Pakistan and Education

 

Our fixation with maths doesn’t add up

Simon Jenkins in The Guardian


Who will win the Varkey Foundation’s million-dollar “best teacher” prize this week in Dubai? Hot favourite is Britain’s star maths teacher,Colin Hegarty, whose videos are followed by a million viewers worldwide. Hegarty has been hailed as the great hope for British maths.

Like much of the public realm, British maths is “in crisis”. The country is languishing alongside America way down every league table. Evidence of this was cited in a new poll from YouGov, measuring the public’s knowledge of maths, science and English. In maths, roughly a third of those surveyed had no idea how to calculate a mode, a median, a “line of best fit” or the area of a circle.

I seriously doubt this poll, since it implies that two-thirds did know the answers. All on whom I tested it failed, including myself. Nor could they see the point. With one voice they replied: “That’s the sort of thing you learned at school.” So what is the point? It merely ensures that many pupils, like the bright child of a friend of mine with maths blindness, have their schooling undermined by the government’s fixation with maths.



Colin Hegarty has been shortlisted for the $1m Varkey Foundation teacher prize. Photograph: Linda Nylind for the Guardian

There is nothing, except religion, as conservative as a school curriculum. It is drenched in archaic prejudice and vested interest. When the medieval church banned geography as an offence against the Bible, what had been the queen of the sciences never recovered. Instead Latin dominated the “grammar” curriculum into the 20th century, to the expense of all science. Today maths is the new Latin.

Science at least improved. In 1988 Thatcher hailed a “dash for science” to halt the decline in young people opting for science subjects. It failed completely. Then in 2006 a radical new GCSE syllabus dragged school science away from test tubes and Bunsen burners towards everyday life, to pollution, global warming, additives, health and diet. Since then the reactionaries have turned to maths as the talisman of educational success. It is in maths that we must beat the Chinese, the Singaporeans and the Finns. Pisa league tables of “top nations” are pored over. Maths teachers are paid more, have their loans written off, are entered for global prizes. No one dares mention the calculator.

Any league table that has China at the top, Britain at 26th and America at 36th tells me something more important than merely who is good at maths. If the US and Britain – among the most vigorous economies and most successful at science – are so bad at maths, it suggests their young people are applying themselves to something more useful. Chinese students are rushing to British and US universities to join them.

No one would argue that pupils should not be able to add, subtract and multiply. But I studied higher maths, from calculus to number theory, and have forgotten the lot. All the maths I have needed comes from John Allen Paulos’s timeless manual, Innumeracy. It is mostly how to understand proportion and risk, and tell when a statistician is trying to con you.

I agree with the great mathematician GH Hardy, who accepted that higher maths was without practical application. It was rather a matter of intellectual stimulus and beauty. A new book by Michael Harris, Mathematics Without Apologies, goes to the extremes of this stimulus, to the categorical ladder, incompleteness theory and the Black-Scholes equation, used to assess financial derivatives. It ends in the “inconsistency nightmare”, that nought might possibly equal one.

We accept the need for maths in advanced physics and in computing algorithms, much as we accept Greek for archaeology and Anglo-Saxon for early literature. The “mathematics of finance” school at Columbia University is lavishly sponsored by Wall Street firms, for good reason. But that does not mean every primary pupil must spend hours, indeed years, trying to learn equations and πr2, which they soon forget through disuse. Maths is for specialists, so why instil arithmophobia in the rest?
Charge the maths lobby with the uselessness of its subject and the answer is a mix of chauvinism and vacuity. Maths must be taught if we are to beat the Chinese (at maths). Or it falls back on primitivism, that maths “trains the mind”. So does learning the Qur’an and reciting Latin verbs.

Meanwhile, the curriculum systematically denies pupils what might be of real use to them and society. There is no “need” for more mathematicians. The nation needs, and therefore pays most for, more executives, accountants, salesmen, designers and creative thinkers.

At the very least, today’s pupils should go into the world with a knowledge of their history and geography, their environment, the working of their bodies, the upbringing of children, law, money, the economy and civil rights.

This is in addition to self-confidence, emotional intelligence and the culture of the English imagination. All are crowded out by a political obsession with maths.

The reason is depressingly clear. Maths is merely an easy subject to measure, nationally and internationally. It thus facilitates the bureaucratic craving for targetry and control. The prominence of maths in the curriculum is education’s version of Orwell’s imaginary boot, “stamping on your face … forever”.

------ Reply from a Maths teacher

Maths isn’t the problem - the way it’s taught is

Tim Gowers in The Guardian


A county council gathers data about where road accidents take place, identifies an accident blackspot, places a speed camera there, and notices that the frequency of accidents decreases. Moreover, this is not a one-off: many other councils have observed similar reductions. Does this show that speed cameras improve road safety?

The answer isn’t as straightforward as one might imagine – and the way to understand this is through mathematics. Unfortunately, the way the subject is taught often leaves people with a narrow and misleading view of what maths is. No wonder Simon Jenkins attacked the subject in Thursday’s Guardian.

Mathematics should be a tool for increasing one’s thinking power but for many children it is just a set of rather pointless rules for manipulating symbols. The problem becomes clear if one asks children a question such as the following: a number 35 bus pulls up at a bus stop and eight passengers get on; what is the age of the bus driver? A large percentage of children, their minds numbed by years of symbol manipulation, will give the answer 43. This is a tragedy: rather than being trained to think, these children have been trained to do the opposite.

To return to the speed cameras, the evidence initially seems conclusive. However, the correct answer is neither a clear yes nor a clear no, but rather that more research is needed. Consider what would happen if the locations of accidents were completely random. Then, just by chance, some places would have noticeably more accidents than others while the data was being collected and these places would be identified as blackspots. But since they would not in fact be more dangerous than anywhere else, the later accident rates at these “blackspots” would tend to decrease to more like the average, whether one installed speed cameras or planted apple trees. This phenomenon is known as regression to the mean. Further investigation is needed to determine whether speed cameras make a difference over and above the difference one would have expected anyway. (It turns out that they do.)

Regression to the mean is one of several statistical phenomena that are counterintuitive until you understand them. But once you do, you become better at making decisions. This is important for individuals – whether we like it or not, we all have to take major decisions based on statistical evidence – and it is even more important for people in positions of authority, whose decisions affect other people.

It is therefore good for the health of a country if its population has high standards of mathematical literacy: without it, people are swayed by incorrect arguments, make bad decisions and are happy to vote for politicians who make bad decisions on their behalf.

So how might mathematics education be different? The way it is often taught, children are asked to take a huge leap of faith: that the symbol manipulation that seems pointless now will one day be useful to them. But this is true for only a small minority of children, who enjoy the symbol manipulation for its own sake and later find themselves drawn towards Stem subjects, where it is indeed very useful. The rest know perfectly well that they will never reach this promised land. What can be done for them?

An indication is given by the speed-camera example. It shows that regression to the mean is an important mathematical phenomenon that can be explained without the need for any calculation or symbol manipulation. Why not use examples like this to bring statistics alive? That way we could explain the point of means and standard deviations rather than just asking people to calculate them.

Of course, some proficiency in calculation and symbol manipulation is important – and it improves one’s conceptual understanding – but it should not be all that is taught. We could also ask children open-ended questions, such as whether it is more dangerous to travel by car or by aeroplane. A question like is not explicitly mathematical, so it is less likely to trigger the brain’s off switch. And if it doesn’t, the ensuing discussion will convey why we should care about multiplication, division, averages and probabilities, what we can say about them when we do not have exact numbers handed to us on a plate, and how to frame mathematical questions to help make decisions that are of practical interest.

I am not suggesting that all maths should be introduced this way. But until our mathematics classes encourage people to think, rather than merely play games with marks on paper, the Simon Jenkinses of this world will continue to confuse mathematics with mindless symbol manipulation, attacking the subject itself when their real target should be today’s curriculum.

Failures in Maths Teaching

Pervez Hoodbhoy in The Dawn

Our generals say India’s spy agency RAW is up to its nasty tricks again. No evidence provided but, okay, we’ll buy the story for now. There are two good reasons. First, it’s safer not to question the wisdom of generals. Second, they speak from deep experience, having long played the spy-versus-spy game across borders.

So let’s provisionally assume that India’s spies have engineered the odd bomb blast here and there, and send occasional gifts to the BLF or other militant Baloch movements.

But RAW’s alleged antics are pinpricks compared to the massive and irreversible brain damage that Pakistan’s schools, colleges, and universities inflict upon their students.

Imagine that some devilish enemy has perfected a super weapon that destroys reasoning power and makes a population stupid. One measure, though not the only one, of judging the lethality of this hypothetical weapon would be lower math scores.

No such scores are actually available, but for over 40 years my colleagues and I have helplessly watched student math abilities shrivel.

Only the wealthy customers of elite private schools and universities, tethered as they are to standards of the external world, have escaped wholesale dumbing down. As for the ordinary 99pc, with the rare exception of super-bright students here or there, some form of mental polio is turning most into math duffers.

Does being poor at math really matter? After all there are plenty of intelligent people everywhere, even brilliant ones, who hate math and therefore are bad at it. But this is only because they had dull and uninspiring teachers who never taught them that math is a beautiful exercise of reason, one step at a time. Once on track, you quickly realise that math is the most magnificent, surprising, and powerful of all human achievements.

The success of the human species over other forms of life on planet Earth depends squarely on mathematics. Without math the pyramids could not have been built, navigation would be impossible, electricity could not have been discovered and put to use, factories and industries would not exist, computers and space exploration would be unimaginable, etc.

Here’s how bad our situation is: in a recent math class, I had rather typical 18-20 year-olds from non-elite schools. They had studied geometry but their teachers had not exposed them to the notion of proof — the step-by-step process in which one starts with a proposition, carefully constructs arguments, and then triumphantly arrives at the conclusion.

Instead, they were taught math as a hodgepodge of recipes. A few they remembered, the rest were forgotten.

I nearly wept to see that barely three to four students out of 60 could prove the angles of a triangle add up to 180 degrees. None could prove that similar triangles have proportional sides. Quite a few had difficulty with fractions, some did not know how to take the square root of four or nine or unless armed with a calculator, and translating even simple real-life situations (like compound interest) into equations was difficult. Twelve-year-old kids in Japan or Europe would have done better.

Their teachers are still worse. Earlier I had the misfortune of teaching math courses to college math teachers. In their late 30s or early 40s, most were staid and stable family men who had come to university, expecting to get a higher degree and hence a higher pay grade.

But for all their years of teaching math, they were blanks. Diluting my nominally ‘advanced math’ course to a beginning level course did not help. My conscience could not allow a single teacher to pass.

Could the use of English — a difficult language for all except ‘O’ and ‘A’ level students — reasonably explain this dreadful situation? I am sympathetic to this point of view and therefore use Urdu exclusively in my physics and math lectures, both in distance learning modules and in real-time teaching (except when a university’s regulations require that I teach in English). But this barely solves 10-20pc of the problem.

So then is the math curriculum at fault? It certainly can be improved but almost the same topics in math and science are listed in Pakistani curricula as would expectedly be covered by a similar cohort internationally. In fact, primary school children in Pakistan are expected to carry a bigger burden than overseas kids.

The impediment to learning proper math is just one — wrong learning goals, wrong attitudes. Mathematics does not require labs, computers, or fancy gadgetry. But it does demand mental capacity and concentration. Nothing is true in math unless established by argumentation based upon a rigorous chain of logic, with each link firmly attached to the preceding one. The teacher who cannot correctly solve a math problem by following the defined logic will suffer loss of face before his students.

Contrast this with the madressah model wherein truth is defined by the teacher and prescribed books. The teacher’s job is to convey the book contents, and the student’s job is to appropriately absorb and memorise. There are no problems to be solved, nor is challenging suppositions or checking logical consistency either encouraged or even tolerated.

Limited to religious learning, such learning attitudes are perfectly fine. But their absorption into secular parts of the education system is disastrous. The hafiz-i-science or hafiz-i-math, which are copiously produced, carry exactly zero worth.

Giving logic a back seat has led to more than diminished math or science skills. The ordinary Pakistani person’s ability to reason out problems of daily life has also diminished. There is an increased national susceptibility to conspiracy theories, decreased ability to tell friend from foe, and more frequent resort to violence rather than argumentation. The quality of Pakistan’s television channels reflects today’s quality of thought.

For too long education reform advocates have been barking up the wrong tree. A bigger education budget, better pay for teachers, more schools and universities, or changing instructional languages will not improve learning outcomes. As long as teachers and students remain shackled to the madressah mindset, they will remain mentally stunted. 

The real challenge lies in figuring out how to set their minds free.

Private schools know how to game elite universities – state-educated kids don’t have this privilege

The system fails bright pupils from ordinary backgrounds. And here’s how we all lose ...

• 
• • Paul Mason
  • The Guardian, Sunday 30 November 2014 20.00 GMT

'There is, in short, massive asymmetry of information in the post-16 education system and the critical determinant is class.' Photograph: Alamy

Let’s call him Matt. Aged 16, he is tall, taciturn and highly talented. He goes to a state school and is about to choose his A-levels. For all kinds of reasons, he believes he should progress, via Oxbridge or the Ivy League, to become an aerospace engineer.

So should he do further maths? If maths is the new rock’n’roll in education, then further maths is a VIP enclosure that fewer than 15,000 young people a year get into.

Last week, I had the chance to put this question to the deputy head of a top private school. “By all means do further maths, but only if you are guaranteed to get an A,” came the answer, as if it were a no-brainer. It was advice born out of years of practical knowledge.

Other opinions are available of course – and that’s the problem. This year, a quarter of a million 16-year-olds will make their A-level choices relying on hearsay, myth and information that is outdated or uncheckable. Those choices will shape their options when it comes to university – and the courses they apply for will then shape their chances of getting in.

There is, in short, massive asymmetry of information in the post-16 education system and the critical determinant is class. Kids at private school can rely on schools that have continual informal contact with elite universities. The result is that – for all the hard work being done by outreach teams in Russell Group universities, and by access teams in state schools – there’s an inbuilt advantage among those going to private schools based on informal knowledge.

Last year’s results for further maths demonstrate the problem. In English state schools, further education and sixth-form colleges, about 11,100 young people sat the exam; in the private sector, which accounts for just 7% of the school population, 3,600 sat it. And private school results were better, with 69% getting A or A* versus 54% in state schools.

Government tables show that this achievement gap is even more pronounced for ordinary maths and the three main science subjects. There are numerous private websites that offer A-level advice, and anecdotally social media are abuzz with the wisdom of teenage crowds over course and subject choices.

But why isn’t there a central repository of information that would turn all this folkore into a level playing field of checkable knowledge? Why isn’t there a single, open-source database that models all specific pathways into higher education? Without it, state school students will always find it hard to win the inside-knowledge game.

At my old university, Sheffield, they told me that you need maths and physics as part of three A grades to study aerospace engineering. That’s in line with the Russell Group’s guide, which also tells you to add design/technology, computer science or further maths.

The admissions tutor of an Oxbridge college, however, tells me: “I think here they’d be worried about no further maths, especially if it was offered at school but they didn’t take it, though I do worry that we send out mixed messages about this.”

The knowledge asymmetries deepen once you realise that elite universities require additional, bespoke tests. Cambridge University’s website reveals that if you want to do engineering at Christ’s, Peterhouse or St John’s you might need to take an extra exam called Step.

In a cantankerous, unsigned diatribe, the Step chief examiner for 2014 complains that only 3.8% of applicants scored top marks. The majority were not prepared for the kind of thinking they had to do. “Curve-sketching skills were weak,” the examiner noted, together with “an unwillingness to be imaginative and creative, allied with a lack of thoroughness and attention to detail”.

I will wager that the people who scored top marks knew that their curves had to look like Leonardo da Vinci’s and that they had to demonstrate imagination and creativity – because their teachers had long experience of this exam, and the others had not. One Oxbridge admissions tutor admitted to me that such testing may add a further barrier to people from state schools.

Suppose Matt wants to go to Oxbridge more than he wants to be an aerospace engineer? Here the advice is – for those in the know – really clear. Don’t apply for the most popular courses, where there can be 12 people for every place. Work out the college and subject combinations that reduce the odds to just three or four to one.

Oxford’s website shows the success ratio for getting on to its popular engineering and economic management course is just 10%, while the success rate of applications for materials science is 42%. A senior administrator at Oxford told me that they suspected few state school teachers really understand this game of playing the ratios. State-school students and people from ethnic minorities crowd each other out by going for the same, obvious, high-ratio and vocational courses.

Why should this matter to the majority of young people, who do not aspire to go to an elite university? And to the rest of society? First, because it is creating needless inequality of opportunity and is just the most obvious example of how poor access to informal knowledge penalises state school kids. Second, because in an economy set to be dominated by information and technology, those 15,000 people who can attempt further maths each year are the equivalent of Aztec gold for the conquistadores. Their intelligence will be the raw material of the third industrial revolution.

There is no reason – other than maintaining privilege – to avoid presenting subject and course choices clearly, logically and transparently. When the system fails bright kids from non-privileged backgrounds, we all lose.

How we misunderstand risk in sport

Aggression, defence, success, failure, innovation - they are all about our willingness to take risks and how we judge them

Ed Smith in Cricinfo 

July 28, 2014

Same risk, different outcome: when a batsman goes after a bowler, he could end up being dismissed or hitting a six © Getty Images

The World Bank recently asked me to give a speech at a forum in London called "Understanding Risk". Initiall, I was unsure how I could approach the subject. How could I, an ex-sportsman turned writer, address financial experts on the question of risk?

On reflection, I realised there is another profession, followed around the world and relentlessly scrutinised, that relies almost entirely on the assessment of risk. Without risk, there can be no reward. Without risk, there are no triumphs. Without risk, there can be no progress.

And yet this entire profession, this whole sphere of human endeavour, doesn't really understand risk at all. It uses the term sloppily, even incorrectly. It criticises good risks and celebrates bad risks. It cannot distinguish between probabilities and outcomes.

It has changed its approach to risk, swapping one flawed approach for the opposite mistake. In the old amateur days, when it was run and managed like an old boys' club, there was little or no calculation of risk - merely unscientific anecdotes and old wives' tales. But the brave new dawn of social science didn't prove any better. In fact, it might be even worse. People put too much faith in maths, metrics and quantification. It has lurched from old boy's network to a pseudo-science - without pausing en route where it ought permanently to reside: with the acknowledgment that risk requires subjective but expert judgement. There is no perfect formula. If there was, everyone with a brain would succeed.

The sphere I describe, of course, is not finance or banking but professional sport. Sporting strategy - sometimes analytical and planned, sometimes instant and intuitive - always revolves around the assessment of risk. Taking risks is what sportsmen do for a living. And yet the analysis of risk does not match this practical reality. We usually talk in clichés not truths, often criticising good risks and praising bad risk-taking.

Here are four ways the sports world often misinterprets risk.

Risk is everywhere

In cricket, every attacking shot played by a batsman carries an element of risk, no matter how small. Stop playing shots and you cannot score runs. "You miss 100% of the shots you don't take," as Wayne Gretzky, the greatest ice hockey player of all time, put it.

And it is amazing when you stop playing attacking shots how much better bowlers bowl. Effective risk-taking has an intimidatory effect. Total risk-aversion the opposite: it emboldens your opponent, making him feel safe and relaxed.

In football, when a midfield player advances up the pitch, he is trying to orchestrate a goal while also reducing his own team's defensive protection. In risking creating a goal, he increases the risk of conceding one. Defenders, too, constantly weigh risks. Pressing the opposition, trying to get the ball back from them, is a risk. In moving up the pitch without possession, you create space behind you - if they are good enough to keep the ball and get past you.

But the alternative - safety-first defending - brings risks of another kind. If you never press, and always retreat into the safety of deep defensive organisation, then you rarely regain the ball. You dig your own trench, unable to threaten or frighten the opposition, merely sitting there waiting for the next wave of attack.

Tennis is all about risk. With your groundstrokes, if you are determined never to lose a point by hitting the ball long, not even once, then sadly you won't play with enough depth to make life difficult for your opponent. You will make zero errors and still lose.

And when it's your turn to return, if you never run round your backhand in the hope of hitting a forehand winner, then you will allow your opponent to settle into a comfortable serving rhythm. In the pursuit of good returning, you have to risk getting aced. You have to risk failure in the short term to give yourself a chance in the long term. You have to dare to be great.

Being right is not the same thing as events turning out well

You can be right and fail. You can be wrong and succeed.

	Sport is about problem-solving. And the best way to discover new, better methods is to allow people to experiment through trial and error. Don't see what everyone else is doing and copy it. Find a better way

Sport rarely allows for this. We say that winning "justified the decision", a classic failure to distinguish between ex ante and ex poste thinking. Instead, the real question should be: would I do the same thing again, given the information I had at the time? Coaches and captains often make the right calls and lose. And they often make the wrong calls and win. It is stupid to judge a man's judgement on a sample size of one event.

The same point applies to risks taken by players. An unthinking tribal fan will shout "hero" when a risk-taking batsman hits a six, then scream "idiot" when the same shot ends up in a fielder's hands.

What a champion to take on the bowler! What a fool to take such a risk! The inconsistency here is not the batsman's, it is the spectator's. Coward/hero, fool/champion, disgrace/legend. The same risk can lead to either assessment.

Many crucial risks are invisible 

There are risks that no one sees that still have to be taken. Critics delude themselves that the only form of bravery in sport is guts and determination. At least as important is nerve, or, put differently, the capacity to endure risk imperceptibly.

When I was commentating with Sourav Ganguly at Lord's last week, he told me that Virender Sehwag used to shout, "He missed a four!" while he was in the dressing room watching team-mates batting. Ganguly quite rightly added that missing an opportunity to do something good is just as much of a mistake as making a visible error.

Many teams imperceptibly yield an advantage through timidity, fearfulness, and anxiety about standing out for the wrong reasons - an advantage they never subsequently reverse.

During the last Ashes series, I used this column to develop the metaphor of looking at sport as an old-fashioned battlefield. As the front lines engage and each army tries to advance, the direction of travel will be determined by tiny acts of skill and bravery - and equally imperceptible acts of risk aversion.

Somewhere on the front line, an infantryman inches a foot closer to his ally, hiding his own shield slightly behind his friend's. Hence one man becomes fractionally safer - but if the action is repeated a thousand times, the front line becomes significantly narrower and weaker as a whole. No one individual can be singled out as a hopeless failure. But the group suffers a collective diminution.

So it is in sport. When a batsman fails to hit a half-volley for four because he is too cautious, an opportunity is wasted to exploit an advantage offered to his team.

We talk a great deal about momentum, but not enough about how momentum is created. Once the whole army is retreating, even the bravest soldiers can fail to hold the line. We talk of courage when the tide has already turned. So in place of the usual clichés, "out-fought", "out-toughed", "out-hungered", I have a simpler word: outplayed. Or, even better, "quietly, perhaps indiscernibly, defeated by superior risk-taking".

The essential risk of being prepared to look silly

This is how sport moves forward. In 1968, a professional athlete had a crazy idea. Madder still, he had this idea just before the tournament event of his life. He wanted to rip up the coaching manual and do it all his own way. His coaches told him to forget about it, to stick with the old way of doing things, not to rock the boat.

He ignored them. He was a high-jumper, and he instinctively wanted to go over the bar head first, back down - not, as everyone else did, leg first, face down. At the 1968 Mexico Olympics, despite everyone telling him he was mad, he went ahead with his revolutionary technique. And how did it work out? He won a gold medal and set a new world record. He was called Dick Fosbury and he'd just invented the Fosbury Flop.

Sport is about problem-solving. A challenge is set: kick the ball into the net; hit the ball over the boundary; jump over the bar. From then on, solutions evolve, sometimes deliberately, sometimes by accident. And the best way to discover new, better methods is to allow people to experiment through trial and error. Don't see what everyone else is doing and copy it. Find a better way.

The left-field question is the one to ask. Why shouldn't I jump over the high-jump bar head first? Why shouldn't I aim my sweep shot towards off side where there aren't any fielders (the reverse sweep, the switch hit)?

Sport moves forward when it is irreverent, resistant to authority. The greatest cricketer of all time, Don Bradman, used a technique that no one has dared to try out a second time. His bat swing started way out to the side, rather than as a straight pendulum line from behind him.

Let me repeat. The method that made Bradman one and a half times better than the second-best player was consigned to the rubbish bin of sporting ideas. Bradman was prepared to look stupid by risking a unique rather than textbook technique. Others have been unwilling or unable to follow.

Bradman, however, benefited from one huge slice of luck. He escaped the greatest risk that can befall any genius: formal education. He learnt to bat on his own, using the empirical method, without a coaching manual. As a child he would repeatedly hit a golf ball against the curved brick base of his family's water tank.

Here is a startling thought. How many Bradmans were persuaded to try the usual technique? How many Fosburys were talked out of taking a chance?

In the course of trying to be different and better, you have to bear the risk of being different and worse.

How internet dating became everyone's route to a perfect love match

The algorithm method: how internet dating became everyone's route to a perfect love match

Six million Britons are looking for their perfect partner online before Valentine's day on Friday, but their chance of success may depend on clever maths rather than charisma

• James Bridle
• The Observer, Sunday 9 February 2014

Six million Britons visit dating sites each month. Photograph: Tom Merton/Getty Images/OJO Images RF

In the Summer of 2012, Chris McKinlay was finishing his maths dissertation at the University of California in Los Angeles. It meant a lot of late nights as he ran complex calculations through a powerful supercomputer in the early hours of the morning, when computing time was cheap. While his work hummed away, he whiled away time ononline dating sites, but he didn't have a lot of luck – until one night, when he noted a connection between the two activities.

One of his favourite sites, OkCupid, sorted people into matches using the answers to thousands of questions posed by other users on the site.

"One night it started to dawn on me the way that people answer questions on OkCupid generates a high dimensional dataset very similar to the one I was studying," says McKinlay, and it transformed his understanding of how the system worked. "It wasn't like I didn't like OkCupid before, it was fine, I just realised that there was an interesting problem there."

McKinlay started by creating fake profiles on OkCupid, and writing programs to answer questions that had also been answered by compatible users – the only way to see their answers, and thus work out how the system matched users. He managed to reduce some 20,000 other users to just seven groups, and figured he was closest to two of them. So he adjusted his real profile to match, and the messages started rolling in.

McKinlay's operation was possible because OkCupid, and so many other sites like it, are much more than just simple social networks, where people post profiles, talk to their friends, and pick up new ones through common interest. Instead, they seek to actively match up users using a range of techniques that have been developing for decades.

Every site now makes its own claims to "intelligent" or "smart" technologies underlying their service. But for McKinlay, these algorithms weren't working well enough for him, so he wrote his own. McKinlay has since written a book Optimal Cupid about his technique, while last year Amy Webb, a technology CEO herself, published Data, a Love Story documenting how she applied her working skills to the tricky business of finding a partner online.

Two people, both unsatisfied by the programmes on offer, wrote their own; but what about the rest of us, less fluent in code? Years of contested research, and moral and philosophical assumptions, have gone into creating today's internet dating sites and their matching algorithms, but are we being well served by them? The idea that technology can make difficult, even painful tasks – including looking for love – is a pervasive and seductive one, but are their matchmaking powers overstated?

 The Kiss, 1901-4, by sculptor Auguste Rodin. Photograph: Sarah Lee for the Guardian

In the summer of 1965, a Harvard undergraduate named Jeff Tarr decided he was fed up with the university's limited social circle. As a maths student, Tarr had some experience of computers, and although he couldn't program them himself, he was sure they could be used to further his primary interest: meeting girls. With a friend he wrote up a personality quiz for fellow students about their "ideal date" and distributed it to colleges across Boston. Sample questions included: "Is extensive sexual activity [in] preparation for marriage, part of 'growing up?'" and "Do you believe in a God who answers prayer?" The responses flooded in, confirming Tarr's suspicion that there was great demand for such a service among the newly liberated student population. Operation Match was born.

In order to process the answers, Tarr had to rent a five-ton IBM 1401 computer for $100 an hour, and pay another classmate to program it with a special matching operation. Each questionnaire was transferred to a punch-card, fed into the machine, and out popped a list of six potential dates, complete with address, phone number and date of graduation, which was posted back to the applicant. Each of those six numbers got the original number and five others in their response: the program only matched women with their ideal man if they fitted his ideal too.

When Gene Shalit, a reporter from Look magazine, arrived to cover the emerging computer-dating scene in 1966, Operation Match claimed to have had 90,000 applications and taken $270,000 in revenue. Even at the birth of the computer revolution, the machine seemed to have an aura about it, something which made its matches more credible than a blind date or a friend's recommendation. Shalit quoted a freshman at Brown University who had dumped her boyfriend but started going out with him again when Operation Match sent her his number. "Maybe the computer knows something that I don't know," she said. Shalit imbued it with even more weight, calling it "The Great God Computer".

The computer-dating pioneers were happy to play up to the image of the omniscient machine – and were already wary of any potential stigma attached to their businesses. "Some romanticists complain that we're too commercial," Tarr told reporters. "But we're not trying to take the love out of love; we're just trying to make it more efficient. We supply everything but the spark." In turn, the perceived wisdom of the machine opened up new possibilities for competition in the nascent industry, as start-up services touted the innovative nature of their programs over others. Contact, Match's greatest rival, was founded by MIT graduate student David DeWan and ran on a Holywell 200 computer, developed in response to IBM's 1401 and operating two to three times faster. DeWan made the additional claim that Contact's questions were more sophisticated than Match's nationwide efforts, because they were restricted to elite college students. In essence, it was the first niche computer-dating service.

Over the years since Tarr first starting sending out his questionnaires, computer dating has evolved. Most importantly, it has become online dating. And with each of these developments – through the internet, home computing, broadband, smartphones, and location services – the turbulent business and the occasionally dubious science of computer-aided matching has evolved too. Online dating continues to hold up a mirror not only to the mores of society, which it both reflects, and shapes, but to our attitudes to technology itself.

The American National Academy of Sciences reported in 2013 that more than a third of people who married in the US between 2005 and 2012 met their partner online, and half of those met on dating sites. The rest met through chatrooms, online games, and elsewhere. Preliminary studies also showed that people who met online were slightly less likely to divorce and claimed to be happier in their marriages. The latest figures from online analytics company Comscore show that the UK is not far behind, with 5.7 million people visiting dating sites every month, and 49 million across Europe as a whole, or 12% of the total population. Most tellingly for the evolution of online dating is that the biggest growth demographic in 2012 was in the 55+ age range, accounting for 39% of visitors. When online dating moves not only beyond stigma, but beyond the so-called "digital divide" to embrace older web users, it might be said to have truly arrived.

It has taken a while to get there. Match.com, founded in 1993, was the first big player, is still the biggest worldwide, and epitomises the "online classifieds" model of internet dating. Match.com doesn't make any bold claims about who you will meet, it just promises there'll be loads of them. eHarmony, which followed in 2000, was different, promising to guide its users towards long-term relationships – not just dating, but marriage. It believed it could do this thanks to the research of its founder, Neil Clark Warren, a then 76-old psychologist and divinity lecturer from rural Iowa. His three years of research on 5,000 married couples laid the basis for a truly algorithmic approach to matching: the results of a 200-question survey of new members (the "core personality traits"), together with their communication patterns which were revealed while using the site.

Whatever you may think of eHarmony's approach – and many contest whether it is scientifically possible to generalise from married people's experiences to the behaviour of single people – they are very serious about it. Since launch, they have surveyed another 50,000 couples worldwide, according to the current vice-president of matching, Steve Carter. When they launched in the UK, they partnered with Oxford University to research 1,000 British couples "to identify any cultural distinctions between the two markets that should be represented by the compatibility algorithms". And when challenged by lawsuits for refusing to match gay and lesbian people, assumed by many to be a result of Warren's conservative Christian views (his books were previously published in partnership with the conservative pressure group, Focus on the Family), they protested that it wasn't morality, but mathematics: they simply didn't have the data to back up the promise of long-term partnership for same-sex couples. As part of a settlement in one such lawsuit, eHarmony launched Compatible Partners in 2009.

Carter says: "The Compatible Partners system is now based on models developed using data collected from long-term same-sex couples." With the rise of Facebook, Twitter, and celebrity-driven online media, have come more personalised and data-driven sites such as OkCupid, where Chris McKinlay started his operation. These services rely on the user supplying not only explicit information about what they are looking for, but a host of assumed and implicit information as well, based on their morals, values, and actions. What underlies them is a growing reliance not on stated preferences – for example, eHarmony's 200-question surveys result in a detailed profile entitled "The Book of You" – but on actual behaviour; not what people say, but what they do.

In 2007, Gavin Potter made headlines when he competed successfully in the Netflix Prize, a $1m competition run by the online movie giant to improve the recommendations its website offered members. Despite competition from teams composed of researchers from telecoms giants and top maths departments, Potter was consistently in the top 10 of the leaderboard. A retired management consultant with a degree in psychology, Potter believed he could predict more about viewers' tastes from past behaviour than from the contents of the movies they liked, and his maths worked. He was contacted by Nick Tsinonis, the founder of a small UK dating site called yesnomayb, who asked him to see if his approach, called collaborative filtering, would work on people as well as films.

Collaborative filtering works by collecting the preferences of many people, and grouping them into sets of similar users. Because there's so much data, and so many people, what exactly the thing is that these groups might have in common isn't always clear to anyone but the algorithm, but it works. The approach was so successful that Tsinonis and Potter created a new company, RecSys, which now supplies some 10 million recommendations a day to thousands of sites. RecSys adjusts its algorithm for the different requirements of each site – what Potter calls the "business rules" – so for a site such as Lovestruck.com, which is aimed at busy professionals, the business rules push the recommendations towards those with nearby offices who might want to nip out for a coffee, but the powerful underlying maths is Potter's. Likewise, while British firm Global Personals provides the infrastructure for some 12,000 niche sites around the world, letting anyone set up and run their own dating website aimed at anyone from redheads to petrolheads, all 30 million of their users are being matched by RecSys. Potter says that while they started with dating "the technology works for almost anything". RecSys is already powering the recommendations for art discovery site ArtFinder, the similar articles search on research database Nature.com, and the backend to a number of photography websites. Of particular interest to the company is a recommendation system for mental health advice site Big White Wall. Because its users come to the site looking for emotional help, but may well be unsure what exactly it is they are looking for, RecSys might be able to unearth patterns of behaviour new to both patients and doctors, just as it reveals the unspoken and possibly even unconscious proclivities of daters.

 Tinder is a new dating app on smartphones.

Back in Harvard in 1966, Jeff Tarr dreamed of a future version of his Operation Match programme which would operate in real time and real space. He envisioned installing hundreds of typewriters all over campus, each one linked to a central "mother computer". Anyone typing their requirements into such a device would receive "in seconds" the name of a compatible match who was also free that night. Recently, Tarr's vision has started to become a reality with a new generation of dating services, driven by the smartphone.

Suddenly, we don't need the smart algorithms any more, we just want to know who is nearby. But even these new services sit atop a mountain of data; less like Facebook, and a lot more like Google.

Tinder, founded in Los Angeles in 2012, is the fastest-growing dating app on mobile phones but its founders don't like calling it that. According to co-founder and chief marketing officer Justin Mateen, Tinder is "not an online dating app, it's a social network and discovery tool".

He also believes that Tinder's core mechanic, where users swipe through Facebook snapshots of potential matches in the traditional "Hot or Not" format, is not simple, but more sophisticated: "It's the dynamic of the pursuer and the pursued, that's just how humans interact." Tinder, however, is much less interested in the science of matching up couples than its predecessors. When asked what they have learned about people from the data they have gathered, Mateen says the thing he is most looking forward to seeing is "the number of matches that a user needs over a period of time before they're addicted to the product" – a precursor of Tinder's expansion into other areas of ecommerce and business relationships.

Tinder's plans are the logical extension of the fact that the web has really turned out to be a universal dating medium, whatever it says on the surface. There are plenty of sites out there deploying the tactics and metrics of dating sites without actually using the D-word. Whether it's explicit – such as Tastebuds.fm, which matches up "concert buddies" based on their Spotify music tastes – or subtle, the lessons of dating research have been learned by every "social" site on the web. Nearly every Silicon Valley startup video features two photogenic young people being brought together, whatever the product, and the same matching algorithms are at work whether you're looking for love, a jobbing plumber, or a stock photograph.

Over at UCLA, Chris McKinlay's strategy seems to have paid off. After gathering his data and optimising his profile, he started receiving 10-12 unsolicited messages every day: an unheard of figure online, where the preponderance of creeps tends to put most women on the defensive. He went on 87 dates, mostly just a coffee, which "were really wonderful for the most part". The women he met shared his interests, were "really intelligent, creative, funny" and there was almost always some attraction. But on the 88th date, something deeper clicked. A year later, he proposed.

Online dating has always been in part about the allure and convenience of the technology, but it has mostly been about just wanting to find "the one". The success of recommendation systems ,which are just as applicable to products as people, says much about the ability of computers to predict the more fundamental attractions that would have got McKinlay there sooner – his algorithms improved his ability to get dates, but not much on the likelihood of them progressing further.

In the end, the development of online dating tells us more about our relationship with networked technology than with each other: from "the Great God Computer", to a profusion of data that threatens to overwhelm us, to the point where it is integrated, seamlessly and almost invisibly, with every aspect of our daily lives.

Primary school maths whiz kids are set up for life

Hamish McRae in The Independent

An important, if troubling, bit of research has just been published by the Institute for Fiscal Studies, backed with some government money.

It shows that 10-year-olds who are good at mathematics earn significantly more once they reach their thirties than those who are not. The IFS took a large group of children born in April 1970, then looked at their maths and English scores 10 years later. Then, they looked at their earnings at the ages of 30, 34 and 38.

The findings showed that those who were in the top 15 per cent of maths scores at age 10, earned on average 7.3 per cent more at 30 – equivalent to £2,100 a year – than the child who scored the average in that class, even adjusting for all other factors. Those who did similarly well in English earned 1.9 per cent – or £550 – more than the middle-ranker. So, being good at English is helpful, but being good at maths is even better.

The IFS says this suggests that employers value maths skills and are prepared to bid for people who have them, and it therefore concludes that we need to invest more in lifting children's performance in maths.

This makes sense, but also carries the worry that if 10-year-olds happen to be bad at maths, they are disadvantaged through life. It would thus follow that having a bad maths teacher at primary school can really damage people's chances, while a great one can lift children up for the rest of their lives.

The task for educators is huge, and clear objectives are a help. But, if numeracy is more important in the job market than literacy, what conclusions should we draw?

Big business has corrupted economics

You know the country is in a financial mess when even establishment figures such as Rachel Lomax are calling for revolutionary thinking

• 
• • Aditya Chakrabortty
  • The Guardian, Monday 26 November 2012 20.00 GMT

Rachel Lomax: 'Where is the revolutionary thinking?' Photograph: David Sillitoe for the Guardian

Rachel Lomax is practically the definition of establishment: Cheltenham Ladies' College followed by Cambridge and the LSE; principal private secretary to then-chancellor Nigel Lawson; deputy governor of the Bank of England for five years until 2008. Which makes what she said on Friday evening all the more startling.

This being a debate on the future of capitalism in the People's Republic of Bristol, the audience were satisfyingly radical – but Lomax was just as bluntly and disarmingly political. The former Treasury mandarin made no bones about admitting that she had been part of a project of "dismantling a version of capitalism" and replacing it with "Anglo-American neo-liberalism". You'd struggle to get scholars of Thatcherism to speak with such straightforwardness, but here it was coming from one of the era's key backroom players.

And now this co-architect of Britain's economic model as good as admitted that the system she had helped create was broken. But Lomax had one question: "Where is the revolutionary thinking?"

You surely couldn't ask for a better measure of the economic mess we're in, that even members of the establishment are now calling for revolution.

Striking as it is, such despair isn't exceptional. Indeed, it now appears endemic among the policy-making elite. Whether you look at Westminster or Threadneedle Street, Britain's economic officials reek of policy fatigue – of having riffled through all the pages in their textbooks without getting a good answer.

Lomax's former colleagues at the Bank of England have chucked £375bn at the economy as part of a quantitative-easing programme – to no great avail. Five years after the collapse of Northern Rock, Mervyn King is warning that the slump may last another half a decade. And as will become clear when George Osborne delivers next week's pre-budget report, the chancellor no longer bothers to pretend that his cuts are working, but simply (and correctly) maintains that things would be about as bad under Labour's existing plans.

For the rest of us, that means all those gloomy warnings about a Japan-style lost decade in wages and economic growth look like coming true. Except that in Britain, with its vast inequality and lack of social cohesion, the effects of such a long and stubborn stagnation are likely to be far worse than those borne by the Japanese. If ever there was a time for new ideas, this is it – yet there's barely even a serious economic debate.

But the giant hole spotted by Lomax is one she and her colleagues have helped cause, by practising a narrow, corrupted form of economics.

In their new book, Economists and the Powerful, Norbert Häring and Niall Douglas trace how the most powerful of all the social sciences became a doctrine for helping the rich – with the aid of huge sums from business. You may be familiar with a version of this critique, thanks to the film Inside Job, which described how some of the best-known economists practising today are in the pay of Wall Street. But the history unearthed by Häring and Douglas is far more disturbing – because they argue that vested interests have slanted some of economics' most fundamental ideas.

Take the Rand corporation, an American cold-war institution that the book describes as closely linked to the Ford Foundation, which in turn was closely linked to the CIA. "It is hard to overestimate Rand's impact on the modern economic mainstream, let alone modern society," write the authors, who tot up at least 32 Nobel laureates with links with the organisation, including some of the biggest names in economics, such as Kenneth Arrow and Mancur Olson. Yet the economics it promoted assumed a society that was highly individualistic and rational. In other words, nothing like society as most of us know it, with its organisations and institutions and cultures. But the Rand researchers got round that problem by producing heavily theoretical and maths-based work, and ignoring empirical reality. From there it was a short step to the neoliberal politics everyone knows today: the kind that argues there is no such thing as society.

By focusing on the economics of economics, the authors describe an evolution of the discipline that barely anyone talks about. It is a kind of corruptonomics: "An effort that was generously funded by businessmen and the military in the name of cementing the power and legitimacy of their selves and their beliefs."

What makes this argument so striking is that Häring started off as a "true believer" in economics. He did his PhD under one of the most eminent academics in Germany, before waltzing off to a highly paid job with Commerzbank. It took him years of delving into the archives to arrive, reluctantly at first, at the conclusion that the subject he had spent years studying and practising was rotten. And while the influence of money on the discipline is largely a US phenomenon, the lopsided subject it produced is now taught at all the leading universities and practised at the major institutions.

The IMF and the World Bank employ economists from all over the world, but it is striking how many of them come from so few universities.

This then is at least part of the answer to Lomax's question. Mainstream economics now preaches a dogma that is particularly agreeable to the elite and has chased most dissenters out of its faculties. Meanwhile the other social sciences lack the confidence or the resources to take on economics. Where's the revolutionary thinking? I suspect Lomax, and others, will be asking that question for a long time.