WHAT ARE THE ODDS OF BEING THE BEST VERSION OF YOURSELF?

Last year I wrote a conference article called Be the best version of yourself! OECD’s promises of welfare through school mathematics. There, I analyzed the circulating discourses regarding what is important for citizens “to know” and “be able to do” as expressed in the report PISA 2012 results: What students know and can do, released by OECD in 2014. In which, the reading of 2012 PISA’s outcomes “takes an important role, given that it reveals how discourses about numeracy proficiency have been entangled and displayed for the making of a productive citizen for society” (^{ANDRADE-MOLINA, 2017a}, p. 394).

I built the argument around how OECD has positioned itself as one of the main networks enabling some discourses to circulate as dominant narratives on education towards economic progress. For example, OECD’s amplification and expansion of peripheral policies as stated by ^{Tröhler, Meyer, Labaree, and Hutt (2014}, p. 2): “[p]olicies that might have a hard time becoming accepted in local contexts seem that much more irresistible when offered as uncontested consensus of the world’s leading democracies”. By doing so, I troubled the productive citizen portrayed by OECD—the “well-equipped-citizen”—and the double gestures that produce processes of exclusion within the intention of including “all” students, or abjection in terms of ^{Popkewitz (2008)}. In other words, how the portrayal of the “well-equipped-citizen” entails the portraying of the low-performer—the abnormal child in need of “social administration, intervention, and salvation” (^{BLOCH, HOLMLUND, MOQVIST & POPKEWITZ, 2003}, p. 15). From this analysis,

OECD discourses normalize and regulate whom the productive citizen is, how the productive citizen should be and should act. To be a productive citizen means students should engage in practices to conduct their own conduct to achieve the ‘well-equipped state’, a will of fitting in the “all” and becoming a lifelong learner. But it also means to recognize in school mathematics an opportunity to reach welfare… All in the name of economic growth! (^{ANDRADE-MOLINA, 2017a}, p. 399).

In going further with the discussion, here I want to trouble the dominant discourses around the “low-performer” described by OECD. The report released in 2016, Low-performing students. Why they fall behind and how to help them succeed, explores the many factors that could explain students underachievement in PISA. As “this report provides the first comprehensive analysis of the problem and how it can be tackled” (^{OECD, 2016}, p.3). In such document, ^{OECD (2016}, p. 42) sates that all countries that have participated in PISA 2012, “even those with the highest performance and equity outcomes, have a sizable share of low performers” (see Figure 1.5 in ^{OECD, 2016}, pp. 43-45). The low-performer, also called poor-performer, is often characterized as disadvantaged children in terms of factors that make them at higher risk of failing. In this article, I content that the low-performer is fabricated under a rationality in which numbers are

Thought as a social technology that instantiate consensus and harmony in the world… All kinds of quantified knowledge are in that respect artificial through creating uniformity among different qualities of things, uniformity that gives social authority to the interrelation of science and policy. (PETTERSSON, POPKEWITZ & LINDBALD, 2016, p. 184).

For doing so, it is necessary to deconstruct the document, and start locating the taken-for-granted truths to map how they circulate in a discourse aimed at making kinds of people. Elsewhere I have argued that the low-performer is fabricated to be low-performer (^{ANDRADE-MOLINA, 2017b}). Here the analytical movement allows troubling the “common sense” assumptions by adding a pinch of Deleuzian nonsense (^{Deleuze, 1990}).

It is urgent to get this right

Far too many students around the world are trapped in a vicious circle of poor performance and demotivation that leads only to more bad marks and further disengagement from school… More than one in four 15-year-old students in OECD countries have not attained a baseline level of proficiency in at least one of the three core subjects PISA assesses… About 13 million 15-year-old students in the 64 countries and economies that participated in PISA 2012 were low performers… This report sets the bar at a very basic level of performance that we should expect all young people in the 21st century to attain… It is education policy and practice that can help students clear this bar, not just per capita income… It is urgent to get this right. Poor performance at school has long-term consequences for both individuals and nations. Students who perform poorly at age 15 face a high risk of dropping out of school altogether; and when a large share of the population lacks basic skills, a country’s long-term economic growth is severely compromised. In fact, the economic output that is lost because of poor education policies and practices leaves many countries in what amounts to a permanent state of economic recession… For lower middle-income countries, the discounted present value of economic future gains from ensuring that all 15-year-olds attain at least the PISA baseline level of performance would be 13 times the current GDP and would average out to a 28% higher GDP over the next 80 years. For upper middle-income countries, which generally show higher learning outcomes, the gains would average out to a 16% higher GDP. In other words, the gains from tackling low performance dwarf any conceivable cost of improvement. (^{OECD, 2016}, pp. 3-4, emphasis added).

As explored previously (^{ANDRADE-MOLINA, 2017a}), the low-performer becomes a ‘threat’ for the economic growth of nation. There exists even the danger of succumb into a permanent state of economic recession due to poor education policies and practices. And when the stability of a country/economy—in alignment with OECD’s terminology—is at risk of being compromised… then it becomes URGENT TO GET THIS RIGHT! Which is expressed in terms of the average future GDP gains from ensuring students’ performance at least at PISA baseline level. Then, just as simple as that, education becomes an investment not only for families to achieve a state of individual welfare, but for countries/economies to avoid long-term economic consequences: the gains from tackling low performance dwarf any conceivable cost of improvement. Which roughly translates in: it does not matter how expensive it is, we would see GDP gains in the near future; we should avoid risks at all cost!

To avoid the risks factors that, in fact, have been previously calculated regarding preceding PISA’s outcomes and following a number of correlations,

The policy agenda to tackle low performance needs to include multiple dimensions, such as: creating demanding and supportive learning environments; involving parents and local communities; inspiring students to make the most of available education opportunities; identifying low performers and providing targeted support for students, schools and families; offering special programmes for immigrant, minority-language and rural students; tackling gender stereotypes; and reducing inequalities in access to early education and limiting the use of student sorting. (^{OECD, 2016}, p. 3, emphasis added).

OECD shows a need to avoid low-performers: “Reducing the number of low-performing students is not only a goal in its own right but also an effective way to improve an education system’s overall performance – and equity…” (^{OECD, 2016}, p. 13). To tackle this issue, OECD proposes to look at all the factors that can affect students’ achievement, “…since low performers are disproportionately from socio-economically disadvantaged families” (Op cit. p.13).

Brazil, Germany, Italy, Mexico, Poland, Portugal, the Russian Federation, Tunisia and Turkey, for example, improved their performance in mathematics between 2003 and 2012 by reducing the share of low performers in this subject. What do these countries have in common? Not very much; as a group, they are about as socio-economically and culturally diverse as can be. But therein lies the lesson: all countries can improve their students’ performance, given the right policies and the will to implement them. (Op cit. p.13, emphasis added).

But, if the path to improvement is not directly dependent on socio-economic and cultural differences, as revealed in the example above, then… why looking at the students’ cultural and socio-economic differences to decreasing underachievement? It is often natural in OECD’s analysis to explore what is common in top or high achievers and to explore what differentiates low or under achievers and top or high achievers.

Just like the example OECD is using to display successful practices, there exist, what they call, resilient students that are the ones overpassing their disadvantages and score above of what is expected—according to OECD’s calculations: “6% of students across OECD countries are considered “resilient” in that, while they are disadvantaged, they manage to beat the odds against them and perform among the top quarter of students in PISA” (^{OECD, 2016}, p. 63). However, what resilient students do to beat the odds against them is not part of the final correlations in the report. As OECD expresses, their analysis “show that poor performance at age 15 is not the result of any single risk factor, but rather of a combination and accumulation of various barriers and disadvantages that affect students throughout their lives” (^{OECD, 2016}, p. 13). This reveals that reality is much more complex, and that diverse factors can interact, even factors that are not yet considered to set the profile of the low-performer. So, let’s explore more in depth the OECD’s profiles of the top- and low-performer.

The advantaged versus the disadvantaged

“Who is most likely to be a low performer in mathematics?” (^{OECD, 2016}, p. 13). As straightforward as may seem, OECD begins by posing this question. We can think about all stereotypes we have acquired by being subjected to and submerged in all these types of dominant discourses. If you thought of a girl instead of a boy, or an immigrant child, or second language learners… you are absolutely correct (but only regarding OECD’s interpretation of a low-performer).

On average across OECD countries, a socio-economically disadvantaged girl who lives in a single-parent family in a rural area, has an immigrant background, speaks a different language at home from the language of instruction, had not attended pre-primary school, had repeated a grade, and is enrolled in a vocational track has an 83% probability of being a low performer. (^{OECD, 2016}, pp. 13-14).

On the contrary,

A student of average socio-economic status who is a boy living in a two-parent family, has no immigrant background, speaks the same language at home as in school, lives in a city, attended more than one year of pre-primary education, did not repeat a grade and attends a general curricular track (or school) has a 10% probability of low performance in mathematics. (^{OECD, 2016}, p. 62).

And if we improve the socio-economic status of the, from now on, disadvantaged student, then,

A student with the same socio-economic status [average] but who is a girl living in a single-parent family, has an immigrant background, speaks a different language at home than at school, lives in a rural area, did not attend pre-primary school, repeated a grade and attends a vocational track has a 76% probability of low performance. (^{OECD, 2016}, p. 62).

From these three profiles, it is possible to assume that socio-economic factors play a key role, as well as gender, background, language, location, etc. And so, our differences as human beings coming from diverse backgrounds are not advantages, they are not even desirable in the classroom for diversity and multiculturalism but they are taken to be risk factors and disadvantages. In other words, we become segregated, even from the beginning as a ‘threat’ for the economy of the country/economy regarding bias correlations. Here, education policies aim at homogenizing the heterogeneity in the classroom (^{TRÖHLER, ET AL., 2014}), to minimize the risk.

While these background factors can affect all students, among low performers the combination of risk factors is more detrimental to disadvantaged than to advantaged students. Indeed, all of the demographic characteristics considered in the report, as well as the lack of pre-primary education, increase the probability of low performance by a larger margin among disadvantaged than among advantaged students, on average across OECD countries. Only repeating a grade and enrolment in a vocational track have greater penalties for advantaged students. In other words, disadvantaged students tend not only to be encumbered with more risk factors, but those risk factors have a stronger impact on these students’ performance. (^{OECD, 2016}, p. 13, emphasis added).

And yet, advantaged students are also at risk of performing lower than average by factors such as repeating a grade and the enrollment in a vocational track but it is less statistically significant for the making of the report. So, they are not considered as part of the potential low-performers. Let’s jump into the risk factors.

A risky diversity

As overly mentioned above, OECD states a multiplicity of risks factors—the disadvantages—that might explain students’ underachievement in PISA. The profile of the disadvantaged student has been set already for you to get a general idea of what are these dangerous elements (see Figure 2.1, ^{OECD, 2016}, p. 63).

There are three main “potential areas of risk”: socio-economic status, demographic background, and progress through education. Each of which have sub-areas and, consequently, each sub-area has risk factors within them. The risk factors are: socio-economic disadvantage, being a girl in mathematics, being a boy in reading and science, immigrant background, different from mainstream language, school in a rural area, single-parent family, no pre-primary education, repeated at least one grade, enrolled in a vocational track. What does OECD has to say about each risk factor and its correlation with the low-performer?

Socio-economic background: OECD clearly states that students’ background both social and demographic “do not determine student achievement, but they do create the conditions for opportunities” (^{OECD, 2016}, p. 62). In this light, OECD is able to correlate socio-economic background disparities with student’s performance.

While low performers come from all socio-economic backgrounds, they are disproportionately disadvantaged… On average across OECD countries, 37% of disadvantaged students are low performers in mathematics, compared to nearly 10% of advantaged students. (^{OECD, 2016}, p. 65, emphasis added).

Gender: OECD states from their correlations that “boys are at greater risk than girls of low performance in reading and in science”. But, “in many countries/economies [not in all of them], girls are at greater risk than boys of low performance in mathematics”. (^{OECD, 2016}, p. 69). This enables OECD to put attention on gender differences that might play a role in students’ achievement, such as anxiety.

Boys are significantly more likely than girls to be disengaged from school, get lower marks, to have repeated grades, and to play video games in their free time… [G]irls tend to behave better in class, get higher marks, are less likely to repeat grades, spend more time doing homework, and read for enjoyment…. But girls are less likely than boys to believe that they can successfully perform mathematics and science tasks at designated levels (low self-efficacy), are more likely than boys to feel anxious about mathematics. (^{OECD, 2016}, pp. 67-68, emphasis added.)

Migration status: OECD states that on average “the gap in mathematics performance between immigrant students and students without an immigrant background was as large as 34 score points – the equivalent of nearly one year of formal schooling” (^{OECD, 2016}, p. 71). However, not all students of immigrant background have the same odds of being a low-performer.

Immigrant students who have spent more time in the country of destination (“early arrival”) tend to perform better than those who have spent less time (“late arrival”); second-generation immigrant students tend to perform better than first-generation students; and students who belong to immigrant communities that are larger and more socio-economically diverse tend to perform better than those coming from smaller and more homogeneous and marginalised communities. (^{OECD, 2016}, p. 71).

Language spoken: Language becomes less statistically significant that other factors, yet, it can be correlated when considering all factors together.

In 17 countries… speaking a different language at home increases the likelihood of low performance even after accounting for other variables, but in 4 countries and economies, speaking a different language at home reduces the chances of low performance. In 16 other countries and economies, statistically significant differences become insignificant after accounting for the other variables, thus factors other than language at home explain the differences in performance. (^{OECD, 2016}, p. 73, emphasis added.)

Family: On average across OECD countries, “26% of students in single-parent families performed below the baseline level of proficiency in mathematics in PISA 2012, while nearly 20% of students from two-parent families performed at that level”. (^{OECD, 2016}, p. 76), However, it becomes interesting how family composition is a factor correlated to low-performance regarding that

After accounting for students’ socio-economic status and other background characteristics, those odds shrink to 1.2. In 16 countries and economies, this greater likelihood is statistically significant after accounting for other student characteristics. In 27 countries and economies, the difference in likelihood becomes insignificant after accounting for other variables. There is no country or economy in PISA 2012 where students from single-parent families are less likely to be low performers than students from two-parent families. (^{OECD, 2016}, p. 76, emphasis added.)

Location: According to OECD’s correlations, rural areas host the largest share of low-performers compare to urban areas. “There is a clear relationship between the share of low performers and geographic location… 29% of students who attend school in rural areas and 21% of students in cities or towns perform below Level 2 in mathematics” (^{OECD, 2016}, p. 80).

After accounting for other characteristics of student background (i.e. socio-economic status, gender, immigrant and language background, family structure, attendance at pre-primary school, grade repetition and programme orientation), differences in the likelihood of low performance related to geographic location shrink, but remain significant in 24 countries and economies. (^{OECD, 2016}, p. 80).

Pre-primary education: Having into account OECD’s correlations, this is one of the main risk factors that can help predict low performance. And socio-economic status is attributed “for a large part of the variation in the relationship between pre-primary education and low performance” (^{OECD, 2016}, p. 85).

41% of students without any pre-primary education performed below the baseline proficiency level in mathematics… 30% of students who had attended pre-primary education for less than a year, and 20% of students who had attended pre-primary education for more than one year performed at that level… The odds of low performance in mathematics for a student with no pre-primary education are 3.3 times higher than the odds for a student who had attended more than a year of pre-primary educations before accounting for other student characteristics, and 1.9 times higher after accounting for them. (^{OECD, 2016}, p. 80).

I think the point of how OECD takes each risk factor has been addressed and, therefore, we can move to trouble and disorder this readings and correlations made, instead of going through each one of the factors left.

The risky correlations

It becomes necessary to challenge the reliability on numbers and statistical comparisons. The disadvantaged, low-performer student comes, first and foremost, from low-income families.^{Duncan and Magnuson (2012)} have argued about the volatility of economic stability, given that it depends on family income, size, and on inflation: “We speak easily of “the poor” as if they were an ever-present and unchanging group, a well-defined economic group” (Op. cit., p. 79). As an example,

Students whose parents have higher levels of education and more prestigious and better-paid jobs benefit from accessing a wider range of financial (e.g. private tutoring, computers, books), cultural (e.g. extended vocabulary, time management skills) and social (e.g. role models and networks) resources that make it easier for them to succeed in school, compared with students from families with lower levels of education or from families that are affected by chronic unemployment, low-paid jobs or poverty. (^{OECD, 2016}, p. 63).

Not all factors considered “risky” are as volatile as socio-economic background, location, and family composition. Some are permanent, unchangeable, like the immigration factor. Nonetheless, according to the correlations made by OECD:

Low performers tend to be more prevalent among immigrant students than among students without an immigrant background; yet there are countries where this is not the case, and still others where the opposite is true… in Australia, Israel, Jordan, Macao-China, Montenegro, Qatar, Singapore and the United Arab Emirates, immigrant students perform better than students without an immigrant background. (^{OECD, 2016}, p. 71, emphasis added).

This means that, in some countries/economies, being immigrant is actually a positive factor, an advantage. And so, instead of looking at the positive experiences and celebrating diversity as a benefit from which we can learn about how to “better” educate children, OECD tends to look at the average by comparing completely different backgrounds and cultures as if they were homogeneous. Posing solutions as: “Attending pre-primary education, for example, is a positive experience that puts potential low performers on a better track” (^{OECD, 2016}, p. 62). OECD’s arguments are on how to minimize disadvantages, rather than asking what resilient students did; did they attend pre-primary schools to overcome their disadvantages? Moreover, all correlations made by OECD about the low-performer assume that students have the desire to learn and are excited to be part of the school system but submerged on risk factors that are disabling them to reach success.

The dominant discourses in OECD’s reports show that advantaged students have significantly lower risk of underachievement when compared with disadvantaged students. Even to the point of being statistically insignificant. However, there is a likelihood of performing under the baseline level for those who are not taken as a threat to economic growth.

On average across OECD countries, a student with a low-risk profile who comes from a disadvantaged family has a 17% probability of low achievement in mathematics, whereas a student who comes from a socio-economically average family has a 10% probability, and an advantaged student has a 5% probability of low performance in mathematics. (^{OECD, 2016}, p. 92).

On average across OECD countries, a student with a high-risk profile who comes from a disadvantaged family has an 83% probability of low achievement in mathematics, compared with a 76% probability for a student who comes from a socio-economically average family and a 64% probability for an advantaged students. (^{OECD, 2016}, p. 93).

There is one more risky correlation. I assume by now that you were able to see it, and it was not intentional from my part to select only the correlations of students’ mathematics performance. In fact, in the spirit of being statistically significant, it is interesting how OECD decided to select all correlations regarding students’ performance in mathematics. Even though in reading and science gender gap, to name one, was completely the opposite, where girls performed better than boys (see Figure 2.4, ^{OECD, 2016}, p. 68). Which means that the disadvantaged students at risk of being a low-performer in reading and science is a boy. It is not manipulation of the information! But it is more statistically significant to talk about the correlations in mathematics than in language and science. OECD carefully place words such as: “less likely”, “more likely”, often, tend to, it is possible that, might be correlated with, may partially account, the likelihood of, are more likely to, and so on, to not inferred causation. Otherwise, it would not be possible to state that

If reforms were implemented today to raise the level of all low-performing students to baseline proficiency in reading, mathematics and science, the long-term economic gains for OECD countries would cover most, if not all, of the cost of these countries’ education systems. Among middle-income countries, many of which also participate in PISA, the economic gains from achieving universal basic skills would average more than eight times their current GDP. (^{OECD, 2016}, p. 35).

Disordering and nonsense

Up to this point, you have your own opinion about correlations, about risk factors, about what entails disadvantages in the classroom and for economic gains. But I promised, in the very beginning, a Deleuzian nonsense to trouble the dominant discourse about the low-performer. What I did was to play with diverse factors and indexes to see if they can also be correlated with PISA’s outcomes. Unfortunately, some of them… are! Following a ^{Deleuze (1990)} inspired move, sense cannot be understood without nonsense. In this analysis, nonsense helps stretching the boundaries of what can be correlated. I took the World Happiness Report and OECD’s Better Life Index, to talk about factors ranging from income to percentage of people satisfied with the quality of the water.

The factors I looked at were: Happiness Index per country (2013-2015), percentage of renewable Energy, housing, income, jobs, percentage of people aged 15-64 with paid jobs per country, community, education, percentage of adults aged 25-64 that has completed upper secondary education, environment, civic engagement, health, life satisfaction, safety, work-life balance, life expectancy, and percentage of people satisfied with the quality of the water. Afterwards, I correlated them with mathematics, reading, and science mean score in PISA 2012 and the average mean score in PISA 2012 to evaluate if these correlations were also statistically significant.

As shown in the table above, OECD’s outcomes in PISA 2012 were correlated with some of the factors of Better Life Index and with the World Happiness Report 2013-2015. Taking into account the average mean score in PISA 2012, it was possible to correlate as statistically significant at the 0.05 level (p<0.05): housing, environment, and work-life balance. It was possible to correlate as highly statistically significant (p<0.01): income, jobs, percentage of people aged 15-64 with paid jobs per country, education, percentage of adults aged 25-64 that has completed upper secondary education, safety, life expectancy, and, to my surprise, percentage of people satisfied with the quality of the water.

It appears that many factors can affect PISA scores, and the reliability in numbers is still much abstract that the complexity of real life. As ^{Rose (1999}, p. 199) argues, numbers encapsulate specific choices made about “what to measure, how to measure it, how often to measure it and how to present and interpret the results”, that enables us to capture sense out of nonsense.

The growth of scientific statistics as a dominating reasoning creates beliefs in that the more data we gather and the more comparisons we make – the more will we know. This use of comparisons and data within statistics carries a number of presuppositions: that reality can be represented in numbers, that it can be controlled and that risks can be managed. (^{PETTERSSON ET AL., 2016}, p.182).

This correlations, readings, and dominant discourses have effects of power in making kinds of people, by having effects of power in fabricating students’ subjectivities. As low income families report to develop feeling of fear and frustration that “rest on their perception that they do not have the adequate resources or networks to succeed, especially once they move on to assume responsibilities as adults” (^{CAVIERES, 2011}, p. 116). In this regard, “exclusion creates among low-income groups a feeling of fatalism according to which nothing they can do or plan will allow them to improve their economic position “ (Op. cit. p. 116). Elsewhere (^{ANDRADE-MOLINA, 2017b}, p. 17) I have argued that “the low-performer is problematic not because students are failing, but because they are fabricated to be low-performers”. Disadvantaged students

are aware that, compared to students from other schools coming from wealthier neighborhoods, they have much less possibilities for succeeding in life. As a result, they do not feel committed either to their studies or their high schools, creating in them a sense of hopelessness that they will not be able to go on to higher education or be hired in well-paid jobs. (^{CAVIERES, 2011}, pp. 123-124).

In this regard, the underachiever is fabricated as a low performer because it cannot escape of all the factors that are considered to be detrimental for future success and national economic growth. The child with accumulated risk factors is subjectified as a danger and a risk for society. Although, from this statistical analysis it is possible to argue that in order to increase future scores in PISA it would be as simple as improving the quality of the water among underachievers.