies and cultural objects.
Carney’s work on policyscapes (Carney and Madsen 2021), and his series
(with Irving Epstein and Daniel Friedrich; Bloomsbury Academic) on ‘New directions in
comparative and international education’, place much greater value on emotions,
well-being and affect; these are aspects of the human condition that are deeply
embedded in the stories and narratives that characterise personal accounts of
practice.
Storytelling through time and space: data, research and institutionalised
education
We first present our individual stories, followed by our collective reflections on
processes and practices of data across these stories. To understand such journeys ‘on
the ground’, we draw upon our own experiences of having engaged with myriad forms
of data in our workplaces – universities – and in the schools and schooling settings in
which much of our research work has been undertaken. Our individual experiences of
engagement with data are followed by a synthesis across these stories to show what
we can learn collectively from these experiences.
These stories include accounts about ‘data-driven’ decision-making to help identify
potential students to attend universities and to finalise A/GCE levels during COVID-
19 in England (Vicente’s story), and school-leaving results in Bangladesh (Obaid’s
story). They also relate to the dominance of national test and associated data in
Australia (Ian’s and Louise’s stories), as well as how standardised school test results
and the fear of failure (‘kiasuism’) in Singapore affect students’ learning beyond their
school years (Louise’s story).
Vicente’s story: data-driven decision making in the UK
As an academic now based in the UK, raised in the Philippines, educated in Singapore
and having worked in Singapore, Spain and in Australia, I see myself constantly
navigating educational landscapes. Frequently, these landscapes present as

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datascapes, reflected in my efforts to try to trace the flows of data in schools
and universities that often appear as rhizomatic, tangled links without a ‘centre’
(Deleuze and Guattari 1988), and by my attempts to ethically ground my sense-
making in relation to datafied practices; I ask: who is advantaged, who is
disadvantaged by the ways data are constituted, pre- sented and understood? The
global interconnectedness and flows of data that constitute education datascapes are
demonstrated quite clearly in the education sector in the UK – both in Higher
Education Institutions (HEIs) as well as in the compulsory key stages (schools).
Data associated with how HEIs engage with international students are a key concern
of mine. HEIs regularly consult the UK Council of International Student Affairs
(UKCISA), the national advisory body that provides information to international
students and univer- sities regarding study opportunities. Apart from the UKCISA, UK
universities are also guided by the Department for Education (DfE) regarding vital
policies (e.g. UK Visa Guide- lines) about student admissions. In turn, DfE pursues a
data-driven decision-making model, drawing upon the resources of a UK private
company, Quacquarelli Symonds (QS) – particularly results from its annual
International Student Survey (ISS) (which was originally created by a private Japanese
educational conglomerate (Mitsui & Co)). HEIs in the UK also maintain active
linkages with the UK Education Advisory Services (UKEAS), a private company
created in Taiwan in the 1990s, and now spread across 30 countries worldwide; this
company frequently undertakes data mining to identify pro- spective HEIs for
students, and in turn provides data processing services to these students for entry into
UK HEIs. In this context, during reflective conversations with academic col- leagues,
one question surfaces quite often: What is happening to Higher Education in the era
of big data? Or, more pointedly, how are private educational corporations and govern-
ment agencies, preoccupied with economic gain and political soft power, (mis)using
data to influence education provision in HEIs?
The data associated with the intersection between schools and further education is
also an important issue for me. In the midst of the COVID-19 pandemic, and with the
can- cellation of the national 2020 GCE/A level national examinations1, the DfE
decided to implement a standardised predictive examination system that led to a
highly embarras- sing public policy reversal. The UK’s Office of Qualifications (Ofqual),
guided by a directo- rate and advisory board predominantly composed of senior
academics from UK HEIs, took on the task of creating a data-informed examination
system. (Interestingly, these senior academics’ remit more broadly is to regulate an
enormous export market of educational credentials in the UK; 4.89 million certificates
were issued worldwide in 2020 (Barcham 2021)). Ofqual incorporated historical
examination performance of a school (as opposed to students’ individual
performance), as well as class sizes, as components of a controversial algorithm to
compute students’ scores as a replacement for the GCE/A levels. The use of the
algorithm placed selective private schools (which have historically better performance
and relatively smaller class sizes) at an advantage over government schools (Wakefield
2020). As an active researcher working with UK schools, this recent fiasco has led to
difficult conversations with school practitioners: How should data- driven, evidence-
informed decisions be undertaken? Should the nature of data be focused on the

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‘solutions’ it provides regardless of possible social consequences? Or should data be
used to arrive at a more shared, common good? In this education datas- cape, I find
it necessary to map the global cultural flows of data and to identify moments where
education is reconfigured and reshaped, and how those ‘constituted’ by data may be
differentially advantaged and disadvantaged.
Obaid’s story: data inflation and the auto-pass system in Bangladesh
My story starts in the national space of Bangladesh where I was born, educated and
taught in the higher education sector. I have also researched English language
education policy and practice in Bangladesh externally, while studying and working at
an Australian university, where I have since been located for nearly 15 years.
Traditionally, data have been associated with meritocracy in this low-income but
aspiring to be middle-income, society where education is given high social value.
Academic achievement is widely per- ceived as having the capacity to disrupt the cycle
of poverty and ensure social mobility/ reproduction at the individual and societal level.
Students’ performances on national stan- dardised tests have been the main form and
source of data. Reporting these data in print and electronic media has resulted in
considerable social valuing and respect for such data, with these reporting events
celebrated as social rituals (Ali, Hamid, and Hardy 2020). Although a more holistic view
of learning is not ignored, standardised numerical data serve as the key indicator of
quality of learning, teaching, and educational institutions.
School practices are commonly geared towards improving student performance on
annual standardised examinations. Many schools often run special coaching for Year
10 students immediately before school-leaving examinations. ‘Shadow education’
(tutoring) is also prevalent, and is driven by promises of helping students to improve
performance on the tests.
Since the current government came to power in controversial circumstances in
2009, civic attention has been diverted away from questions of democracy. The
government promised higher levels of ‘development’ in all sectors, including
education. Accordingly, a discursive spin was introduced to highlight the
government’s achievements. For edu- cation, this meant achieving higher success
rates in standardised tests; the easiest way of achieving this was via ‘data
engineering’. A comparison of the secondary school certifi- cate examinations
between the first five years of the new century, and since the current government took
office, shows that pass rates have almost doubled. While previously the pass rates
were below 50%, since 2010, they have been close to 80% every year, skyrock- eting
to almost 93% in 2014. Data inflation has become a key feature of education and
datafication in the past decade.
The production and management of education data during the COVID-19
pandemic led to further, dramatic data inflation. As schools closed in March 2020 (re-
opening in Sep- tember, 2021), the education boards in the country were unable to
arrange the Year 12 Higher Secondary Certificate (HSC) school-leaving examination
for the 1.3 million stu- dents in general, religious and vocational streams of education.

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Like the Education Department of the UK Government in Vicente’s story, education
authorities in Bangladesh had to produce student performance data without
arranging examinations. They gener- ated students’ HSC results based on a formula
combining their Secondary School Certifi- cate (Year 10; 75%) and Junior Secondary
Certificate (Year 8; 25%) results and published a 100% pass rate. Consequently, the
tertiary sector has struggled to determine how to allo- cate places for students for
undergraduate studies. Moreover, while some students may be lucky to have passed
the HSC hurdle through this auto-pass system, many students are worried that they
may have to bear the social stigma of auto-pass in a society where grades are valued
because students earned them, not because of technical issues or pol- itical
manipulation.
Ian’s story: the pervasiveness of NAPLAN in Queensland, Australia
As a former secondary school teacher of Anglo-Irish and Italian heritage, who grew
up, lived, and worked in rural and urban Queensland, Australia, I have engaged
with various kinds of data related to school students as both a teacher and academic.
However, it was when I moved from New South Wales back to Queensland in 2010,
having spent the first six and a half years of my academic career at Charles Sturt
University (in the regional city of Wagga Wagga, New South Wales), that my attention
to data crystal- lised. I was surprised at how much time was devoted to national
literacy and numeracy testing – National Assessment Program: Literacy and Numeracy
(NAPLAN) – in Queens- land. When I visited schools, it seemed that every school had
detailed data about their individual student results from the test, and associated data,
even as the test was osten- sibly designed to provide a ‘snapshot’ of the systemic
outcomes of schools and systems in Australia. It seemed as though what was
publicised as something of a ‘national audit’ of how schools were performing to
purportedly provide the government with information about where additional
support was required, had morphed into a competitive regime of individual test-
taking in which schools (and states (see Lingard and Sellar 2013)) were locked in
a competitive battle with one another in which the currency of most worth was high
and/or ever improving results on NAPLAN. Students and teachers were increasingly
identified as underperforming and as needing remediation depending on how schools
were portrayed in these annual results. It seemed that various ‘system-val- idity’ results
associated with NAPLAN trumped more ‘site-validity’ concerns on the part of
teachers, with inadequate attention to the tensions between the two. There seemed
to be evidence of attempts to ‘hold a unitary view of validity’ (Freebody and Wyatt-
Smith 2004, 43) which downplayed the need to consider both systemic and more site-
specific needs, and how to collect evidence for these two very different purposes.
I was also struck by how, when asked about whether staff at the school where they
worked were influenced by NAPLAN, many teachers would say that they were not.
However, they would then proceed, almost in the same breath, to describe the array
of practices that were used to track and record students’ results in literacy and
numeracy, and how these were collected in response to concerns about NAPLAN
results! This occurred despite how these tests were increasingly questioned by

10
teachers, academics and teacher unions, and how they could have detrimental effects
for the students who sat them, including reports of feelings of a lack of self-worth,
and even instances of stu- dents self-harming (Howell 2016; Rice et al. 2016). Teachers
had imbibed systemic demands around the need to collect data on an ongoing basis
and this was seen as necessary to be able to track and trace how students were
performing. It didn’t seem to me that these teachers were somehow naïve about the
effects of such standardisation processes but simply that they often struggled to think
beyond the parameters of such testing under conditions in which the generation and
collection of such data were so per- vasive. This is also perhaps not surprising given
the plethora of resources provided to schools by various edu-businesses/companies
seeking to monetise the focus on
NAPLAN. Indeed, in the Queensland (and Australian
context more broadly), parents can purchase NAPLAN preparation workbooks and
trial tests at newsagents and supermarkets to work with their children to help them
prepare for the test.
The systematicity of these practices was also reflected in how teachers, and
principals in particular, would describe how the push to collect these data came from
the ‘centre’, from Education Queensland (Department of Education), and that this was
particularly pushed by what they described as their Assistant Regional Directors
(ARDs) in each of their regions. In the Queensland context, a regional director
oversees all aspects of edu- cation provision – including schooling and vocational
education – in that jurisdiction. The wide scope of this role invariably means these
regional directors delegate authority to individual schools to their Assistant Regional
Directors – who might be responsible for looking after 20, 30 or more schools,
depending on the size and complexity of the region and the nature of their other
responsibilities.
When asked about conversations with these ARDs as school principals’ ‘line-
managers’ (a common term used by principals, even as the term is most obviously
associated with the business and corporate world), they would invariably describe how
they would be asked to show how they had endeavoured to improve their students’
performance against an array of school generated curriculum and standardised data,
including NAPLAN. One discussion sticks in my mind; one principal in a rural
school described how her ARD asked her the question about why her NAPLAN
data in Year 5 had dropped from the previous year, even though she only had two
students in Year 5! As she relayed it: ‘“Yes, we know you’ve only got 2 kids". But they
still ask – they still push: “Hey, your NAPLAN data dropped from last year! What’s
going on?” Even though they say all the right things, they still ask that question! And
you think, it’s all about data to them; it’s all about numbers’ (Hardy 2021, 163).
Louise’s Story: Australian & Singaporean experiences of performance data
I am a fifth generation white Australian, schooled in Catholic schools in Brisbane, then
trained as an early childhood teacher in Sydney. I taught in child-care centres,
preschools, and schools in Sydney and Brisbane for 8 years before moving into teacher
education. My scholarship and lived experiences of early childhood education always
foregrounded hol- istic, qualitative, personalised data (or ‘documentation’, inspired by

11
the work of the pre- schools of Reggio Emilia, Italy). The shock of the impact of
schooling performance data first struck me when a young boy I know intimately was
in Year 3 and handed me his Queensland Year 3 Literacy and Numeracy test (the
standardised Queensland state test used to collect data about students’ literacy and
numeracy before NAPLAN was intro- duced as a national test in 2008) results, and
said, ‘I think they are telling me that I am stupid.’ He had been away sick on the day
the test was conducted and was made to sit the test on the first day he returned to
school, during his lunch-break play time. As an 8-year-old boy, he was not
interested in the test; he wanted to play with his friends. The report noted he
was above average for everything except writing, in which he scored below the
national minimum benchmark, probably because he scarcely wrote any- thing as he
wanted the test to be over as quickly as possible! Interestingly, at the age of 8, he had
the literacy and numeracy skills to interpret the report, despite his class teacher
providing no explanation of the report, just sending it home in children’s bags for
parents to read. He is highly intelligent, one of the most widely read and
knowledgeable people I know, yet because of schooling performance data, he built
an internal, self- deflating dialogue that immobilised him from completing school,
and university (despite being awarded a merit scholarship for his entrance exam
results). I tell this story to communicate the affect of academic performance data
and reporting, and how it is a heavily weighted symbol in the ideoscape of
datascapes.
In July 2019, I relocated to Singapore to become coordinator of an undergraduate
teacher education programme that attracted mostly Singaporean and some
international students. As soon as I started teaching in Singapore, I noticed how quiet
the Singaporean preservice teachers were. Often, I would directly ask an individual
student a question, and would not receive a reply. The students would look
apprehensive or turn to the student next to them, hoping they may speak for them.
Over months of facilitating relational ped- agogies in classes of preservice teachers, I
came to realise that through schooling, Singa- porean students learnt to follow the
teacher instructed formulas, to not question, to not initiate, to not speak, to not create
anew, to only listen and write. To get the Primary School Leaving Examination (PSLE)
results required to get into the secondary school their parents wanted them to
attend (thereby enabling access to subsequent post-sec- ondary (particularly
university) courses), Singapore students learnt to follow exactly the teacher instructed
formulas, to listen, memorise and recall facts.
Singapore is often described as a nation ideologically built on meritocracy (e.g. see
Tan 2008). A small island state of 709 square kilometres, it was founded and built as a
nation on its people as human capital resources. Arguably, with a reliance upon human
capital alone, a compliant citizenry has been carefully orchestrated, particularly
through a culture of fear: fear of punishment and fear of losing out. There is an
entrenched societal fear of failing – what is locally referred to as ‘kiasuism’– a Singlish
word derived from Chinese and Hokkien that has a meaning close to ‘fear of losing
the best’ or ‘anxiety caused by the fear of losing an opportunity’ (Altay 2013).
Consequently, children are subjected to tutoring from the age of two. From my lived
experiences in Singapore, I witnessed kiasuism clearly functioning as a well-

12
entrenched ideoscape (Appadurai 1996) across the Singapore experience of schooling.
In this context, tutoring in the early years has become normalised so that even edu-
cation scholars and expatriates get ‘sucked into’ this expectation. Every residential
hub, shopping district/mall has a multitude of tutoring businesses or ‘enrichment’
classes. There is some monitoring and regulation of these centres by the Ministry of
Education but no oversight of individual tutors operating independently (Teng 2014).
Anyone can be a tutor in Singapore; there is no close monitoring of qualifications
(Cheng 2019). One day, as I was climbing on my bike outside an early years
tutoring business, I looked across at its glassed entrance and saw a child (perhaps
two or three years of age) coiled on the floor, blankly looking through the gap under
the blind, perhaps wishing to be anywhere else but there.
Storying datascapes: reimagining and demystifying data
Our stories, reflecting the contours of the educational datascapes we encountered,
provide multiple examples of an overreaching surveillance of educational processes
via data generation, collection and interrogation. Standardised, large-scale data
seemed to be the dominant modes in the settings we reflect upon here. Such forms
of data appeared to be everywhere – omniscient – and powerful – omnipotent.
Louise’s and Ian’s stories reflect the power of the ‘imaginaire’ (Appadurai 1996) of national
testing regimes and the data that become fixated upon within so many national systems
of schooling. From the stories provided, perhaps the most harrowing individual example
of the power of particular ideas – ideoscapes (Appadurai 1996) – was expressed in Louise’s
reflections on the power of metrics to label students and to reduce their sense of efficacy.
Her account of the young Australian boy’s experiences reflects how standardised modes
of data are deployed dispassionately in schooling settings without regard for their effects
upon students’ actual learning, or indeed their very being. Louise’s recall of the ways in
which the young boy’s schooling marginalised his individual capacities and capabilities
(reflective of the ideoscape of performance data) were similarly evident in Ian’s insights
into how such marginalisation occurs systemically, and is reflective of a broader, institutio-
nalised problem that is inadequately recognised; this is the case even as the limitations of
such testing have been well documented in the Australian context (Howell 2016; Rice et
al. 2016). More topological relations were clearly in evidence in such instances, with their
accompanying dataveillance techniques and their use of databases/data infrastructures to
encourage submission to data – acts of data submission (Lewis and Hartong 2021).
The pain and anguish associated with these data in Louise’s story also reflect the affect
(Beer 2016) that attends data and datafication processes, and how the broad systemic
influ- ences Ian reflected upon are felt by the many individuals and groups of students
(and parents and teachers) who ‘make up’ such systems. Similarly, the kiasuism associated
with the Singa- porean context was so strong that the university students themselves
seemed to feel too afraid to proffer their perspectives and insights; this fear of failure was
viscerally felt, inhibiting students’ further learning and development. Again, the
affective ideoscape at play within these ‘metric societies’ (Beer 2016) limited what these
students believed was possible. Fur- thermore, the much more prolific occurrence of

13
student (and parental) anxiety, including its extreme manifestation as suicide, is not
simply a phenomenon limited to one country (e.g. see OECD 2017; Kosidou et al. 2014;
Min 2019) but reflective of how the datafied affective ideoscape has exerted so much
influence within and across national contexts.
Such power and influence constitute part of the broader ‘meritocracy’ that has come
to be associated with schooling. Indeed, such data are so powerful that in Singapore,
for example, anxious parents themselves argue against changes to the competitive
edu- cation system that uses students’ PSLE results at the end of primary school
to filter access to more ‘successful’ secondary schools that subsequently affect later
educational and life opportunities (Ng 2020). Alternative approaches struggle to be
recognised within the datascape that has come to characterise schooling practices in
the settings reflected upon here (and more broadly).
Under these circumstances, dominant modes of data, such as standardised PSLE
results and NAPLAN data in Singapore and Australia respectively, and heavily
institutionalised forms of data collected in the Bangladesh and English contexts,
have come to be seen as necessary tools for surveillance – a situation abetted by
mediatisation of such data and the sensationalism that often attends their reporting
(mediascapes). The ‘controlled’ forms of data collected contribute to an equally
‘controlling’ approach to teaching – further acts of data submission (Lewis and
Hartong 2021) – even as such responses are recognised as problematic (e.g. see Ng
(2016) in relation to the Singaporean system). That the focus upon national
standardised data in the Bangladesh context is itself a social spectacle also speaks
to the power and influence of dominant modes of data arising from such
accountability and assessment systems.
Surveillance processes are also assisted by various kinds of private-public
partnerships between governments and industry. Whether it was the influence of the
UK private company, Quacquarelli Symonds (QS) in generating HEI league tables, or
the Taiwanese and Japanese companies that have been involved in data analytics
informing HEI decision-making in the UK, these various private organisations are
integral parts of broader infrastructures of accountability which generate and collect
data, reconstituting government schooling in the process (Hogan and Thompson
2021). Similarly, the private tuition industry in Singapore in Louise’s story, and in
Bangladesh in Obaid’s story, reflect the influence of a broader ‘financescape’
(Appadurai 1996) of funds that help to prop up, or profit out of, the entrenched pursuit
of high scores in purportedly meritocratic school- ing systems. And the plethora of
resources provided to support NAPLAN in the Queens- land/Australian context
similarly reflects the interplay between financescapes and datascapes, and how the
former enable the spread of the latter beyond the school setting; this includes into
the private realm of the family home, as parents work with their children on
NAPLAN-style mock tests and resources.
As Vicente and Obaid’s stories, in particular, also indicate, time can exert influence
in multiple and unpredictable ways. Arguably, the rate of ‘accelerationism’
(Buddeberg and Hornberg 2017; Sellar and Cole 2017) at present, and evident in

14
some of the stories, is of a different order from that reported in existing research.
In the context of the disruption created by COVID-19, there has been a viscerally
felt need to come up with a ‘solution’ quickly. In the English case, the way in which
this was to be achieved was through using historical data as a predictor of school
outcomes. In the Bangladesh case, the application of an algorithm to quickly ‘produce’
the necessary results was deemed adequate. Not only is there an acceleration evident
(in Harvey’s ( 1989) sense), but also topological relations via information-centric
manipulation of data (Sellar and Gulson 2019) in these stories. The attendant
disenfranchisement of the students affected by such datafication processes seems
to have been largely ignored (at least initially in the English case). The way in which
Ofqual incorporated historical test performance of a school (rather than students’
individual performance) in its algorithms, together with factors such as class size,
reflects this manipulation; previous outcomes were topologically connected to future
outcomes. The result was simply a perpetuation of already entrenched inequity in a
system characterised by the continued privileging of selective private schools (with
historically better performance and smaller class sizes) over govern- ment schools
(Wakefield 2020). In the Bangladesh case, the decision to implement the auto-pass
algorithm appeared to be the latest in a broader process of inadequately accounting
for the nature and evidence of students’ actual learning. The result is a dero- gation
of any subsequent data- and evidence-informed approaches that draw upon such
misleading information. We have pictorialised such a dystopic imaginaire in Figure 1
as an additional means of visually storying some of the problematics we raise here.
Figure 1 is a possible representation of how a composite datascape of our stories
might be visualised. Through imagery, we play with dominant motifs in our stories,
seeking to demystify the nature of data. Media ‘waves’ across the landscape skies
(media flag) and airwaves (radio data) (as highlighted in Ian’s story). The dominant
narrative of school per- formance data permeates the landscape across all stories as
the broader public discourse in education is metaphorically amplified (‘radio data’).
Schools and homes are crowded and dominated by the financescape of tutoring
businesses, and various kinds of ‘data mining’ companies that profit from the attention
to standardised modes of data in school- ing and university settings. The broader
ideoscape is promulgated through media and tutoring businesses (prominent in
Louise’s story) and educational authorities. The edu- cation and quality
departments/ministries seek to operationalise the imaginaire of the sig- nificance of
data (so overtly expressed in Obaid’s and Vicente’s stories). Data plumblines flow from
children and youth in homes to schools (with tutoring businesses on the side) to
departments, ministries and data-mining companies illustrating the intensity of this

15
Figure 1. The datascape of our stories: The interplay of scapes within contexts. Graphic
created by Paula Jayne of Seedhead Design Consultancy.
imaginaire and the interconnectedness of these influences. While the school may be
in the centre of this image, it is portrayed as something of a ‘factory’ for generating
multiple forms of data that are then taken up and amplified by a variety of other
institutions (including government, media, private tutoring and data-mining
companies).
Arguably, what such visualisation of these stories highlights is the evisceration of
the professional capacity of educators to identify and validate students’
learning/results, and the reification of various ‘shadow professionals’ (Lewis and
Hartong 2021) associated with the new data infrastructures. These technicians, and
the technoscapes of the predictive analytics used to generate these numbers, draw
upon incomplete/partial information that fails to capture students’ actual learning.
Instead, what is presented to students is a proxy figure that bears little resemblance
to such learning capacities and capabilities. What happens to the students as
individuals under these circumstances?
When we consider the broader policy conditions within which individual students
as learners and people are disenfranchised, Walter Benjamin’s Angel of History,
and his own storytelling approach, provide useful stimuli vis-à-vis the effects of
this reification of dominant data discourses. Reflecting upon Paul Klee’s 1920 print of
Angelus Novus (‘new angel’), Benjamin’s Angel of History is caught in a state of
perpetual motion not of his making and which is propelling him backwards into the
future. This propulsion is occurring in such a way that he can only ever see the
unfolding state of turpitude and disarray that characterises his ‘progress’:
His face is turned toward the past. Where we perceive a chain of events, he sees one
single catastrophe which keeps piling wreckage upon wreckage and hurls it in front of
his feet. The angel would like to stay, awaken the dead, and make whole what has been
smashed. But a storm is blowing in from Paradise; it has got caught in his wings with
such violence that the angel can no longer close them. This storm irresistibly propels him
into the future to which his back is turned, while the pile of debris before him grows
skyward. This storm is what we call progress (Benjamin 1955/1992, 249).
The ‘new’ (Lury, Parisi and Terranova 2012) spatio-temporal relations, together with
time- space compression (Harvey 1989) as elaborated in each of our stories reflects
the power of the storm of data influencing and impacting upon students, educators,
parents – all with a vested interest in institutionalised forms of education. It is not too

16
much of a stretch to describe lives as wrecked – ‘piling wreckage upon wreckage’– as
a result of these domi- nant discourses of data. Furthermore, the more those involved
struggle against dominant discourses and reified conditions of data, the more
difficult it appears to do so.
However, through providing accessible accounts of the problematic effects of data
– storying data – we might begin to consider alternatives – to reimagine learning. As
Ben- jamin (1955/1992) himself described: ‘traces of the storyteller cling to the story
the way the handprints of the potter cling to the clay vessel’ (91); the very act of
storytelling as the storyteller drawing from her experience or that of others and
‘mak[ing] it the experi- ence of those who are listening to his [sic] tale’ (87), enables
insights and creates perspec- tives that may contain within them the seeds for change
and necessary renewal. Such renewal may serve as a necessary corrective to the
intensity of an imaginaire of reductive forms of data. And just as the late, prominent
comparative educator Erwin Epstein sought not to write a conclusion in his edited
volume of notable twentieth century comparativists because the future of comparative
education was still very much unfolding – influenced as it is by the Global South, Asia
as Method and critiques of US comparative educators in relation to colonialism
(Cowen 2020) – so too different and varied responses and stories of different educators
with very different and varied experiences, including from various Southern contexts
and circumstances, suggests the possibility for more responsive and engaged
approaches to what constitutes more productive forms of data.
Conclusion
Through and across our respective stories, we have sought to reflect upon and critique
dominant data discourses in specific institutionalised educational settings/places.
Such stories provide an alternative form of ‘data’ and serve to debunk the purportedly
‘univer- sal’ value implied by more dominant discourses of data (Schäfer and van Es
2017). These stories give cogency to the fragmentation of lived lives, and the necessity
to ‘write in frag- ments’ in an effort ‘to honour the world as we find it, not as we want
it’ (Carney and Madsen 2021). By doing so, we seek to ‘refuse the myth’ (Couldry
2017, 238) of how data construct us, but also how we, in turn, can reconstitute how
data are construed in our everyday practices as ‘critical data practitioners’ (van
Dijck 2017, 12). That is, we seek to demystify data, at the same time we wish to
reimagine it.
Such demystification and reimagination entails acknowledging and including the
messiness of actual stories – a form of data that cannot simply be ‘cleaned’ in ways
sometimes ascribed to more dominant forms of data. At the same time, we also
acknowledge the inherently auto-ethnographical nature of the stories presented, and
that a different set of stories could also be told by other academics and educators,
reflecting a different set of experiences, and potentially requiring different forms of
conceptual resources to make sense of such stories, and their implications. Indeed, we
argue for increased attention to stories in research that are typically marginalised and
ignored within more dominant discourses of data. Such stories necessarily need to be
told in the words of those to whom they relate – to be co-constructed (not simply

17
‘collected’) – through ongoing engagement and constant checking with participants. This
includes stories from parents, students and teachers whose voices are silenced,
ignored or overlooked. Such accounts do not seek to establish a warrant for ‘gener-
alisability’ ascribed to more positivist research but instead seek to remain true to the
lived experiences of those to whom they relate. By so doing, such accounts serve as a
stimulus to others to consider how they might engage and produce data differently.
By drawing attention to stories reflective of the broader datascapes in which our
work unfolds, through our examples, we have sought to provide an alternative to
dominant dis- courses, and a way to ‘capture’ the datascape within and across
contexts. Mapping this landscape and how it affects students and educators is key to
challenging more dominant conceptions of data and their transient but deeply
limiting and problematic nature (Schäfer and van Es 2017). By doing so, we can
demystify the seeming omnipotence and omniscience of these ‘universal’ data, and
the temporal and spatial datascapes of which they have become such an integral part,
and provide possibilities for thinking differently about how to respond more
educatively to those with whom we engage.
Note
1. While the terms ‘examination’ and ‘test’ are frequently used interchangeably as nouns,
there is also a sense in which examinations are more formal assessment
instruments assessing particular courses of study while tests may be associated with
assessing particular skills or knowledge (Collins Dictionary,
https://www.collinsdictionary.com/dictionary/english/)
Notes on contributors
Dr Ian Hardy is Associate Professor in the School of Education, The University of Queensland.
Dr Hardy’s research focuses on educational policy and politics, including in international and
compara- tive settings. He is author of the recently published volume School reform in an era
of standardiz- ation: Authentic accountabilities (Routledge; 2021).
Dr Louise Phillips is Associate Professor in the Faculty of Education, Southern Cross University.
Dr Phillips’ research focuses on story(tell)ing, children’s rights and citizenship, arts and rights
based pedagogies and methodologies, decolonizing methodologies, sensation and place. She
is lead author of Research through, with and as storying (Routledge; 2018) and Young children’s
community building in action: Embodied, emplaced and relational citizenship (Routledge;
2020).
Dr Vicente Reyes is Associate Professor in the School of Education, The University of
Nottingham. Dr Reyes’s research focuses on educational transformations, technology
innovations in education and comparative education reform. His latest book is entitled Mapping
the terrain of education reform: Global trends and local responses in the Philippines
(Routledge; 2016).
Dr M. Obaidul Hamid is Associate Professor in the School of Education, The University of
Queens- land. Dr Hamid’s research focuses on the policy and practice of TESOL education in
Asia. He is co- editor of Language planning for medium of instruction in Asia (Routledge;
2014).

18
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Ian Hardy http://orcid.org/0000-0002-8124-8766
Louise Phillips http://orcid.org/0000-0002-2937-
145X Vicente Reyes http://orcid.org/0000-
0002-1539-1839
M. Obaidul Hamid http://orcid.org/0000-0003-3205-6124
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