Learning as a Product Versus Learning as Process

Many of the theories of learning that were developed during the first decades of the twentieth century tended to conceptualize learning as an end product or outcome – most often as a distinct change in behavior.

Students and educators who subscribe to this notion of learning-as-product tend to see learning as consisting of the following:

  • A quantitative increase in knowledge
  • Memorizing or storing information that can later be retrieved.
  • Acquiring facts, skills and methods that can be retained and used as necessary.

However, it is also possible to see learning as an ongoing process, and people who subscribe this notion of learning tend to describe learning as:

  • Learning as making sense or abstracting meaning, learning that involves relating parts of the subject matter to each other and to the real world.
  • Learning as interpreting and understanding reality in a different way, learning that involves comprehending the world by reinterpreting knowledge.

These later two descriptions of learning as an ongoing process see the learner as building upon his or her previous experiences and, in some instances, changing his or her behavior as a result.

Learning as a process is the view of learning that many contemporary educationalists and psychologists would concur with. As Bruner (1996) puts it ‘learning is not simply a technical business of well managed information processing’. Instead, learning might also be seen to involve individuals having to make sense of who they are and develop an understanding of the world in which they live. From this perspective, learning can be seen as a continuing process of ‘participation’ rather than a discrete instance of acquisition.

IMO working within a marketised education system encourages us to see learning as a product – that is the ‘bottom line’ of education is the results, and there is much store placed on exam-training in order to game the system and get better results (the final outcome), and students, teachers and parents increasingly judge the quality of a school or it teachers on their ability ‘to deliver results’.

This is, in fact, an extremely narrow and shallow way to judge the quality of education.

If, on the other hand, we consider ‘learning as a process’ this forces us to focus on the quality of the learning experience and the context in which learning takes place – reflecting on the marketised system we realize what a poor educational experience many of us get (teachers and students alike) because of the pressure to achieve results. In such a system, there is little time to ‘understand the world and who we are’.

Another benefit of seeing learning as a process is that we come to realize that ‘learning’ takes place in many contexts outside of the formal education system – at home, at work, and through the many informal channels through which we ‘develop ourselves’.

Maybe more of us need to forget formal education with its obsession with learning as a product, and instead just focus more on life-long learning and self-development outside of formal education?

Sources

Selwyn (2017) Education and Technology – Key Issues and Debates

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Should girls be allowed to wear trousers in school?

britney-spears-schoolgirl

Most UK schools have introduced trousers for girls into their uniform codes in the last twenty years, but some continue to ban them and will send home any girl who turns up wearing trousers, at least according to Trousers for All, which campaigns to give girls the option to wear trousers as part of their school uniform.

Trousers for all notes that ‘The ban on trousers for girls covers the entire spectrum of schools: primary, secondary, public and private, faith and non-faith”.

While it might seem like a throwback to the 1970s, or even the 1870s, this really does go on today – take this 2016 Mumsnet discussion as an example:

school skirts

Despite the above, there does seem to be widespread support (there certainly is on Mumsnet) for schools adopting uniform policies which either stipulate a ‘skirt ban’, so that that both boys and girls must wear trousers only, or that girls at least have a choice over whether they wear trousers or a skirt.

The following arguments have been put forward for allowing girls the freedom to wear trousers:

Firstly, it seems to be a pretty blatant breach of the government’s own 2010 equality act, and while it hasn’t been tested in court yet, it seems unlikely that if a parent mounted a legal challenge against a school banning their female child from wearing trousers, the school would lose – I mean, it’s been a workplace norm for 40 years now after all!

Secondly, forcing girls to wear dresses restricts their sense of freedom (Guardian Opinion Article (2017), and it does seem somewhat hypocritical that schools are expected to inspire in children a sense that ‘they are free to achieve anything they want’, except for wearing trousers in school for the next few years, if you’re a girl.

Thirdly, according to Becky Francis: “The stipulation that boys wear trousers while girls must wear skirts promotes messages that boys are active, while girls should be less active, decorative, and ‘demure’.”  (Professor Becky Francis, Director of UCL Institute of Education).

Trousers for All takes this a step further, suggesting that… ‘Schools forcing girls to wear skirts is equivalent to states forcing females to wear a veil and to companies forcing females to wear high heels. All of these are expressions of sexism.’

What do you think: is it right for schools to ban girls from wearing trousers?

 

Deeper Analysis: will a gender neutral clothing policy end the ‘policing of girls’ bodies in schools’? 

Laura Bates (of Everyday Sexism)  makes the argument that whether we have a gender neutral clothing policy in schools or not, girls bodies are still going to be ‘policed’ in school more than boys, citing examples of girls being sent home for wearing skirts deemed to be too short (and distracting to boys and teachers), and even examples of girls who have been sent home for wearing trousers which were too tight.

girls tight trousers

The trousers which were too tight for school

You also might like to contrast the way in which the above cases are treated, compared to the fact that these boys who turned up to school in skirts to protest their school’s ‘no shorts’ policy face no disciplinary action whatsoever. Maybe, just maybe, this reflects the fact that schools on average have a greater range of rules policing female compared to male bodies?????

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China’s Social Credit System: Big Data meets Big Brother

Most of us are used to having our daily activities constantly monitored and evaluated – what we buy, how much tax we pay (or not), what television programmes we watch, what websites we visit, where we go, how ‘active’ we are’, who our friends are and how we interact with them – such monitoring is now done routinely via Amazon, Facebook, and Google.

Now, imagine if all of that ‘big data’ was fed to the central government, and mashed into a single number which would be our ‘citizen score’ which in turn would measure the value of our contribution to our nation and which would inform everyone of how patriotic, politically sound and trustworthy we are as a person.

And imagine further if that ‘citizen score’ determined our eligibility for certain jobs, our creditworthiness, where our children could go to school, or even our chances of getting a date.

This isn’t fantasy, China is in the process of developing such a Social Credit System, which will be mandatory by 2020. Presently, the Chinese government is liaising with various big data companies and trialing out schemes in order to figure out what kinds of data to collect, and what algorithms to use to determine an individual’s final ‘citizen score’.

The Trial Run…

One company which is set to be a major player in running China’s social credit system is Alibaba, which is currently trialling a ‘credit ranking scheme’ which people can voluntarily sign up to.

The scheme gives people a score of between 350 and 950, based on data collected from five major categories…

  1. Credit history – does the person pay their bills on time?
  2. Ability to fulfill contractual obligations on time
  3. Personal information – mobile phone number, address
  4. Behaviour and preference – such as what products someone buys – people who buy nappies are given a higher score, because parents tend to be more responsible, people who spend 10 hours a day playing video games are given a lower score.
  5. Interpersonal relationships – who your friends are and what you say on social media — those who ‘big up the Chinese economy’ get a higher score, for example.

It’s the the fourth and fifth categories above which are the most interesting… the first three are pretty standard (insurance companies in most countries will use these to assess premiums), but the last two involve turning personal comments into social and political capital…. they really politicize the personal!

When China’s social credit system ‘goes live’ in 2020, private companies will essentially be spying for the Chinese government – and negative tweets about Tiananmen Square for example, will hurt your social credit score.

FFS DO NOT thumbs up the tank guy!

And if your friends post negative tweets about Tiananmen Square, well, that will also make your score go down!

Rewards and Punishments

Volunteers who are currently signed up to Alibaba’s trial get rewards if they get a high credit score – preferential access to loans if they get a score above 600, and if they get to 650 they get faster check-ins at hotels and airports.

When the system eventually goes live in 2020, people with lower citizen scores will be punished – with slower internet speeds, restricted access to restaurants and will lose the right to freely travel abroad, for example.

As the government states the social credit system will ‘allow they trustworthy to roam everywhere under heaven while making it hard for the discredited to take a single step’.

Is it that different to what we’ve got in the West?

While this may look like a horrific meeting between George Orwell’s 1984 and Pavlov’s dogs, maybe this isn’t that different to western big data management systems?

We’ve had credit scoring for 70 years now, that doesn’t exist in China yet, so this could just be a rapid development of what here has evolved by stealth.

And as to using personalized data….. individuals already rate restaurants, movies and books, and each other!, and various companies routinely scrutinize big data….maybe we are also getting closer to the Chinese concept of ‘life scoring’ as our real world and online worlds merge.

Sources

Modified from The Week, November 2017

Book – Rachel Botsman (2017) Who Can You Trust: How Technology Brought Us Together – and Why it Could Drive Us Apart

 

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The Ganges and Urbanisation

Taking a trip down the Ganges in India turns out to be a great way of exploring the relative advantages and disadvantages of urban living compared to rural living in India, and a great way of exploring the advantages and disadvantages of urbanisation, a topic in A-level sociology’s international development module.

Thankfully for us plebs, you don’t need to have to go on a gap yah to experience such a trip, you can just settle for watching the recent BBC documentary – ‘The Ganges with Sue Perkins‘ in which she explores various villages, towns and cities along India’s longest river.

India Urbanisation.png

I can thoroughly recommend the first 15 minutes of episode three of this documentary – in which Perkins visits the rapidly developing city of Patna  – she hooks up with half a dozen young Indian women learning trades, and seeming to be undergoing the whole ‘female empowerment’ thing, which seems in line with modernisation theory’s idea that urban settings break traditional values, and these women certainly seem to be looking forward to future lives of work based on a solid education, which would not be the case for them had they stayed in their villages.

India women.png

However, where marriage is concerned, it turns out that their parents will still be choosing their husbands for them, so this isn’t modernisation theory writ large… it seems gendered traditions are still strong in India, at least in this example.

NB – there’s a lot more observations which demonstrate the complex interplay between modernisation and tradition in India all the way through this documentary – all in all, a very entertaining way to explore the non-linear ways in which ‘development’ occurs in India today.

Posted in Global Development, Industrialisation and urbanisation | 2 Comments

$450 million for a Painting – WTF..?

So, you’re a multi-billionaire, you have $450 million kicking about, but your’re bored of all the usual gaudy bling bullshit…

 

This poll was inspired by today’s news that Leonardo Da Vinci’s ‘Salvator Mundi’, painting sold for $400m at auction today, with a grand total of $450 million once Christie’s auction house had added on its $50 million commission.

expensive painting

Leonardo Da Vinci’s ‘Masterpiece’

Now we may never actually know who bought this painting, but assuming it’s an individual (although  it may have been bought by a company or conglomerate), this raises the question of how much wealth you must have to be able to spend this much money on a painting!

Surly we must be looking at someone worth over $10 billion, so probably someone from the top 100 or so wealthiest people, possibly one of these from Forbe’s rich list, given that it’s unlikely that anyone’s going to risk more than 5% of their TNW on one investment, unless they really LOVE renaissance art or of course.

Anyway, whoever the anonymous buyer is, all mega-purchases like this do for me is remind of the existence of the global super-rich – that handful of billionaires that make up the top 0.00001% of the world’s population – domains like Christie’s auction house are their’s, and purchases of items in the several millions of dollars a regular occurrence.

This event is just a painful reminder of how much of a toss the global elite don’t give about global poverty. Between them, those present at that auction house yesterday could have transformed the lives of so many. NB I know it’s not THAT simple – money for development often gets misspent, it has unintended consequences etc etc… so I am being a bit idealistic, all I’m trying to do here is get some perspective on the enormous sum spent on that painting.

Here’s one calculation that does just that…

  • According to calculations by Oxfam, £250 000 is the sum required to provide clean drinking water to 16 800 people in Ethiopia.
  • $450 000 000/ £275 000 = 1636.36 rec
  • 1636.36 * 16 800 = 27 490, 848 people.

I don’t know about you, but I’m really not comfortable with the co-existence of global problems such as lack of access to clean water and a global Eloi jet setting around the world buying high status items at luxury auction houses.

 

 

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Global Gender Inequalities – An Overview

Gender Inequalities in Employment –

  • For every dollar earnt by men, women earn 70-90 cents.
  • Women are less likely to work than men – Globally in 2015 about three quarters of men and half of women participate in the labour force. Women’s labour force participation rates are the lowest in Northern Africa, Western Asia and Southern Asia (at 30 per cent or lower).
  • When women are employed, they are typically paid less and have less financial and social security than men. Women are more likely than men to be in vulnerable jobs — characterized by inadequate earnings, low productivity and substandard working conditions — especially in Western Asia and Northern Africa. In Western Asia, Southern Asia and Northern Africa, women hold less than 10 per cent of top-level positions.
  • When all work – paid and unpaid – is considered, women work longer hours than men. Women in developing countries spend 7 hours and 9 minutes per day on paid and unpaid work, while men spend 6 hours and 16 minutes per day. In developed countries, women spend 6 hours 45 minutes per day on paid and unpaid work while men spend 6 hours and 12 minutes per day.

Gender Inequalities in Education –

The past two decades have witnessed remarkable progress in participation in education. Enrolment of children in primary education is at present nearly universal. The gender gap has narrowed, and in some regions girls tend to perform better in school than boys and progress in a more timely manner.

However, the following gender disparities in education remain:

  • 31 million of an estimated 58 million children of primary school age are girls (more than 50% girls)
  • 87 per cent of young women compared to 92 per cent of young men have basic reading and writing skills. However, at older age, the gender gap in literacy shows marked disparities against women, two thirds of the world’s illiterate adults are women.
  • The proportion of women graduating in the fields of science (1 in 14, compared to 1 in 9 men graduates) and engineering (1 in 20, compared to 1 in 5 men graduates) remain low in poor and rich countries alike. Women are more likely to graduate in the fields related to education (1 in 6, compared to 1 in 10 men graduates), health and welfare (1 in 7, compared to 1 in 15 men graduates), and humanities and the arts (1 in 9, compared to 1 in 13 men graduates).
  • There is unequal access to universities especially in sub-Saharan Africa and Southern Asia. In these regions, only 67 and 76 girls per 100 boys, respectively, are enrolled in tertiary education. Completion rates also tend to be lower among women than men. Poverty is the main cause of unequal access to education, particularly for girls of secondary-school age.

Gender Inequalities in Health

Women in developing countries suffer from….

Poor Maternal Health (support during pregnancy) – As we saw in the topic on health and education, maternity services are often very underfunded, leading to hundreds of thousands of unnecessary female deaths as a result of pregnancy and child birth every year.

Lack of reproductive rights – Women also lack reproductive rights. They often do not have the power to decide whether to have children, when to have them and how many they should have. They are often prevented from making rational decisions about contraception and abortion. Men often make all of these decisions and women are strongly encouraged to see their status as being bound up with being a mother.

Gender Inequalities in the Experience of Overt Violence – Around the world, women are

  • Victims of Violence and Rape – Globally 1/3 women have experience domestic violence, only 53 countries have laws against marital rape.

 

  • Missing: More than 100 million women are missing from the world’s population – a result of discrimination against women and girls, including female infanticide.
  • At risk from FGM – An estimated 3 million girls are estimated to be at risk of female genital mutilation/cutting each year.
  • Girls are more likely to be forced into marriage: More than 60 million girls worldwide are forced into marriage before the age of 18. Almost half of women aged 20 to 24 in Southern Asia and two fifths in sub-Saharan Africa were married before age 18. The reason this matters is because in sub‐Saharan Africa, only 46 per cent of married women earned any cash labour income in the past 12 months, compared to 75 per cent of married men

Gender Inequalities in Politics

Between 1995 and 2014, the share of women in parliament, on a global level, increased from 11 per cent to 22 per cent — a gain of 73 per cent, but far short of gender parity.

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Are female surgeons better?

New research suggests that women make better surgeons than men. For the study, a team at the University of Toronto compared like for like procedures performed by 3,314 surgeons at a single Canadian based hospital over an eight-year period.

This revealed that the post-operative death rages for female surgeons were 12% lower than for their male counterparts – a figure that equates to one less patient dying per every 230 operations a woman performs. (Clearly the death rates are very low!).

Previous research has also found that women doctors have, on average, slightly better outcomes than male ones and that they are less likely to be struck off.

How might we explain these disparities?

  • Researchers speculate that women may be more better communicators and more cautious than men.
  • However, it may also be that women face greater obstacles to entering a male-dominated profession – with the result that only the most skilled qualify as surgeons.
  • You also have to question the representativeness of the Canadian study – in only one hospital in one country, you can hardly generalise from this!

Sources

The Week, 21 Oct 2017

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The most popular A Levels of 2017

Maths wins, with 88, 000 entries, followed by English (74, 000 entries) and just to prove we truly live in an uncritical, individualised society, Psychology comes in at 3rd with 57,000 entries.

Here’s a tree map I knocked up showing this – the interactive version is at this link

A level statistics 2017

Click here for another interactive version which allows you to compare entries from between 2014 to 2017….

A quick note on some of the categories…

Basically feel free to harangue me if you don’t think PE is a social science – I just didn’t want to call it a science, and neither does it really fit anywhere else.

I also may have cut out a few of the more minor A-levels, so this isn’t exhaustive.

 

 

 

 

 

 

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Neoliberal Policies harming Children

In 2005 New Labour liberalised the gambling the laws, ending the ban on T.V. advertising, which is in line with neoliberal policies of decreasing state regulation of private companies.

12 years later and we have a situation where endless T.V. adverts glamorise gambling and hook new converts, and where online gambling companies such as 888 Sport and Paddy Power are targeting children with their online gambling games – exploiting a loophole in the law in which allows online games to advertise to children, but not casinos etc.

Toxic Childhood.png

According to the industry’s own regulator, the Gambling Commission, around 450 000 children, or one in six of all those aged 11-15 now gamble at least once a week.

It seems that in this case, the right of gambling companies to make a profit trumps the well being of our children (*), and there’s also a nice example of Toxic Childhood here…. not only do our kids now have to deal with information overload, the contradictions of staying thin while being surrounded by junk and the pressures of over-testing, they’ve now got to deal with a potential life time of gambling addiction.

*Come on, that was good.

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What is Big Data?

Big data refers to things one can do at a large scale that cannot be done at a smaller scale. Big data analysis typically uses all available information and billions of data points to identify correlations which reveal new insights about human behaviour which are simply not available when using smaller data sets.

What is Big Data.png

Big data has emerged with the widespread digitisation of information which has made it easier to store and process the increasing volume of information available to us.

Big data is also dependent on the emergence of new data processing tools such as Hadoop which are not based on the rigid hierarchies of the ‘analogue’ age, in which data was typically collected with specific purposes in mind. The rise of big data is likely to continue given that society is increasingly engaged in a process of ‘datification’ – there is an ongoing process of companies collecting data about all things under the sun.

It’s worth pointing out at this stage that big data is also fundamentally related to the rise of large information technology companies, most obviously Google, Facebook and Amazon, who collect huge volumes of data and see that data as having an economic value.

A good example of ‘big data analysis’ is Google’s use of its search data to predict the spread of the H1N1 flue virus in 2009, based on the billions of  search queries which it receives every day. They took 50 million of the most search terms and compared them with CDC (Centre for Disease Control) data, and found 45 search terms which were correlated with the official figures on the spread of flu.

As a result, Google was able to tell how the H1N1 virus was spreading in real time in 2009 without relying on the reporting-lag which came with CDC data, which is based on people visiting doctors to report flu, a method which can only tell us about the spread of flu some days after it has already spread.

A second useful example is Oren Etzioni’s ‘Farecast company’ – which evolved to use 200 billion flight-price records to predict when the best time for consumers would be to buy plane tickets. The technology he evolved to crunch the data today forms the basis of sites such as Expedia.

There are three shifts in information analysis that occur with Big Data

  1. Big data analysts seek to use all available data rather than relying on sampling. This is especially useful for gaining insights into niche subcategories.
  2. Big data analysts give up on exactitude at the micro level to gain insight at the macro level – they look for the general direction rather than measuring exactly down to the single penny or inch.
  3. Big data analysis looks for correlations, not causation – it can tell us that something is happening rather than why it is happening.

Cukier uses two analogies to emphasise the differences of working with big data compared to the ‘sampled data’ approach of the analogue age.

Firstly, he likens it the shift from painting as a form of representation to movies – the later is fundamentally different to a still painting.

Secondly, he likens it to the fact that at the subatomic level materials act differently to how they do at the atomic level – a whole new system of laws seem to work at the micro level.

Big Data – don’t forget to be sceptical! 

This post is only intended to provide a simple, starting point definition of big data, and the above summary  is taken from a best selling book on big data (source below) – this book is very pro-big data – extremely biased, overwhelmingly in favour of it – if you buy it and read it, keep this in mind! Big data also has its critics, but more of that later.

Sources

Based on chapter 1 of ‘Mayer-Schonberger and Cuker (2017) Big Data: The Essential Guide to Work, Life and Learning in the Age of Insight’.

 

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