The Pandemic and Mobile Technology

Abstract

School lockdowns have cast a light over access to mobile technology. But bans on mobile phones, against research evidence, is still a widespread phenomenon. As students are having educationally potent devices taken away, how can the arguments for banning be turned to a positive?

The current pandemic, if nothing else, has disrupted the discourses around technology and particularly mobile technology. We might wonder whether the experience of lockdowns might ultimately shift the balance of forces and change ideological positions on the place of mobile technology, but this remains to be seen.

Interestingly, what we haven’t heard are demands from schools that mobile phones be banned from homes during home or on-line learning, in the way most ban them from schools. This is surprising because surely all the same arguments in support of a ban would hold in whether learning was located in the classroom or in the home. This of course raises questions about the justification for mobile phone bans, especially when for some pupils, their mobile phone is their only current contact with the school and the outside world.

However, in actuality, logic has never really been central to the banning of mobile phones, as is argued in a current article in the British Journal of Educational Technology. The article, is by Neil Selwyn, currently a “Distinguished Research Professor” at Monash University, and who can be called a world ‘expert’ in digital education having researched the field for 25 years, and Jesper Aagaard.

The article is titled “Banning mobile phones from classrooms – An opportunity to advance understandings of technology addiction, distraction and cyberbullying”, though this gives a somewhat false impression of his argument which develops “a counter-narrative to dominant discourses surrounding the prospect of banning phones from schools” because “the idea of removing powerful mobile devices from the hands of students goes against much of the literature in fields such as m-learning and media education” (p. 10). Yet, the experts on mobile technology have found themselves on the wrong side of school practice.

Unfortunately, to date, the efforts of experts who have attempted to argue against such bans has fallen on predominantly deaf ears. Indeed, strong arguments can be made for resisting the blanket banning of phones in terms of equity, practicality and diminished educational opportunities. Nevertheless, government and public support for bans remains strong. (p. 9)

The widespread trend for banning mobile technology is, Selwyn claims, a retreat from decades of “uncontroversial support for technology”, which although largely true ignores the widespread opposition to the use of calculators in mathematics back in the 80s and beyond, in particular from conservative politicians who see them as an attack on “teaching the basics”. However, in contrast to the almost universal acceptance, implementation and installation of interactive whiteboards, bans on mobile technology have been widespread, which is seen as a pivotal moment in slowing the pace of technological change. We see that:

cohorts of students are having educationally potent and personally significant devices taken away while in their classrooms and schools. (p.9)

Of course, mobile phones are not the first artefacts to be banned from schools. Schools are heavily regulated places where various behaviours are restricted and/or prohibited” (p. 10). France has banned the hijab, schools prohibit trainers, jewellery, short skirts and afro-style hairstyles. So, it is useful to see phone bans in the same light in order to both understand and respond to the practice.

So, somewhat in resignation, Selwyn asks:

If we cannot prevent bans from being introduced in the short term, how might we benefit from them in the longer term. (p. 10)

Selwyn’s argument is that the research evidence is unlikely to influence the widespread banning of mobile technology, which stems from the moral panics we have seen before. Printed books were strongly opposed as having the potential to harm the young. See in particular my article “Mobile Technology in the Classroom” which surveys all the current research.

Rather than fight the bans, Selwyn’s alternative is to use the phenomenon as an opportunity to raise critical questions about underlying problematic issues surrounding technology and society.

He sees five elements in the case put forward in support of mobile phone bans:

  • Children becoming addicted to technology
  • Children being distracted in lessons
  • Mobile phones fostering cyberbullying
  • Children being subject to surveillance
  • Environmental sustainability of ubiquitous technology

Though it is the first three that are routinely put forward as the rationale for banning mobile devises, he looks at each in turn by considering how they might be used to advance debate.

Reconsidering concerns over “technology addiction”

It is proven that the impact on young brains in particular with mobile phones is akin to that of cocaine, that is how bad the impact. (George Christensen, Australian MP, cited in Caldwell, 2019)

As Selwyn tells us, it is important to acknowledge that it has emphatically not been scientifically “proven” that phone (over)use is equitable to drug addiction; Christiansen is quite wrong. So, bans will not have any influence on technology addiction.

However, framing mobile phone bans as about “addiction or inconvenience” allows a discussion with young people over the effects on them of a ban. “Will students primarily experience the enforced absence of their phones as a practical inconvenience rather than a psychological hardship?”. Has mobile phone use become habitual for young people? How and why has this happened and how does it manifest itself? This permits a discussion of good and bad habits in learning.

An opportunity to investigate the issue of “digital distraction”

By banning cellphone use that distracts from learning, we are helping students to focus on acquiring the foundational skills they need, like reading, writing and math. (Lisa Thompson, Ontario Minister of Education, cited in Jones, 2019)

Seeing digital devices as a distraction “marks a reversal of previous educational enthusiasm for digital devices allowing more active and engaged” (p. 12).

The issue with this is that the so-called evidence-based claims that mobile phones are distracting, are only supported by scientific studies that show device-based “off-task behavior” is correlated with significant declines in academic performance. This confuses “distraction through multitasking” with “distraction through off-task behaviour” – that is, undertaking research on off-task diversion but claiming it applies to “on-task multitasking“.

The literature shows that when learners use phones to engage in off-task behaviours (shopping, gaming, watching a film) their achievement level goes down. Is anyone really surprised? This aspect allows a discussion over what is the nature of distraction in a lesson, and how we deal with multi-tasking as learner and teacher, and the very real difference between these.

Nevertheless, it is mistaken to conflate the prevalence of device-based multitasking with digital distraction per se. The phenomenon of digital distraction does not relate to students’ attention simply being divided between multiple tasks at once, but to their attention being diverted from the primary educational task by the use of digital devices for off-task purposes.

An opportunity to explore the issue of “cyberbullying”

Half of all young people have experienced cyberbullying. By banning mobiles we can stop it at the school gate. (James Merlino, Victorian Minister for Education, cited in Henriques-Gomes, 2019)

Selwyn claims “there are reasons here to temper our expectations about the effectiveness of phone bans.” Research indicates that when cyberbullying does take place, there are large overlaps with traditional bullying so it is unlikely that mobile phones are somehow causing or facilitating bullying. Moreover, there is a risk that simply banning these devices from school might distract educators from addressing the more direct causes of such harmful behaviour.

An opportunity to address students’ relationships with “surveillance capitalism”

The ban of phones on school will effectively limit the data collected on children by not allowing them to use their personal devices during school hours. (Arantes, 2019)

This rarely seems to arise in debates about mobiles, but Selwyn points out that data is harvested from all of us wherever we use our phones. So, banning classroom use has little effect.

An opportunity to imagine schools’ technology in a post-abundance digital era

Schools these days are awash with interactive white boards, PowerPoint presentation, computer simulations, digital manipulatives, cloud storage, and along with our mobile phones these consume huge amounts of energy. This leads to another argument for reducing the use of mobile technology – the fast-declining environmental sustainability of digital education. Any phone ban might, therefore, act as a starting point for students, teachers and school communities to reorient their device use in the classroom as part of a sustainable future. Do we all need IWBs? Do we each need a mobile device? Do we really need digital manipulatives?

Finally….

Finally, given the impact mobile technology now has on young people’s lives, how many schools actually ask their pupils about school practices that impinge so much on their daily lives and their futures? Might we ask:

How do students feel about the social dynamics of a mobile-free school? Are they relieved, annoyed, anxious or invigorated? How do these reactions differ between different groups of students?

To finish with a quote from Selwyn, although educationally unsound, mobile phone bans can offer us:

the potential to refocus discussions about educational technology away from the “technology” per se, and towards a diverse range of perspectives on what digital devices are, what they do and the conditions they create in school contexts. Mobile phones—as any form of digital technology use in education—have a range of consequences that are neurological, psychological, social, political and ecological in nature. The arguments outlined in this paper move discussions of education and technology towards more nuanced understandings where the complexities of “real world” digital education are fully engaged with.

(p. 17)

Selwyn does not go into detail over how these discussions might take place. But that was not his intention. What he does highlight is, in contrast to what many schools claim, mobile phones have not been shown to be a distraction, are not the cause of (cyber)bullying and are not addictive.

One way forward is for schools to begin to have these debates, and move to asking “what do we need to establish in order for pupils to have access to their mobile phones safely?” This would presumably include all these assumptions we make about the use of mobile phones by adults in schools – because I imagine few, if any, schools ban staff from having their phones in their possession in school. Indeed, what would staff do if a Head one day banned them from having their phones in school?

Moving Beyond Being ‘Evidence-Informed’

Accessing Educational Research

The claim that an educational practice or policy is “evidence informed” or “evidence-based” is becoming widespread in the discourse of educational management and is used by many practitioners. Yet this can often be little more that an attempt to legitimatize current practices, by drawing of the evidence of everyday procedures. What is accepted as “evidence” is not unrelated to one’s own ideological persuasion and perspective on teaching and learning. Accepting as evidence that which conflicts with one’s own beliefs or experience becomes very difficult.

Take as an example, the overwhelming international evidence on attainment grouping in mathematics (see my pages on pupil grouping) which has shown it has few advantages, but it does depress academic attainment, and discriminates against learners from lower social class backgrounds. However, that evidence is not sufficient to eradicate the practice, far from it. Rather than letting the evidence inform the practice (“evidence informed practice“), what we see is the evidence rejected because “it won’t/can’t work” (practice rejected evidence“).

Why is this? I believe there are two fundamental reasons – one political and one pragmatic.

Politically, when a widespread educational practice discriminates against pupils from disadvantaged communities this is not necessarily recognised nor seen as relevant to the conservative right currently in government in the UK. Social segregation is embedded within a Conservative discourse and even the architecture of schools. It is “how it is“and it portrayed as “it could hardly be otherwise“. Of course individual teachers can do little to shift this infrastructure apart from subvert, and to offer local alternatives – some of which can be seen here. The bottom line is, education is political – schools are a mechanism for inculcating the dominant political culture to future generations.

Pragmatically, ability grouping (or social class segregation) is so deeply embedded in the history, curriculum and pedagogy of schools, that shifting it is a herculean task – or rather a Sisyphean endeavour, where small advances become undone by the boulder of conservatism rolling us back down the hill.

This raises two questions for the teaching professional. The first question is what counts as quality evidence that is robust, valid, and reliable, and where can this be found. The second question is how is the profession to use and implement this evidence?

This first question is not a trivial matter of merely using Google Scholar. Given that most published research appears in peer reviewed academic journals (one of the criteria for quality), it is often hidden behind publishers’ paywalls and thereby inaccessible to teachers. The result is teachers are forced to rely on often dubious ill-informed summaries in the TES, or just reading the all too brief published abstracts.

Many academic researchers are qualified to masters level in the use of research methodology (a condition for funding from the Economic and Social Research Council), and in the same way teachers are trained in pedagogy, most researchers will have followed research methodology courses at doctoral level for a PhD. The audience for research in journals is usual other researchers and consequently it has a language and theoretical underpinnings befitting that audience. It is not written for a practitioner audience. This is a major shortcoming of the dissemination strategy of much (though not all) research.

It is very easy therefore for teachers to rubbish research which they might not understand and which challenges their preconceptions. For example, recently an article on by Dr Ian Cushing that had been published in the British Educational Research Journal, the UKs foremost research journal (and had therefore been peer reviewed), was circulated on Twitter. The paper is on language use and discipline and surveillance in schools – and is available as an open access article here. Now, Ian has a BA in Linguistics, an MA in Phonetics (I have no idea what that is!), a PhD in Applied Linguistics, as well as a PGCE in English Teaching. He studied research methodology at masters level, and now works as an academic at Brunel University. He knows what he is writing about.

One teacher (with a blue tick and 16,000 followers) responded to the article on Twitter describing it as follows:

“a load of self-indulgent impenetrable academic bollocks and has nothing useful to say to teachers.”

Now there will be of course many reasons hiding behind that comment, not least a sense of challenge and threat since Ian’s research was questioning a teaching strategy that this teacher was using – and in particular Ian was critiquing the oppressive surveillance culture promoted by the “Teaching Like a Champion” industry. But the teacher’s reaction raises a real issue – something which for years I taught to Masters and Doctoral students at the University of Nottingham – the impact (or lack of) of educational research on educational practice. This could have been a very short module: “not much” and that remans an intractable problem, for both pragmatic and political reasons.

Using Educational Research

However, all might not be lost. A team at Monash University in Australia are working on a project on theQuality Use” of educational research. This is a five year project seeking to improve the use of evidence in Australian schools. What the project does is provide a structure and a framework around which we can frame ‘thoughtful engagement with and implementation of appropriate research evidence‘. This is reported in an article in the British Education Research Association magazine Research Intelligence No 144, Autumn 2020.

Their framework has several components, and Figure 1 below shows the components of their framework.

Research Intelligence 144, Autumn 2020, p. 27

Their Core Components are:

Appropriate research evidence. This requires the research itself to be rigorous, well designed, valid and reliable, all of which are technical criteria well described (but sadly not always well implemented) in the educational research field. Also the focus of the research needs to be appropriate for the context where it might be used. However, not all educational research is intended for implementation by a practitioner audience. Some will be theoretical or focusing on systemic issues.

Thoughtful engagement and implementation. This requires some intellectual critique and collaboration between the researchers and teachers. Meaning needs to be explored, and the implications of any implementation examined.

In operationalising these two components there are three individual components, and three organisational components that all need to be put in place.

Individually, practitioners need to have the necessary skillsets to enable them to understand, critique and interpret education research. They need to have a mindset to want to engage thoughtfully and critically with the evidence, and lastly there needs to be constructive and positive relationships between researchers, academics and practitioners.

Another article in the same issue of Research Intelligence comes from Nadia Siddiqui and Lindsey Wardle from Durham University, titled “Can users judge what is ‘promising’ evidence in education?“. In it the say:

Many teacher training courses, even those led by university academic departments, do not currently provide sufficient skills for teachers in understanding and being able to judge research that can enhance their teaching practice and have direct benefits for pupils’ learning outcomes. There is surely a need for embedding high-quality research capacity-building in initial and advanced teacher training programmes

Research Intelligence 144, Autumn 2020, p. 21

This seems to be so true, yet the practice of having “research leads” in schools but providing these teachers with little in the way of research training, experience or expertise seems to me to be diverting attention away from schools engaging robustly in research.

Organizationally, schools and universities need to establish the infrastructures to allow collaboration and provide sufficient resources especially in terms of time. The schools and University departments need to have a leadership that will champion engagement between research and practitioners, and lastly, this needs to be embedded within the culture of schools and universities.

However, schools and universities do not exist as islands forging their own way through the minefield that is school improvement. They depend upon system-level influences, both within schools and higher education. This is the stage where individuals lose much influence. Political interference in both sectors is now considerable and policy and decision making at the system level is more likely to infuriate and frustrate both teachers and academics, than to inspire them.

All this is a far cry from a teacher reading a research article on sociolinguistics in a research journal and describing it as “self-indulgent impenetrable academic bollocks“. That gets us nowhere.

The next question is how we implement an implementation strategy. Feel free to comment below, but leave the “load of self-indulgent impenetrable academic bollocks” for now eh?

Peter

Economic Factors Behind Mathematics Achievement


Adapted and expanded from Jurdak, M. et al. (2016) pps 23-27. Available as a pdf here.

Mathematics for some

Socio-economically advantaged students and schools tend to outscore their disadvantaged peers by larger margins than between any other two groups of students. (OECD, 2013a, p. 34)

This is indeed a sobering thought that will no doubt cause controversy by its claim that it is economics, income inequality or social economic status (SES) which is more significant than gender and race in explaining differences in mathematics achievement. Whilst this will cause consternation, it is just obvious. Being a disadvantaged student does not mean you can’t go on to do well, earn a salary significantly over the median wage, or even write an article for an international conference! While some students from disadvantaged backgrounds can succeed “against the odds” (Bembechat, 1998), the system leaves many where they are. Social mobility becomes the story of the few, not the many; history is written by the winners. But relatively few of us ex-poor kids do well, and in so doing, we leave many behind us. I wrote about this in my PhD thesis (Gates, 2000). In 1963 I took the 11+  as did my best mate, Tony. We both came from the same socio-economic background, but for some reason, I passed, Tony did not and after we left Primary school, I never saw Tony again.

“Kids who failed, I never met them again. We lived in the same town; we moved in different cultures. We were totally segregated.” Colin Welland on TV Programme “Grammar School Kids”.

In ”Is mathematics for all?” Gates & Vistro-Yu, (2003) argued that across the world indeed mathematics wasn’t for all, but was differentially experienced. We suggested several strategies to help a process of democratisation of mathematics: detracking, equitable allocation of resources, and the appreciation of working-class cultures. Two decades later and we are still arguing for the same strategies, which begs the question – why? Something must be going on to sustain the levels of inequality within the teaching and learning of mathematics in the face of much apparent consternation and displeasure. What are we doing wrong, or rather not doing right? Or maybe, more sinisterly, is this inequality sustained because it is intentional and desirable.

I am going to start with a focus on the OECD analysis of PISA 2012 data and in particular what that throws up about poverty and achievement. One finding is that across OECD countries, a more socio-economically advantaged student scores the equivalent of nearly one year of schooling higher than a less-advantaged student (OECD, 2013a, p. 13).

What the PISA studies consistently show is that SES is very clearly associated with mathematics achievement in a number of complex ways, at the national level (through higher spending on education), the school level (through providing a safe environment and high-quality resources) and the individual level (through parental engagement for example) (OECD, 2013a, p. 37). However, what current economic and social analyses are showing is that it is not so much the existence of poverty that is the result of many social problems, but is the existence of – and the contemporary increase in – income inequality which lies at the root.

The highest-performing school systems are those that allocate educational resources more equitably among advantaged and disadvantaged schools and that grant more autonomy over curricula and assessments to individual schools. (OECD, 2014, p. 4)

So, whilst the mathematics education research community might want to frame the debate on mathematics achievement around cognitive development, identity, curriculum, teaching style, cognitive loads, Direct Instruction or inquiry etc. we are up against a much bigger problem – the growing income inequality (Dorling, 2014).

A growing body of evidence points to high and rising inequality as one of our current decade’s most important global issues in light of the far-reaching implications increasingly associated with it. (Stotesbury and Dorling, 2015, p. 1)

This extent of the malignant effect of inequality has been well illustrated (Wilkinson, 2005; Wilkinson & Pickett, 2010) such that greater equality increased everyone’s quality of life. But if we want social class to have less influence on educational (and therefore mathematics) outcomes, “it will be necessary to reduce the material differences which are so often constitutive of the cultural markers of social differentiation” (Pickett & Wilkinson, 2015, pp. 323-324). As mathematics educators therefore, we need to work to reduce the wealth of the affluent (particularly the new 1%) and distribute it to the poor. This will be a difficult process for many in the mathematics education community, yet is exactly what has been proposed by Ole Skovsmose, Rico Gutstein and Ubiratan D’Ambrosio – amongst many others – including Wilkinson and Pickett (2010, p. 108) for some time.

Drawing together data from a range of international sources, Stotesbury and Dorling (2015) have looked at the PISA data on mathematics achievement. Their analysis is suggesting two things. First that mathematical achievement is negatively correlated with income inequality (as measured by the ratio between the percentage of wealth owned by the top 10% and the bottom 10%), but second that this correlation is significantly stronger when we measure the maths ability of older (16-24) students. “This is interesting because it hints at the possibility that more unequal countries’ education systems fail to foster long-term understanding to the same extent that education in more equal countries appears to have a longer lasting effect on young peoples’ ability” (Stotesbury and Dorling, 2015). In other words, in countries with low levels of income inequality, what is taught in school mathematics seems to be retained longer once the student leaves the education system than where income inequality is higher.

That is a quite a surprising claim. How might a macroeconomic statistic on measures of relative wealth influence how students learn mathematics even after they leave school? Well, the PISA data would need to be mined in a lot more detail to uncover the causal mechanisms at work. Dorling’s work, and that of Wilkinson and Pickett, points to a number of social characteristics of unequal societies – increased social conflict, anxiety and insecurity, levels of homicide, etc. and it is in there where we may find some of the root causes (but not the manifestations) of underachievement in mathematics.

In a study of mathematics education in “high performing” countries, Askew et al. (2010) argue that attainment in mathematics might be “much more closely linked to cultural values” (p. 12). This they admit “may be a bitter pill for those of us in mathematics education who like to think that how the subject is taught is the key to high attainment” (p. 12). Yet the way we respond to that may also be both cultural, and, importantly, political. Askew et al. argue that no system has the definitive answer; that choices need to be made between some very central social characteristics. So “you can have an egalitarian education and high standards (Finland), or you can have a selective one and still have high standards (Singapore)” (Askew et al., 2010, p. 14). The question is though, whose choice is it and how is that choice made? The economic and political system itself facilitates some choices over others. Yet as researchers we too have choices. The word “politics” does not appear in Askew et al. who prefer a focus on “socio-cultural-historical backgrounds”. What Dorling, and Wilkinson and Pickett’s work offers us, is a different choice of emphasis, that complements the focus on characteristics more visible in mathematics education. The single reference in Askew et al (2010) to “(social) class” (remember THE most significant characteristic according to the OECD) is restricted to a discussion of how parental social class in China is not a significant discriminator when looking at parental expectations. (See Gates and Guo, 2013 for a discussion of the influence of social class in British-Chinese student achievement).

But it does not have to be like this, “countries do not have to sacrifice high performance to achieve equity in education opportunities” (OECD, 2013a, p. 3), “Mexico, Turkey and Germany improved both their mathematics performance and their levels of equity” (OECD, 2013a, p. 26). The OECD analysis also illustrates that merely increasing expenditure on education will not bring about improvement in achievement if it is not accompanied by greater equity. It is not a matter of how much is spent, but on how it is spent

In particular, greater equity in the distribution of educational resources is associated with higher mathematics performance. 30% of the variation in mathematics performance across OECD countries can be explained by differences in how educational resources are allocated between advantaged and disadvantaged schools. (OECD, 2013a, p. 29).

In highly differentiated educational systems, the impact of a students’ socio-economic status on his or her educational goals is stronger than in less differentiated systems (Buchmann & Dalton, 2002; Buchmann & Park, 2009; Monseur & Lafontaine, 2012)

In highly differentiated systems, socio-economically disadvantaged students tend to be grouped into less academically orientated tracks or schools, and this has an impact on their educational aspirations, possibly because of the stigma associated with expectations of lower performance among students enrolled in these tracks and schools, or because less – and often poorer quality – resources are allocated to these schools. (OECD, 2013b, p. 86)

Given the widespread use of social class segregation through “tracking“ and “setting” in the organisation of mathematics classes, this effect is likely to be enhanced.

Mathematics isn’t for all

We have experienced in mathematics education “the social turn” (Lerman, 2000) and the “socio-political turn” (Gutiérrez, 2010) yet both of these have tended to encompass largely a view of inequity as “a problem affecting particular groups of people [rather than] a problem of the school system” (Pais, 2014, p. 1086). As a result of an individualisation of failure, attention has been directed away from the economic system which by design creates inequality. It might also explain why the systematic failure of children from working class communities gets so easily overlooked, despite all the research that has explored this area for decades (Pais & Valero, 2012). I might go further – it is because of the “depoliticisation of research” (Pais, 2014, p. 1090) that allows much of mathematics education research to cast a blind eye to the most significant source of underachievement. One source of this depoliticisation is a tendency to assume, erroneously, that postmodern approaches offer insights because they “move beyond Marxist views of power” (Gutiérrez, 2010, p. 12). Given the immense power of Marxist analyses of the economy, such individuating constructs as discourse, identify and a focus on localised struggles, whilst locally useful, can only fail to grapple with the structural inequalities which are an inherent component of international capitalism.

As illustrated in OECD publications, internationally there is a long history of under achievement in mathematics illustrated by many young people not enjoying the subject and not taking up further study after compulsory schooling and the problem remains entrenched. One thing is clear – success at mathematics is not evenly distributed across sections of society. So to understand the differential performance of pupils from low SES backgrounds, we need to look into classroom practices to ask difficult questions about the experiences of learners from certain social-economic groups. Of course much literature in mathematics education talks not of social class, but of levels of attainment. Much literature in the field of mathematics education focuses on teaching and learning and on levels of pupils’ attainment through a focus on the pupil, the classroom, the teacher, the curriculum, and the school – in other words on the localised manifestation of cultural practices. Fewer studies drill down into the very structure of the economic and political system exploring how it solidifies into the interactions and artefacts of mathematics education. This is an example of what Bourdieu calls misrecognition (Bourdieu, 1989, p. 377) where social power is exercised by making itself unrecognisable (Bourdieu, 1990) – and thus representing a denial of the economic and political interests at work.

There are though some robust examples of inquiries into social class. One such is Sarah Lubienski who studied mathematical experiences of pupils with an eye to looking at pupils’ backgrounds (Lubienski, 2000a, 2000b, 2002, 2007). Whilst she naturally expected to find SES differences what she actual found were very specific differences in two main areas – whole class discussion and open-ended problem solving. These are two well-researched pedagogical strategies and classroom practices which at least in professional discourse are held in some esteem. Discussion based activities were perceived differently by pupils from different social backgrounds. High SES pupils thought discussion activities were for them to analyze different ideas whilst low SES pupils thought it was about getting right answers. The two groups had different levels of confidence in their own type of contributions with the low SES pupils wanting more teacher direction. Higher SES pupils felt they could sort things out for themselves – as their parents do in life presumably. I suspect this is not an uncommon feature of many schools but where does it emanate? Here then social class is a key determining characteristic largely absent from much literature on discussion based mathematics.

A second area where Lubienski noted differences was that of open-ended problem solving. The high level of ambiguity in such problems caused frustration in low SES pupils which in turn caused them to give up. High SES pupils just thought harder and engaged more deeply. It is well known that middle class pupils come to school armed with a set of dispositions and forms of language which gives them an advantage because these dispositions and language use are exactly the behaviours that schools and teachers are expecting and prioritise (Zevenbergen, 2000). High SES pupils have a level of self-confidence very common in middle class discourses whilst working class discourses tend to be located in more subservient dependency modes, accepting conformity and obedience (Jorgensen et al., 2014).

Middle class pupils after all tend to live in families where there is more independence, more autonomy and creativity (Kohn, 1983). Studies of parenting suggest different strategies are used in different class backgrounds. Low SES, working class parents are more directive, requiring more acquiescence. Middle class parents tend to be more suggestive and accommodating reason and discussion (Lareau, 2003).  The middle classes grow up to expect to be and to feel superior with more control over their lives. Class is never far away from the mathematics classroom, but it is often far away from mathematics education research

The poverty of experience

One popular justification for learning mathematics is its usefulness and applicability to “the real world”. Yet many young people experience mathematics from a very unreal world – the world of the school classroom. This world has specific rules, practices and objects all of which work insofar as they make school mathematics work as a system in and of itself. Pupils solve equations without ever having a purpose, other than to get a solution through applying a set of procedures in the correct order. The solution is not a solution to any real problem and it is questionable whether any of the procedures learned in school mathematics would solve any problem that young people encounter either as adolescent pupils or as young workers. In this way learners are alienated (to use a Marxist concept) from their learning. This was expressed very starkly in the Cockcroft Report 462 way back in 1982

Mathematics lessons in secondary schools are very often not about anything. You collect like terms, or learn the laws of indices, with no perception of why anyone needs to do such things. There is excessive preoccupation with a sequence of skills and quite inadequate opportunity to see the skills emerging from the solution of problems. As a consequence of this approach, school mathematics contains very little incidental information. A French lesson might well contain incidental information about France – so on across the curriculum; but in mathematics the incidental information which one might expect (current exchange and interest rates; general knowledge on climate, communications and geography; the rules and scoring systems of games; social statistics) is rarely there, because most teachers in no way see this as part of their responsibility when teaching mathematics. (Cockcroft Report, 1982, Para 462)

Now of course school mathematics is school mathematics – and is experienced in a somewhat similar context by almost all pupils whatever their social background. However, what is different for different pupils is the form that school mathematics takes. Some pupils will remain within a somewhat abstract world where the systems of thought of the school will be exactly what they need to move onto a next stage – be it further study of mathematics or higher education. For others however, those whose trajectory will be moving more directly toward employment in some form, their school mathematics will be at odds with what everyone knows is needed to practice. Young people who move into employment move from one set of practices to another quite different set.

In a structural way this is no different from the workplace; there you find jobs to be done, manuals to help, tools to use, timescales to keep to, and a team of people to work with. Here however, they are often referred to as systems, tools, artefacts and protocols (Gagliardi, 1990). These became “crystallised operations” (Leont’ev, 1978) and the work activity not only structures the tools and artefacts, but becomes also structured by it (Pozzi et al., 1998). Recent work on workplace mathematics has shifted a focus away from a more conceptual, cognitive approach where we look to how school mathematics can be used in other settings, to a more situated and cultural approach (Hoyles et al., 2010; Roth, 2014; Williams & Wake, 2007a, b). Not only has this changed the way we see mathematics in use, but it has also contributed to a change in how we see mathematics itself. What we do now know is that school mathematics is quite different from workplace mathematics. Many young people coming from school fail to see the nature of mathematics as conventional and idiosyncratic when used to undertake practical tasks. Because mathematics is “shaped” by the workplace context, rather than procedural, this leaves them unprepared for tasks in which mathematics is embedded and functional.

Class, in some guise or another, is always a latent variable whose invisibility obscures possibilities for action. However this remains not merely an epistemic or empirical question, but a political and an ideological one. Engaging explicitly with class and social differences in learning has been shown to have the potential to open up greater opportunities for higher order thinking (Jorgensen et al., 2011), and for raising the intellectual quality of pupil cognition (Kitchen et al., 2007). However, if failure in mathematics is structured and systematic as the OECD seems to suggest, why is this not clearer in mathematics education research? That is indeed an ideological question. Pais and Valero (2012, p. 18) argue “although many researchers acknowledge the social and political aspects involved in reforming mathematics education, they end up investigating problems as if they could be solved through better classroom practices”, but changing school mathematics practices “depends of course on changing the formal educational structures that determine and shape the particular mathematics education practice experienced by the students in their schools” (Abreu et al., 2002, p. 4). If we are to change things, we have to more away from claiming as they do that such considerations are “beyond the scope of this book”.

We need to engage more with the consequences of the economy which structures our existence, our exchanges and our relationships. This might mean shifting away from a denial of grand narratives, and looking instead toward those structural explanations of the social world which have proved successful.

References

Abreu, G. d., Bishop, A., & Presmeg, N. (2002). Transitions between contexts of mathematical practices. The Netherlands: Kluwer Academic Publishers.

Askew, M., Hodgen, J., Hossain, S., & Bretcher, N. (2010). Values and Variables. Mathematics Education in High-Performing Countries. London: Nuffield Foundation.

Bembechat, J. (1998). Against the Odds. How “at Risk” Students Exceed Expectations. San Francisco: Jossey Bass.

Bourdieu, P. (1989). La noblesse d’Etat. Grands corps et Grandes écoles. (translated as The State Nobility: Elite Schools in the Field of Power, Cambridge, Polity Press, published 1996). Paris: Editions de Minuit.

Bourdieu, P. (1990). The Logic of Practice, (Translation of Le sens pratique, by Richard Nice 1980). Cambridge: Polity Press.

Buchmann, C., & Dalton, B. (2002). Interpersonal Influences and Educational Aspirations in 12 Countries: The Importance of Institutional Context. Sociology of Education, 75(2), 99-122.

Buchmann, C., & Park, H. (2009). Stratification and the Formation of Expectations in Highly Differentiated Educational Systems. Research in Social Stratification and Mobility, 27(4), 245-267.

Cockcroft Report (1982) Mathematics Counts, London: HMSO.

Dorling, D. (2014). Inequality and the 1%. London: Verso.

Gagliardi, P. (1990). Symbols and Artifacts. Views of the Corporate Landscape. New York: Aldine de Gruyer.

Gates, P. (2000) A Study of the Structure of the Professional Orientation of Two Teachers of Mathematics: A Sociological Approach, Unpublished PhD Thesis, University of Nottingham.

Gates, P., & Guo, X. (2013). How British-Chinese parents support their children: a view from the regions. Educational Review, 66(2), 168-191.

Gates, P., & Vistro-Yu, C. (2003). Is Mathematics for all? In A. Bishop, M. Clements, C. Keitel, J. Kilpatrick & F. Leung (Eds.), Second International Handbook of Mathematics Education (pp. 31-73). Dordrecht: Kluwer Academic Publishers.

Gutiérrez, R. (2010). The Sociopolitical Turn in Mathematics Education. Journal for Research in Mathematics Education, 41, 1-32.

Hoyles, C., Noss, R., Kent, P., & Bakker, A. (2010). Improving Mathematics at Work: the need for techo-mathematical literacies. London: Routledge.

Jorgensen, R., Gates, P., & Roper, V. (2014). Structural Exclusion through School Mathematics: Using Bourdieu to Understand Mathematics a Social Practice. Educational Studies in Mathematics, 87, 221–239. doi: 10.1007/s10649-013-9468-4

Jorgensen, R., Sullivan, P., Grootenboer, P., Neische, R., Lerman, S., & Boaler, J. (2011). Maths in the Kimberley. Reforming mathematics education in remote indigenous communities. Brisbane: Griffith University.

Jurdak, M., Vithal, R., de Freitas, E., Gates, P. and Kollosche,D. (2016) Social and Political Dimensions of Mathematics Education. Current Thinking. ICME-13 Topical Surveys, Dordrecht: Springer.

Kitchen, R., DePree, J., Celedón-Pattichis, S., & Brinkerhoff, J. (2007). Mathematics Education at Highly Effective Schools that Serve the Poor: Strategies for Change. New Jersey: Lawrence Erlbaum.

Kohn, M. (1983). On the transmission of values in the family: A preliminary foundation. Research in the Sociology of Education and Socialisation, 4(1), 1-12.

Lareau, A. (2003). Unequal Childhoods. Class Race and Family Life. California: University of California Press.

Leont’ev, A. (1978). Activity,Consciousness and Personality. New Jersey: Prentice Hall.

Lerman, S. (2000). The social turn in mathematics education research In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning (pp. 19- 44). Westport, CT: Ablex Publishing.

Lubienski, S. (2000a). A clash of cultures? Students’ experiences in a discussion-intensive seventh grade mathematics classroom. Elementary School Journal, 100, 377-403.

Lubienski, S. (2000b). Problem solving as a means towards mathematics for all: An exploratory look through a class lens. Journal for Research in Mathematics Education, 31(4), 454-482.

Lubienski, S. (2002). Good Intentions Were Not Enough: Lower SES Students’ Struggles to Learn Mathematics Through Problem Solving. In NCTM (Ed.), Lessons Learned from Research (pp. 171-178).

Lubienski, S. (2007). Research, Reform and Equity in US Mathematics Education. In N. Nasir & P. Cobb (Eds.), Improving Access to Education. Diversity and Equity in the Classroom (pp. 10-23). New York: Teachers College Press.

Monseur, C., & Lafontaine, D. (2012). Structure des systèmes éducatifs et équité : un éclairage international. In M. Crahay (Ed.), Pour une école juste et efficace. Brussels: De Boeck.

OECD. (2013a). PISA 2012 Results: Excellence through Equity: Giving Every Student the Chance to Succeed Volume II: PISA.

OECD. (2013b). What Makes Schools Successful? Resources, Policies and Practices – Volume IV. Paris.

OECD. (2014). PISA 2012 Results: What Students Know and Can Do – Student Performance in Mathematics, Reading and Science (Volume I, Revised edition, February 2014), : PISA.

Pais, A. (2014). Economy: the absent centre of mathematics education. ZDM Mathematics Education, 46, 1085-1093.

Pais, A., & Valero, P. (2012). Researching research: mathematics education in the Political. Educational Studies in Mathematics, 80(1), 9-24. doi: 10.1007/s10649-012-9399-5

Pickett, K., & Wilkinson, R. (2015). Income Inequality and Health: A Causal Review. Social Science and Medicine, 128, 316-326.

Pozzi, S., Noss, R., & Hoyles, C. (1998). Tools in practice, mathematics in use. Educational Studies in Mathematics, 36(1), 105-122.

Roth, W.-M. (2014). Rules of bending, bending the rules: the geometry of electrical conduit bending in college and workplace. Educational Studies Mathematics, 86, 117-192.

Stotesbury, N. and Dorling, D. (2015) Understanding Income Inequality and its Implications: Why Better Statistics are Needed, Statistics Views, 21st October.

Wilkinson, R. (2005). The Impact of Inequality. London: Routledge.

Wilkinson, R., & Pickett, K. (2010). The Spirit Level. London: Allen Lane.

Williams, J., & Wake, G. (2007a). Black boxes in workplace mathematics. Educational Studies in Mathematics, 64, 317-343.

Williams, J., & Wake, G. (2007b). Metaphors and models in translation between college and workplace mathematics. Educational Studies in Mathematics, 64, 345-371.

Zevenbergen, R. (2000). “Cracking the code” of mathematics classrooms: school success as a function of linguistic, social and cultural background. In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning (pp. 201-223). Westport: Ablex publishing.