2.1. Approaching algorithms as sociotechnical artifacts

To open algorithms with a socio-technical approach, we can turn to Science and Technology Studies (STS). Indeed, opening black boxes is a prominent approach in STS. In a seminal article, Langdon Winner (Reference Winner1993) writes that ‘the term black box in both technical and social science parlance is a device or system that, for convenience, is described solely in terms of its inputs and outputs.’ This echoes some of his earlier work in which he underlines how technologies can be political (Winner, Reference Winner1986), both through the values instilled in them during their making and through the social practices required to make them work. Black boxes can thus be considered as placeholders for complex and opaque systems, but this does not mean that they are unopenable.

One way to open them is to focus on the way they are challenged through controversies. This is the hallmark of the actor network theory (ANT) developed by STS scholars Madelaine Akrich, Michel Callon and Bruno Latour (Akrich, Callon & Latour, Reference Akrich, Callon and Latour2006). With their approach, they suggest focusing on the way network of actants – both humans and non-humans – are built, maintained, and changed through various negotiations. Doing so, they encourage researchers to identify obligatory passage points through which the objectives of all the actants Footnote ¹ meet. To open black boxes, they argue that we should study the way actants are changed, or translated, through various negotiations to become an assemblage, a temporary stabilisation of a network where every actant finds its interest. Studying controversy around black boxes is thus a way to identify what is necessary for such network to stay in place.

Then, Akrich (Reference Akrich1987) argues that as part of this negotiation process, innovators inscribe their own vision into their end product. The inscription leading to the implementation of a script within a socio-technical system, a framework of action defined by its makers but also by the space in which it is put in place. In other words, the script of an object consists of what it prompts users to do, regardless of how it was intended to be used by its makers. The work of de-scription, focusing on the mechanisms of adjustments between users as imagined by the makers and real-world users, then become crucial to identify the script of a socio-technical system. Finally, Akrich (Reference Akrich1987) adds that as technological objects stabilise, they are turned into instruments of knowledge. If a sociotechnical system successfully becomes a black box, it is then able to transform sociotechnical facts – resulting from various negotiations – into facts that are perceived as true de facto.

2.2. Approaching the OfQual algorithm in practice

Building on this literature from STS, some authors started to open the black boxes of algorithms with a socio-technical perspective, paving the way for critical algorithms studies. Authors as Angèle Christin (Reference Christin2020) for example identifies how the opaqueness of algorithms is socially enacted and encourages researchers to study how algorithms produce a refraction of our societies by assembling a skewed reflection of it. Nick Seaver (Reference Seaver2017) proposes to see algorithms as being entanglements of social practices that are culturally enacted by people who engage with them. Consequently, algorithms are part of a cultural stream and not solely an artefact interacting with it anymore. In practice, Seaver’s (Reference Seaver2017) approach suggests that algorithms should thus not be seen as apart from society as it is not possible to isolate them from their societal context. Others such as Rob Kitchin (Reference Kitchin2017) propose ways to engage with them in practice by dismantling piece by piece the socio-technical assemblage surrounding algorithms.

As a data collection strategy, Seaver (Reference Seaver2017) suggests practising what he calls scavenging by collecting secondary and sometimes tertiary sources to grasp the algorithms that are often hidden behind complex codes or secrecy from their developers. Doing so, one should not reject resources based on their intelligibility. Indeed, the inaccessibility (both in term of difficulty to comprehend, but also to access in the first place) of sources is also a part of the analysis as it is a major aspect of black boxes (Christin, Reference Christin2020). Collecting codes, gargantuan datasets, and specialists’ reports is thus also part of such research. These resources in turn allow researchers to deconstruct the code step by step, by including all comments and documentation available (Kitchin, Reference Kitchin2017). It is these scavenged resources that in turn grant access to the identification of the step-by-step process that transform an input into an output within an algorithm. The study of said process allowing us to engage critically with the algorithm to identify how their script is imbued with certain values.

In the case of OfQual, as it was part of a public policy in order to overcome the cancellation of exams, there was a commitment to be as transparent as possible in order to build confidence in the system from the public (OfQual, 2020a). As a result, a lot of documents were produced explaining the choice made by OfQual and the inner workings of their algorithm. Most of the analysis will thus be built on the literature made available by OfQual, as well as a parliamentary inquiry on the impact of COVID-19 on education and children’s services (2020). Both those resources regrouping perspectives from various stakeholders (developers, executive directors, students, teachers, etc.). With those, I will retrospectively rebuild, step by step, the different negotiations that happened while making and implementing the algorithm. Following on classical STS literature (Akrich, Reference Akrich1987; Bijker, Reference Bijker1995; Callon, Reference Callon1984), I will pay particular attention to how the different negotiations happened within the making and implementation of the system that led to its contested outcome. Following Bloor’s (Reference Bloor1976) principle of symmetry – that encourages us to avoid studying a past controversy following a discourse a posteriori – I will avoid identifying the OfQual algorithm as being neither right nor wrong. I will thus avoid perceiving it as being inherently unfair against some, but I will instead identify how different discourses around fairness emerge in its making.

2.3. Fairness in algorithms

The STS perspective offers great possibilities for approaching algorithms as socio-technical artefacts, imbued with values derived from their production. But in order to understand how the notion of fairness is approached in the creation of the algorithm in question here, we need to take a brief detour into theories of fairness in algorithms.

As STS scholar Bilel Benbouzid (Reference Benbouzid2022) points out in his analysis of Fair Machine Learning (or FairML) as a field of study, the ways in which fairness is considered have evolved over time, with sometimes conflicting views. He highlights a tension within FairML between the methodological realism of engineers and the constructivism of social scientists. In the former case, algorithms are seen as capturing an objective reality or ground truth, echoing the work of STS scholar Folrian Jaton and Bowker (Reference Jaton and Bowker2020). In the constructivist approach, engineers themselves define the criteria for what is ‘fair,’ using metrics and formal rules. As Benbouzid (Reference Benbouzid2022) argues, the field of FairML offers a rare case where the two approaches coexist in a form of applied realism.

Having identified this tension, he then explores different, sometimes conflicting, measures to account for different approaches to fairness. First, statistical fairness, to ensure that different groups are treated equally. Then procedural fairness, which ensures that decision-making processes follow certain transparent and fair rules. Finally, individual fairness, which seeks to treat similar individuals in a similar way.

At another level, authors such as Melanie Smallman (Reference Smallman2022) show that there are conflicting accounts of fairness due to the multiple levels at which fairness is defined. Looking at existing frameworks for regulating ethis in AI in welfare, she argues that ‘a rights-based approach has focused our gaze on the individual’ (Smallman (Reference Smallman2022, p. ;13) overcome this shortcoming, she suggests looking at fairness from different perspectives). She suggests six scales: individual, group and community, institutional, national, global and over time. Approaching fairness from this multi-level perspective allows us to better anticipate the far-reaching consequences of the implementation of algorithms in our societies.

Authors such as Benbouzid (Reference Benbouzid2022) and Smallman (Reference Smallman2022) thus offer great accounts of how fairness can be a contested matter within critical algorithms studies. However, in order to explain these conflicting accounts of fairness in the development of algorithms, I will build on the work of STS scholars Selbst et ;al. (Reference Selbst, Boyd, Friedler, Venkatasubramanian and Vertesi2019) to highlight how the OfQual case demonstrates different accounts of fairness in the development of algorithms. In their work, they identify five key moments in algorithm development that developers can fall into. Traps that lead to abstraction errors, where the reality represented by an algorithm is no longer true to its ground truth. These five moments are presented as traps that developers can fall into.

First, the solutionism trap, which invites developers to consider when to design and whether an algorithm is needed at all. The second is the ripple effect trap, which asks us to consider the specifics of each scenario when implementing automated systems. The third is the formalist trap, which calls to avoid reinforcing existing inequalities or introducing new ones. The next is the portability trap, which reminds us to include the perspectives of all relevant social groups in each situation. Finally, the framing trap invites developers to consider the whole system into which the algorithm will fit.

In this paper, I will begin by opening the black box of the OfQual algorithm by identifying how various negotiations happening in its making and implementation shaped its script. Following on Seaver’s (Seaver, Reference Seaver2017) technique of scavenging, I will build on reports made by OfQual and the government, complementing those with interviews with OfQual developers. Then, I will identify how the different accounts of fairness are intertwined throughout the five moments identified by Selbst et ;al. (Reference Selbst, Boyd, Friedler, Venkatasubramanian and Vertesi2019) to underline the script (Akrich, Reference Akrich1987) of the algorithm.

3.1. Education in England

To better understand the context in which the algorithm was implemented, I first propose to trace the path of a grade assignment through the English educational system in a year without the algorithm. This will then allow us to understand the steppingstones on which the OfQual algorithm was built, and how the algorithm introduced in 2020 has reshaped the assessment pathway.

The first set of individuals we encounter are the students being assessed. In this case, these are all the students who took their A-Levels in England in the year 2020. A-Levels are exams taken at the end of secondary education by students in England. Typically, students choose between two or three A-Level subjects (i.e. mathematics, geography, and economics) in a centre of their choosing, depending on the requirements of the colleges they wish to attend. This means that around 700,000 A-Levels needed to be taken in 2020 for about 250,000 students by summer 2020 (OfQual, 2020a). The A-Levels, once passed and graded, are used by students to access their desired programs within higher education, the highest the better. The grade ranged from A+ to E, with the grade U (for ungraded) given to students depending on the percentage of students reaching each mark in their exams (the top 10% receive A+, top 20% gets A, then B and so on and so forth).

The next actors in the grading process are the teacher. Their aim is to prepare students for their final exams through teaching and assessment throughout the year. These teachers are part of schools and colleges, which OfQual calls ‘centres’ (OfQual, 2020a) as the term better encompasses the wide variety of educational formats in England. A student wishing to take an A-level will therefore join a centre that offers A-levels in the subject they wish to take. Each centre is run by a headmaster, who played an important role in approving the CAGs for the 2020 evaluation (OfQual, 2020a).

Next in the graduation process are the exams boards, which each centre asks to provide certified A-Level exams. There are currently five exam boards in England: AQA, OCR, Pearson, Cambridge and WJEC. In many cases, the different boards offer A-Levels in the same subject, with centres having to choose between different boards to provide them with A-Level assessments. According to OfQual (2020c), centres may choose one board over another based on the form of the assessment, the support service or the specifications of each centre.

Each exam board provides the requested examinations and ensures the correction of the said examinations. They then provide students with their grades via a standardised national platform. For education experts Denis Opposs & al. (2020), the exam boards in the English educational system are typical of what they call a quasi-market system. According to them, the delegation of assessment to private third-party organisations is based on the belief that competition between different boards will push them to raise the overall level of education, as each board wants to have the best performing students. The system has been contested in England by politicians from various backgrounds, but it is still in use to this day (Opposs et ;al., Reference Opposs, Baird, Chankseliani, Stobart, Kaushik, McManus and Johnson2020).

The list of subjects that exam boards are allowed to award is set by OfQual based on contents set by the Department for Education. OfQual is specific to England, and it is the only regulatory office for education under the direct authority of the UK government. Indeed, parliaments of Scotland, Wales and Ireland are responsible for their matters in education. Exam boards are a consequence of the peculiarities of the quasi-market approach promoted in England, as the other parliaments provide the qualification themselves without referring to third-party administrations (Opposs et ;al., Reference Opposs, Baird, Chankseliani, Stobart, Kaushik, McManus and Johnson2020). OfQual objective is to regulate the different exam boards to ensure comparability over time. It is a non-ministerial governmental organisation, which means that they must follow the recommendations of the Department for Education, but they can do so with a certain degree of freedom.

3.2. Settling for the algorithm

The English education system is one in which examinations are outsourced to independent organisations following a logic of competition, in our case examination boards. Such system lies on the belief that as each board wants to have the highest achieving students; they uplift the general level of education through competition between them (Opposs et ;al., Reference Opposs, Baird, Chankseliani, Stobart, Kaushik, McManus and Johnson2020). To maintain such system in place, grades must be comparable not only between different exam boards but also through time. As the OfQual algorithm was introduced in such a context, standardisation was an important aspect of its development.

Such constraint is acknowledged by the Department for Education who asked OfQual ‘that qualification standards are maintained, and the distribution of grades follows a similar profile to that in previous years’ (Williamson, Reference Williamson2020). This meant that OfQual had to come up with a way to standardise results for A-Levels to avoid them being too high or too low compared to the year-on-year average. To do so, OfQual put in place a process to smooth the CAGs so that the general grade distribution would be in line with historical distribution. To achieve their goal, they agreed on the following set of objectives that they presented in their interim report.

1. i. to provide students with the grades that they would most likely have achieved had they been able to complete their assessments in summer 2020

2. ii. to apply a common standardisation approach, within and across subjects, for as many students as possible

3. iii. to protect, so far as is possible, all students from being systematically advantaged or disadvantaged, notwithstanding their socio-economic background or whether they have a protected characteristic

4. iv. to be deliverable by exam boards in a consistent and timely way that they can quality assure and can be overseen effectively by Ofqual

5. v. to use a method that is transparent and easy to explain, wherever possible, to encourage engagement and build confidence” (OfQual, 2020a).

To achieve these goals, OfQual had different resources at hand. First, as predictions about the general average of students are made each year to predict the grade distributionFootnote ² (Kelly, Reference Kelly2021; Ozga, Baird, Saville, Arnott & Hell, Reference Ozga, Baird, Saville, Arnott and Hell2023), OfQual already had access to a lot of historical data about the history of each centre. Indeed, they knew the final grades for each student’s A-Level in the years before the algorithm as well as most of the results of the CGSEs of students in the 2020 cohort.

On top of that, OfQual announced in a report from the 3^rd of April 2020 (2020a) that they were going to ask centres to predict students’ grades and to rank them. To do so, centres were instructed to give a grade for each student for each A-Level and to rank students within each subject with the highest attaining student on top. Those rankings are the CAGs which I introduced earlier. They asked for these resources before knowing what they were going to do with them as they wanted to act in a timely manner.

Yet, they relied on the belief that the predictions of teachers would not be sufficient as they would be higher than grades students usually achieve. Indeed, OfQual (2020b) used previous studies on grade prediction which suggested that teachers tended to be less precise when asked to grade students in general (absolute accuracy) than when they were asked to sort students in comparison with one another (relative accuracy). They also used tests on their historic data to support that absolute accuracy was less optimal for their objectives (OfQual, 2020a). According to them, teachers were imprecise in about half of the cases. And most of those times, the teachers were too optimistic. Using those sources as a reference, OfQual argued that using grades predicted by teachers through absolute accuracy directly would have led to an inflation in grades for the year 2020. This was verified as most of the CAGs given by teachers were indeed above the national average. But to say it is only due to the wrong appreciation of teachers might be a hasty conclusion. Indeed, as teachers were aware that the grade they gave were going to be standardised, it might be possible that they raised their students’ grades above their expectations.

OfQual (2020) also argued that using CAGs directly would have led to inequalities between centres and students. As it was not possible for OfQual to properly form teachers to produce accurate standards in such a timely manner, there would be too many disparities between centres and variation between years would be too high and lessen the values of higher grades for the 2020 cohort. This led OfQual to consider a way to standardise the results given by teachers through the CAGs to fit with the historical distribution of grades.

It is important to note here that OfQual put in place an online survey to do public consultation around the objectives their system should pursue (OfQual, 2020c). Those happened between the 15th of April and the 22nd of May and the 8th of June and receive feedback from 1,939 students amongst 12,623 responses, a low margin regarding all A-Levels being awarded (OfQual, 2020d). Moreover, the matter of identifying whether OfQual or the Ministry of Education is responsible for deciding to implement the approach that ended being used is contested (Ozga et ;al., Reference Ozga, Baird, Saville, Arnott and Hell2023). Calling this case ‘the OfQual algorithm’ might therefore be a shortcut for more complex issues of relation of power within the English administration, but those are out of the scope of this sole paper.

3.3. The algorithm in action

OfQual had three main resources at hand: centres’ historic grade distribution, students’ previous achievements, and the CAGs. In order to provide students of the 2020 cohort with grades with such data available, OfQual tested eleven prediction models (OfQual, 2020a). In the end, they chose to use a model called the Direct Centre Approach (DCP) which focused mainly on the historical distribution of grade within each centre, favouring the specificities of centres over the national average or the students’ histories. They chose the DCP approach as it was the one affording the most reliable results whilst also being cost effective – both in term of money and time – to put in place (OfQual, 2020a). The algorithm was later made public by OfQual in an extensive report explaining the way their model works.

The algorithm starts by producing a first prediction of the grade distribution for an A-Level in each centre. This prediction is made based on the historical grade distribution of each centre for each A-Level, considering the average grade distribution of a centre over the last three years. The relatively low number of years for the comparison is due to a change in regulation in 2016 meaning that the test could only be made with the data from 2016 to 2019. The algorithm thus had to follow the same time-period to ensure its accuracy compared to the testing. One downside of this distribution was that the fast change in grade distribution (either upward or downward) of some centres was not considered by the algorithm.

The algorithm then predicts a grade distribution for the second time based on students’ previous achievements. Of course, when processed by computers, this step happens at the same time as the first one, but for the clarity of the explanation, it is easier to break it into steps here. During this part, not all students are being taken into consideration. Indeed, some students cannot be matched to their previous achievement by the algorithm, for example students coming from abroad. To ensure that the general distribution of grades is not affected too much by this limitation, the formula developed by OfQual weights the proportion of unmatched students with the national grade distribution. This means that in a classroom where most students were unmatched, the predicted distribution would be closer to the national one.

The model then adjusts the initial distribution of grades to be more in line with previous achievements and the history of each centre and to produce a prediction matrix that is in line with both the historical grade distribution of each centre and the previous achievement of students. The students are then matched to the prediction matrix based on their ranking within the CAGs. For example, if there are 12% students predicted to get a grade A+, the first 12% of the ranking get the grade A+. To avoid having students between two grades,Footnote ³ marks are calculated following the predicted distribution and the history of each centre. The student’s grade is then settled based on that predicted mark.

The working of the OfQual algorithm can be simplified with the following representation (Figure 1):

Figure 1. Simplified representation of the OfQual algorithm, realised by the author.

The way the model works means that the students’ calculated grades strongly relied on the proportion of students that achieved each grade from 2017 to 2019 within each centre, giving more weight to the history of each centre than to the previous achievement of students and their CAGs. That is a direct consequence of the government’s injunction that I underlined earlier. Indeed, this was made to ensure that the general average of each centre, and thus the national average, would not change too much.

3.4. Testing the algorithm for equity

As OfQual, following on the incentives of the government for education, successfully made an algorithm reproducing historical grade distribution at a national level, it raised the question of the presence of inequalities that could be imbued within this new process. In their interim report (OfQual, 2020a), OfQual explained how they tested their system regarding equity. It is important to note here that OfQual’s focus was on the mitigation of biases. Indeed, as they put it, ‘the analyses conducted show no evidence that this year’s process of awarding grades has induced bias. Changes in outcomes for students with different protected characteristics and from different socio-economic backgrounds are similar to those seen between 2018 and 2019.’ (OfQual, 2020a). But how did they come to this conclusion? In addition, is debiasing the solution toward a fair algorithm in this case?

To identify whether their model had a significant impact on social equity, OfQual identified a set of variables regarding students’ backgrounds. Two of the variables were taken from exam boards directly: gender and prior attainments. And five variables were taken from the national pupil database (NPD): ethnicity, major language, special educational needs, free school meal, social economic status. Based on these variables OfQual then analysed how the gaps in grade distribution between the different categories were impacted by their model. They did such analysis not only for each category independently, but for students crossed categories (gender crossed with ethnicity for example).

Doing so, OfQual ensured that their model accurately reproduced the historical grade distribution within the English educational system. As the algorithm became a controversial issue after the delivery of grade, partly due to accusation regarding the unequitable nature of the model, OfQual made their whole dataset as well as the algorithm available for independent researchers to study the model. Despite the accusations of the protesters, scholars from various disciplines studying the algorithm all argue that the algorithm in fact did not discriminate against any protected categories identified by OfQual. Indeed, those studies showed that ‘it is unlikely that students belonging to any of the combinations of protected characteristics considered here were worse off in terms of their teacher-assessed grades when compared to what they would have been likely to have received had COVID interruptions not occurred’ (Magowan, Reference Magowan2023).

Indeed, looking at the tables given by OfQual (2020), we can see for example that students benefiting from free school meals, an indicator of poorer socio-economic status, were 6.7% less likely to obtain a grade A or above than students who did not benefit from it in 2018. They were 7.1% less likely to achieve such a grade in 2020. Overall, students from a lower socio-economic status were 7.3% less likely to obtain a grade A or above in 2020, a number in line with previous years. Such a repetition of historical differences in the distribution of grades between students holds true for each background variable. Overall, the algorithm successfully reproduced historical grade differences between the tested protected categories that were identified earlier.

Of course, the composition of each of these categories could be questioned, as the sorting of students in said manner had a direct impact on what was tested regarding inequalities. For example, the division of the gender category between male and female students, removing the other category due to the ‘very low numbers’ (OfQual, 2020a) meant that OfQual had no information on the impact of their model on transgendered students for example. Likewise, the variables regarding ethnicity could also be questioned with OfQual doing their analysis on six ethnic groups (Asians, Black, White, Mixed, Other, and Unclassified), and on one minor ethnic group as qualified by the NPD: the Chinese, usually a sub-group of the Asian ethnic group.Footnote ⁴

Then, OfQual underlined in their report that there is no indication of discriminatory biases against protected categories in general. Doing so, they refer to the 2010 equality act regarding protected categories that should not be discriminated against. Those categories are as follows: age, gender reassignment, civil status, pregnancy, disability, race (colour, nationality, ethnic and origins), belief, sex, and sexual orientation. Whilst some categories might not be relevant for the case at hand, like age for example, not all protected categories have been tested by OfQual. With a model relying heavily on teachers’ ranking of students, we might ask whether students from the queer community might have been discriminated against by their teachers, coming worse off than they could have with the algorithm.

Through their testing process, what OfQual demonstrated is that the points obtained by each protected category identified while testing for inequalities were in line with previous years variations. But what that meant is that attainment gaps between said backgrounds were not only maintained but also institutionalised in an automated system reproducing existing achievement gaps between categories. Indeed, with this system in place, it meant for example that no more than around 17.7%Footnote ⁵ of students belonging to the black ethnicity group could achieve a grade A or above.

3.5. Contesting the algorithm

Due to the 2020 standardisation model, 41.3% of the CAGs had been tampered with to fit with the historical distribution of their centres.A 39.1% of the total being downgraded one or two grades. As I demonstrated earlier, the results of the calculated grades showed no anomalies in terms of equality between students in comparison to previous years regarding the controlled categories. Its main consequence was to lower the grades that were given by the teachers. The algorithm had thus fulfilled its goal given by OfQual and the ministry of education to provide students with grades in line with the standards.

But on the 13th of august 2020, as students received their grades and OfQual released their interim report explaining the algorithm, contestation appeared. Quickly, #fuckthealgorithm was used as a rallying cry for students both online and offline (Benjamin, Reference Benjamin2022). On the 16^th of August, hundreds of students chanted their rallying cry in front of the Department for Education building with placards stating things such as ‘I’m a student not a statistic’ or ‘poor ;≠ ;stupid’ (Benjamin, Reference Benjamin2022). The general contestation following the implementation of the algorithm has been studied by many scholars following different disciplines, with two main arguments against the algorithm underlined: first against the results of the algorithm, second about the removal of students in the process.

Authors such as Kelly (Reference Kelly2021) argued that protests were stemming from the belief that students from lower-socio economic backgrounds were at higher risk of seeing their grades downgraded. The public enquiry on the impact of COVID-19 on education and children’s services (2020) give us great account of such fear, regrouping a total sum of 454 written evidence around the impact of Covid on education. The following testimony from the enquiry being an illustration of such concern:

Indeed, while it untrue to say that the algorithm induces biases against the tested protected categories in comparison to other years (Magowan, Reference Magowan2023), it is still true that students from low achieving backgrounds and protected groups were bound to achieve lower grades as they were limited to the historical best their background allowed.

This led to new conflicting discourses when the algorithm was revealed, leaving the government with two options: enforcing the calculated grades at all costs without taking into consideration the revendications of students and teachers, or accept the expertise of teachers and apply their CAGs directly. But as they chose the second option, that meant that the algorithm was rendered useless.

However, a study of the marks awarded to pupils in 2020 after the cancellation enables us to test some of the hypotheses put forward by OfQual and the Ministry to implement the algorithm. Indeed, the grades showed that the disparities between centres in fact did not widen but stayed stable regarding the history of each centre. Moreover, as I exposed earlier, there is no evidence that the CAGs induced biases against protected categories identified by OfQual in comparison with previous years (Magowan, Reference Magowan2023; OfQual, 2020a). The main difference brought by the CAGs being the overall higher distribution of A* and A grades. The fact that the general grade distribution between protected categories does not change after the U-turn teaches us that while teachers might tend to over-estimate their students in general, they are still very good at reproducing existing distinction between students.