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Nordin, Andreas (2018). The idea of distance in data-driven curriculum policy making: productive critique.
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The Idea of Distance in Data-driven
Curriculum Policy Making: A Productive
Critique
Andreas Nordin1
Linnaeus University, Sweden
Introduction
Since the mid-1980s, neoliberalism has come to dominate the mindset of policy makers and
politicians in the industrial world, advocating individual freedom, free markets, and limited
state interventions. Although neoliberalism as a body of thought contains many different
interpretations, freedom from state intervention constitutes a core organizing principle
(Schmidt, 2016). This ideological agenda also has implications for the educational sector,
making it more autonomous in relation to central governments and increasingly governed
according to managerial principles. However, as pointed out by Rose (1999), this new
freedom is by no means neutral. Instead, the distance created between the state and its
educational institutions and the trust that follows lead to calls for new ways to monitor and
measure educational progress (Power, 1997). As governments must govern at a distance (cf.
Rose, 1999), educational systems have to be made transparent and measurable for
governments to follow up on goal attainment. Power (1997) referred to this phenomenon as
the societal “audit explosion” because everything must be made transparent to facilitate
audits. Production and use of numerical data become the means to fill the gap of uncertainty
perceived by governments when their authoritative power over national educational systems
is decentralized and marketized. Governments’ ability to make decisions based on numerical
data answers modern society’s moral demand for impartiality and fairness, lending legitimacy
to insecure and vulnerable politicians (Porter, 1995). These politicians become insecure and
vulnerable because governments now have limited control over the everyday work of their
own welfare institutions and have to trust local actors and authorities to achieve the goals they
set. Drawing on research on data-driven education and non-affirmative education theory, this
article critically examines some ways in which distance as a fundamental ideological idea
underpinning neoliberal policy has come to shape and reshape all levels of contemporary
curriculum policy making.
Here, curriculum policy making is seen as a discursive process taking place within and
between different policy levels, distinguishing among the societal, institutional, and relational
levels (cf. Gundem, 2010). In line with the “transnational turn” in curriculum theory (cf.
Nordin & Sundberg, 2018), the societal level refers to transnational arenas and actors as well
as national policymakers and politicians. The institutional level refers to the organization,
content chosen, and methods used in schooling and national curricula. Finally, the relational
level refers to the interplay among local curriculum actors, such as teachers and pupils.
Taking a discursive approach means abandoning simplified, top-down and bottom-up
approaches to analyzing curriculum policy making and instead acknowledging curriculum
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policy making as a multidimensional, multidirectional process connected through discursive
interactions. Following Schmidt (2011, 2016), discourse encompasses both the substantive
content of ideas and the interactive process through which ideas are conveyed. To use the
words of Uljens and Ylimaki (2015, p. 38), such a position focus on “discursive relationships
among ideas, curriculum, agency, and structure; the interplay among societal aims translated
into content, methods and planning and the social interaction around these in educational or
societal institutions.” Schmidt (2016) also elaborates on different kinds of ideational content.
In this paper, though, the notion of idea is restricted to distance as a philosophical idea, the
deepest and slowest-changing form of policy ideas carrying normative values and moral
principles that structure and guide policy and politics. The focus lies on the different ways in
which the philosophical idea of distance is discursively expressed within and between
different levels of curriculum policy making in the context of data-driven education.
Data-driven education
In the educational sector, organizations such as the Association for the Evaluation of
Educational Achievement (IEA) and the Organization for Economic Co-operation and
Development (OECD) became powerful actors developing structures to monitor and measure
educational systems in the late 1950s and early 1960s. At first, the organizations followed
progress within specific countries, but since the late 1990s, they have increasingly focused on
international comparisons. The restructuring of bureaucratic states according to the principles
of new public management (NPM), along with increased globalization and thus also global
competition, has created demand for more and better comparative educational statistics
(Lingard & Sellar, 2016). In a globalized world, the production and use of educational
statistics have become the means to produce what Gorur (2015) referred to as “calculable
worlds,” a rational world where the future, however illusory, is made predictable and
therefore seemingly governable. However, the globalized world contains many uncertainties
and risks (cf. Bauman, 2006; Beck, 1992; Power, 1997), so the calculated outcomes continue
to turn out wrong, leaving nations in a condition of shock (cf. Addey, Sellar, Steiner-Khamsi,
Lingard, & Verger, 2017; Baroutsis & Lingard, 2017; Gorard, 2001; Nordin, 2014, in press;
Slater, 2015; Steiner-Khamsi, 2003; Takayama, 2008). As pointed out by Bauman (2006),
numbers “set fear afloat” through the normative power exercised when they are visualized in
league tables and ranking lists, and this fear expands and thickens as distance grows, and
actors and actions disappear out of sight. The spatial distance requested among policy makers
to secure objectivity thus also has a productive dimension because it allows for increased
complexity and thus new uncertainties, paradoxically making curriculum policy making even
more irrational and unpredictable. Drawing on Power (1997, p. 95, italics in original), one can
argue that the educational sector in effect has been “colonized by an audit process which
disseminates and implants the values which underlie and support its information demands.”
Numerical data have become the obvious first language of education, expanding the very
distance(s) they were set up to bridge (cf. Nordin, in press).
However, this development has not led to any epistemological shift among insecure
politicians. Instead, it has triggered the production and use of statistics, with ever more
refined sets of indicators covering more policy areas as statistical data have become the only
legitimate governing tool for politicians operating at a distance to use (Addey et al., 2017;
Lingard & Sellar, 2016). Following Lawn (2013, p. 9), the act of visualization appears to be
viral, turning system actors into believers in “self-creating hubs of data production and flow.”
This is evident in the continued development of new international large-scale assessments
(ILSAs) aimed at covering ever more aspects of education. For example, the OECD has
followed up its Programme for International Student Assessment (PISA) with new
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assessments, such as PISA Tests for Schools, PISA for Development, and the Programme for
International Assessment of Adult Competencies (PIAAC). As pointed out by Lingard and
Sellar (2016, p. 364), the OECD also attempts to include so-called “non-cognitive skills” in
its assessment structures, making them seem as comprehensive as possible. This development
supports Porter’s (1995) argument that numerical data are so flexible that almost anything can
be made measurable and thus seemingly governable. However, this is not just a development
on the societal level. ILSAs are based on tests performed by school leaders, teachers, and
students whose everyday work has to be arranged in such a way that they can produce reliable
data for use by policy actors, such as the OECD and the IEA. This means it is imperative to
also insert a specific evaluative rationale into the institutional and relational levels. Here, the
idea of distance is imperative, a distance that is supposed to ensure the provision of reliable
data. In this development promoting standardized classrooms and teaching to the test (Au,
2011), the most distant (and disinterested) actor becomes the evident expert at the
transnational and national policy levels as well as in the classroom (Lewis, 2017). In this
paper, the concept of “data-driven education,” therefore, refers to the different ways in which
the production and use of numerical data shape the way education is thought and acted out
within and between various (societal, institutional, and relational) levels of curriculum policy
making.
A productive critique
In recent years, a growing amount of research has critically examined the development
of data-driven education policies and practices. Some researchers have focused on
methodological issues and the accuracy of measurements (e.g., Sivesind, 2014; Wu, 2010),
and others on the production and the producers of educational statistics (e.g., Lawn, 2013;
Lingard & Sellar, 2016; Tröhler, 2014). Important contributions have also examined the
productive aspects of numerical data and their use to shape and reshape the education sector
(e.g., Alasuutari & Rasimus, 2009; Lewis & Hogan, 2013; Martens & Niemann, 2013;
Mølstad, Petterson, & Forsberg, 2017; Nordin, in press; Takayama, 2010) and to promote
transparency, control, and efficiency in schools (e.g., Au, 2011; Lewis, 2016; Lingard &
Sellar, 2013). Together, these strands of research have contributed to broadening the
understanding of the societal, methodological, and relational aspects of data-driven
curriculum policy making. However, Gorur (2017) argued, still too little attention has been
directed to the co-production of society taking place in the interplay between society and
science (here understood as statistics). From a theoretical perspective, Gorur (2017, p. 349)
turned to science and technology (STS) to develop “a more productive critique”
acknowledging fundamental questions, such as “what kind of worlds are being brought into
being, and what kinds of worlds do we want to create?” However, as much as I agree on the
need for a more productive critique and the usefulness of STS to further such an analysis, I
argue that critical policy studies on data-driven education also need to pay more attention to
the main object of inquiry, namely, education. To be productive, a critique has to have the
conceptual resources to address both questions of what is reconfigured and reduced in
educational institutions, practices, and knowledge and what alternative imaginaries and
scenarios are at hand in terms of educational futures (cf. Connell, 2013; Thomson, Lingard, &
Wrigley, 2012). The aim of this paper is to contribute to such a productive critique of the idea
of distance in data-driven curriculum policy making, drawing on research on data-driven
education and non-affirmative educational theory. The critique centers on three expressions of
distance in data-driven curriculum policy making, which also structure the text: spatial,
methodological, and relational distance. Each aspect is examined in two steps. The first step
is deconstructive and draws on earlier research on data-driven education, while the second
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step is reconstructive and draws on non-affirmative education theory (Uljens, 2016; Uljens &
Ylimaki, 2015, 2017).
Drawing on continental European philosophy, curriculum theory, and leadership
research, Uljens and Ylimaki (2017) have outlined a non-affirmative position in education.
For obvious reasons, this paper cannot do justice to the richness of such a position. Here, the
goal is limited to the use of three of its concepts as a possible starting point to develop a
productive critique of the idea of distance in data-driven curriculum policy making. First, the
non-affirmative theory assumes that educational institutions have a position of relative
independence from societal and other interests (Uljens, 2016). This relative independence is
central to understanding education as an interactive process in which sentient agents operate
within and/or between different educational levels. Unlike many critical approaches
(reproductive and transformative), Uljens and Ylimaki (2015, 2017) argues for a reflexive
normative position that sees opening up to mutual, reflexive interaction as the critical task
rather than pushing through certain solutions decided in advance. The non-affirmative
position acknowledges the variety of interests and stakeholders in education but questions any
attempt to equate education to any one of these, be it political, economic, or other. The second
important concept is summons to self-activity. It draws attention to intersubjectivity as a
fundamental prerequisite for reaching a position of relative independence; it is through
reflexive interaction with other people carrying other experiences and visions that the future
takes shape. The third and final concept is recognition, which draws attention to the
profundity of the interactions advocated here. Recognition of a person here means recognition
of all the freedom of the other, as well as a reflexive recognition challenging one’s own
position in light of the other(s). In sum, I make use of three concepts central to the non-
affirmative theory of education (relative independence, summons to self-activity, and
recognition) to try to develop a productive critique. My intention is not to impose a certain
understanding of education but to make productive use of concepts from educational
philosophy to critically examine the idea of distance as expressed in contemporary data-
driven curriculum policy making.
Distance in data-driven curriculum policy making
The division of distance into three different expressions is made for argumentative
reasons because in educational policy and in practice, these aspects are intertwined. The three
expressions, therefore, are to be understood not as separate aspects but as shifts in focus while
critically examining a complex, composite policy idea. Fundamental to the critique developed
in this paper, spatial distance is first and foremost discussed in relation to the societal policy
level, while the discussions of methodological and relational distance are more explicitly
related to all three levels of curriculum policy making (societal, institutional, and relational).
Spatial distance
As pointed out by Addey et al. (2017), the emergence of data-driven education has to be
understood in the context of societal changes and the quest to govern the public sector
according to managerial principles (NPM), replacing presumably inefficient hierarchical
bureaucracies with the presumed efficient market (cf. Power, 1997). As noted by Rose and
Miller (1992), NPM, emphasizing local and professional freedom, embodied the neoliberal
ideal of indirect, distant governmental intervention and gave a special position to the
processes of audit and accounting. Spatial distance became a prerequisite for institutional
efficiency. As shown by Power (1997), the adoption of NPM by public-sector organizations
gave rise to new forms of non-governmental control systems, themselves soon increasingly
formalized within national governing structures. National politicians still had little or no
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control over these structures. Rose (1999, p. 199) referred to this development as a “spiral of
technicization of politics” that gave rise to new forms of experts and expert institutions,
represented in the context of curriculum policy making by organizations such as the IEA and
the OECD.
In this paper, the fundamental problem is seen to be this neoliberal separation between
the institutions to be governed and those who govern and decide educational futures, be it
national governments exercising legal power or international organizations exercising soft
power through league table and ranking lists. The distance per se is not the problem but the
different way(s) in which it reduces the complexity and richness inherent in the concept of
education to that which can be made measurable and therefore controllable from a distance.
The shift to NPM has been accompanied by new forms of accountability structures focusing
on learning outcomes measured by indicators and statistics (Lingard & Rawolle, 2011). These
structures acknowledge numerical statistics as the obvious first language of education
(Nordin, in press). Statistics, though, is a language mastered by few, so most people
(including policy makers and politicians) are dependent on mediators and/or translators to be
able to participate in the conversation (cf. Pizmony-Levy et al., 2014). Introducing a language
unknown to most educational actors, irrespective of policy level, alienates them from their
everyday work and deprofessionalizes their context-dependent language (cf. Nordin, 2016;
Robertson, 2013). Transnational actors, such as the OECD, then offer insecure national
politicians, deprived of both the tools to govern and their first language to talk about
education, a scientific method to govern education through the use of numerical data. As
formulated by Porter (1995), p. 8), “a decision made by the numbers (or explicit rules of some
other sort) has at least the appearance of being fair and impersonal. Scientific objectivity thus
provides an answer to the moral demand for impartiality and fairness.” However, the level of
predictability in a non-linear, complex, globalized world is low, repeatedly putting nations in
a condition of shock when the expected numbers do not materialize for PISA or other ILSAs
(cf. Nordin, in press). As numerical data have become the only language available for
insecure politicians to maintain pubic legitimacy, there has not been no significant
epistemological shift; instead, it has led to ever-increasing, self-generating production and use
of numerical data (cf. Lawn, 2013). One can argue that numerical data seem to have similar
effect as empty calories: they do not offer much nutrition but trigger a craving for more.
Trying to fill out the neoliberal distance with numerical data to make education governable,
therefore, seems somewhat contradictory. Numerical data are neutral, empty of educational
nutrition, and so they produce more emptiness, not less.
Turning to the non-affirmative education theory offers a complementary approach to
understand what is to be distanced from what and the reasons why. In introducing the concept
of relative independence, Uljens and Ylimaki (2015) drew attention to the neo-Marxist
question of whether education has independent power (Apple, 2002) or, to use Habermas’s
terminology, whether education has a certain (emancipatory) knowledge interest to defend.
Non-affirmative theory critiques both critical and positivistic answers to the question as
overly normative, advocating educational and societal solutions decided in advance. The non-
affirmative answer to what is defensible then is the right to question rather than to implement
already decided solutions. This is not to say that everything that exists or what people and/or
organizations would like to see come into existence is negative by definition. Rather, it is to
say that what is educationally defensible and desirable is that these existing realities or desired
futures be made subject to open, reflexive communicative interaction and that educational
institutions prepare their students to participate in such complicated conversations. In a time
when data-driven curriculum policy making has been given an almost hegemonic position,
non-affirmative theory points to the importance of continuously questioning the very system
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(and its many stakeholders) within which one operates. Non-affirmative theory reminds us
that any attempt to reduce the drainage of educational content from educational discourses
fueled with numerical data starts with a question. The distance advocated here, therefore, does
not have so much to do with geographical distance as with maintaining critical distance from
any stakeholder trying to impose a specific solution decided in advance, whether economic,
juridical, evaluative, emancipatory, democratic, or other.
Methodological distance
In the late 1960s, researchers, including Torsten Husén, Benjamin Bloom, and Robert
Thorndike, among others, met at the IEA to develop more effective methods to collect
comparable data to better understand national education systems and provide curriculum
policy makers with robust knowledge on how to improve student learning (Hegarty, 2014).
Considering the world to be their laboratory for performing experiments on different aspects
of schooling, such as organization, content, and instructional methods, they contributed to the
establishment of what Husén referred to as the “empirical-positivist paradigm” in education
(Lawn, 2014, p. 29). Central to this scientific methodology is the assumption of objective
knowledge and the possibility of producing neutral facts about educational systems and its
learners. This method soon gained public legitimacy and became a clear ideal among the
growing numbers of bureaucrats operating within the modern state. As stated by Porter (1995,
p. 8), “the appeal of numbers is especially compelling appeal to bureaucratic officials who
lack the mandate of a popular election, or divine right.” Numbers provide an unquestionable
basis for rational decision making and thus also the legitimacy bureaucrats themselves lack
while placing rival measures at a great disadvantage (Porter, 1995).
As shown in research on data-driven education, this phenomenon has expanded
dramatically within curriculum policy making over the past couple of decades. In an
uncertain, globalized world, decisions made based on numbers seemingly offer a way to
produce “calculable worlds” (Gorur, 2015, p. 578). They lend scientific objectivity to insecure
politicians who need to believe in the possibility to produce predictable futures. This
vulnerability once characteristic of bureaucrats has come to characterize national and local
politicians and educational actors at all levels as the notion of expertise has been transformed.
In the context of data-driven education, the one who is the most distant, the most
disinterested, is seen as the obvious expert. Rule-following and scientific (objective) methods
have replaced the contextual knowledge upon which politicians and educators traditionally
have relied. When the most distant is by definition the most reliable, global education policy
actors become the evident experts on any educational matter at any level.
Often forgotten is that when used for political reasons, numerical data always become
truth through social processes not entirely guided by scientific principles (cf. Porter, 1995).
Even if it were possible to ensure the objectivity of numerical statistics themselves, it would
be impossible to maintain that objectivity in their usage. Political decisions by definition are
ideological, not scientific, and therefore can never be neutral in a scientific sense. Moreover,
most politicians have not mastered advanced statistics and cannot check whether they are
objective or not. To facilitate decontextualized comparisons, the methodological quest for
objectivity has also found its way into the classroom, promoting standardized teaching and
narrowing the instructional curriculum to match the test (Au, 2011). Although not spelled out
explicitly, data-driven curriculum policy making exercises a strong normative pressure on
what knowledge is desirable, namely, that which is (or can be made) measurable and therefore
comparable (cf. Au, 2011; Nordin, in press; Sivesind, 2014). In an analysis of United States
classrooms, Au (2011, p. 30) showed that “in the case of high-stakes testing in the US, as the
content of the curriculum moves to match what the tests require, the structure of curricular
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content knowledge similarly shifts towards the fragmentation demanded by the test.” In a
study on the most recent curriculum reform in Sweden in 2011, Wahlström and Sundberg
(2018, p. 9) found a similar development.
The Swedish curriculum reform of compulsory school in 2011 represents a
denationalised and instrumental conception of education shaped by two powerful international
influences: a technical-instrumental discourse on curriculum, which emphasizes learning
outcome, and a neo-conservative discourse on curriculum, which emphasizes curriculum
content as an uncontested body of knowledge. These international influences resulted in a
standards-based curriculum. (Wahlström & Sundberg, 2018 p. 9)
The number of standardized tests and materials for teachers’ and students’ use also
tends to increase when teaching standardized curricula (e.g., Mølstad & Karseth, 2016;
Nordin, 2014), furthering the influence of the objective methodology in the classroom.
Despite the claims of objectivity and disinterestedness, data-driven curriculum policy
making has been shown to be highly normative because it ignores many forms of knowledge,
such as human experience, craft knowledge, wisdom, and tacit knowledge, to mention only a
few. In the context of data-driven education, such forms of knowledge are all seen as part of
the problem, polluting true knowledge with qualitative interpretations and personal judgment.
Non-affirmative theory shows that ranking objective and comparable knowledge as of most
worth run the risk of losing aspects perhaps hard to measure but valuable to education.
Treating numerical data as a one-size-fits-all solution narrows the educational imagination of
policy makers and politicians, as well as of teachers and students. The educational discourse
is fueled with more of the same, so they are in many ways deprived of the educational power
that lies in interactions with the other. The philosophical concept of recognition derives from
the fundamental role of the other in human development. It is in light of the other that human
development can take place when one’s own ideas and assumption are challenged and
questioned. Whereas data-driven curriculum policy making emphasizes the role of correct
answers as drivers of educational quality, non-affirmative theory highlights the role of the
question. It is the question that holds the power to transcend the present state, to move beyond
what is taken for granted. Inviting different forms of knowledge and human experiences is
perhaps not desirable within the empirical-positivist paradigm focused on standardization,
rule following, and objective assessment, but from a non-affirmative position, it is seen as a
prerequisite for educational value to emerge. Non-affirmative theory supports development as
governments, local policy actors, principals, teachers, and student themselves “learn to make
use of their productive freedom” (Uljens & Ylimaki, 2015, p. 37) and find their own voice.
Such an educational idea focuses not only on understanding national legislations and curricula
per se but also on “reaching the questions and interests to which existing policies, norms or
practices are seen as answers or responses” (Uljens & Ylimaki, 2015, p. 37).
Relational distance
Data-driven curriculum policy making rests on the assumption of harmonious
relationships among nations that learn from each other and adopt best practices from countries
performing well on ILSAs. Objective knowledge is by definition independent of context and
thus transferable. As shown by Steiner-Khamsi (2013), there are several problems within such
an assumption. First is the denial of the role of context in the way objective knowledge is
interpreted and translated when recontextualized in different settings. Second is the neglect of
the competition inherent in data-driven curriculum policy making. Ranking lists and league
tables not only present objective knowledge but also exercise a power of their own, turning
nations and individuals into competitors, not collaborators. As shown by Addey et al. (2017),
the rationales for participating in ILSAs are multiple: providing evidence for education
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policy, legitimating national politics, supporting the development of national assessment
systems, and strengthening international relations. Although nations participate for different
reasons, I argue that economic competition is a fundamental principle underpinning most
other reasons, not the least because the OECD has become the global hub of data production
and use within education (Lingard & Sellar, 2016). As shown by Tröhler (2014), the initial
urge to develop decontextualized indicators grew out of the highly competitive relationship
between the United States and the former Soviet Union, particularly the U.S. crisis following
the launch of the Soviet satellite Sputnik on October 4, 1957. Losing the space race against
the Soviet Union led to calls for the standardization, centralization, and scientification of the
U.S. educational system to reclaim superiority. Hyman George Rickover, father of the U.S.
nuclear navy, even described education as “the first line of defence” (Rickover, 1959 cited in
Tröhler, 2014, p. 21). Tröhler (2014, p. 3) explained that in the wake of Sputnik, “the Cold
War became thoroughly educationalized,” or expressed differently, education became a
means for international and national competition. Over the past couple decades, the
competitive discourse of the Cold War has transformed into an economic slogan. The battle to
be won takes place in the global economy with its flexible labor market, rather than space, but
is still governed by the same competitive rationale. Against this background, talk of mutual
policy learning and sharing best practices seems unrealistic because competition seems to
foster protectionism rather than generosity, distancing nations from each other rather than
uniting them.
The competitive rationale also operates at the relational level. Addey et al. (2017)
reported on an interview with Andreas Schleicher, OECD director of education and skills,
who asserted that all countries need to measure the skills of their citizens because they are all
competing in a global market. In the context of data-driven curriculum policy making, nations
and people are placed against each other in competitive relationships expressing a “horse-race
mentality” (Kamens, 2013, p. 117). Although claiming objectivity and disinterestedness, data-
driven curriculum policy making appears to be highly normative because a limited number of
powerful international organizations, such as the OECD, have the privilege of dictating the
educational agenda and advocating the preferred version of education ideally to be adopted by
all countries and individuals. This is a version of education designed according to an
evaluative rationale, telling the truth about the past. To maintain such an illusion of
objectivity, relational distance has to be advocated at all educational levels. However, as
pointed out by Biesta (2010, p. 500; italics in original), education can never be all about the
past as “education is a teleological practice—a practice framed by a telos: an aim or purpose.”
The argument raised here is that “distance,” despite claims of teleological disinterestedness,
operates as such a telos within data-driven curriculum policy making. Distance is
simultaneously seen as a prerequisite for any educational practice to be valuable (in terms of
evaluation) and a purpose for any educational practice to be meaningful (in terms of
evaluation). The telos of data-driven education thus seems to be evaluative rather than
educational.
In these circumstances, the critical task for educational research lies not only in the
challenge to develop better, more nuanced tools facilitating data-driven curriculum policy
making but also in the need to introduce concepts opening up for discussions on alternative
educational futures. Non-affirmative education theory calls policy makers, politicians,
educators, and students to self-activity that sees the qualitative aspects of education and
human experience as equally important as numerical data to exploring the complex,
multifaceted phenomenon of education. It is an invitation to participate, to make use of one’s
own freedom. “In terms of school leadership, the act of invitation or summons is directed
towards teachers’ and students’ potentiality and forces the latter to become aware of his own
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freedom as a cultural and political being and ability to realize his own aims” (Uljens &
Ylimaki, 2015, p. 36). Non-affirmative education theory thus reminds us that international
organizations have no exclusive or divine right to dictate educational aims. Instead, this
theory argues, what is of educational value lies in the mutual exploration of such aims, not in
pushing through ready-made answers. It is the question and the continued engagement with
what is different and unfamiliar that invites reflexive communication and calls for educational
actors at all levels to make use of their freedom to think and decide themselves what a
desirable future could be.
Concluding remarks
In this article, I have raised critical arguments about some ways in which distance as a
fundamental neoliberal idea has come to influence all levels of curriculum policy making
today, guided by an evaluative rationale. This development has led to the continuously
expanding use of summative assessments tools and thus the production of comparable data,
which I have referred to as data-driven curriculum policy making. Adopting a
multidimensional approach to curriculum policy making has enabled critical examination of
how the idea of distance has been discursively constructed and acted out within and between
different policy levels and the ways in which it has come to shape and reshape the educational
imagination and the actions of the actors operating at these different levels. Common to all
levels of policy making is a general shift from accountability for intentions to accountability
for results. The main responsibility of educational actors, irrespective of policy level,
concerns what comes out of schooling, or its measurable results, and not what is put in, or the
processes taking place. It is the summative outcomes, presented in ranking lists and league
tables, that decide whether anything educationally valuable has taken place.
This problematic development has gained a fair amount of attention among critical
scholars in education, who have deconstructed the asymmetric policy discourses and their
underlying power relations and have raised normative arguments for alternative perspectives
and counter-discourses. However, to contribute to a more productive critique, I have argued
for the need for a more reflexive position understanding educational aims as invitations, as
starting points rather than as predetermined ends. Being productive in this sense means that
educational aims never can be entirely decided in advance but have to emanate from a
communicative interaction enabling different educational aims and interests to coexist.
Turning to non-affirmative educational theory has offered a conceptual repertoire enabling
such a reflexive position, offering direction without prescribing ready-made solutions decided
in advance. Developing a productive critique of data-driven curriculum policy making from
this perspective, therefore, means inviting a broader discussion of what education can be all
about and what alternative scenarios in terms of educational futures are at hand. Doing so can
enrich the educational imagination instead of replacing one narrow interpretation with
another, and start replacing distance with interaction.
Notes
1 andreas.nordin@lnu.se
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mailto:andreas.nordin@lnu.se
Nordin. The Idea of Distance in Data-driven Curriculum Policy Making 35
Transnational Curriculum Inquiry 15 (2) 2018
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Submitted: October, 30, 2018
Approved: November, 16, 2018
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