10 reasons why I am NOT a Social Constructivist

Educators nod sagely at the mention of ‘social constructivism’ confirming the current orthodoxy in learning theory. To be honest, I’m not even sure that social constructivism is an actual theory, in the sense that it is verified, studied, understood and used as a deep, theoretical platform for action. For most, I sense, it is a simple belief that learning is, well, ‘social’ and ‘constructed’. As collaborative learning is a la mode, the social bit is accepted without much reflection, despite its obvious flaws. Constructivism is trickier but appeals to those with a learner-centric disposition, who have a mental picture of ideas being built in the mind.

Let me say that I am not, and never have been, a social constructivist. My disbelief in social constructivism comes from an examination of the theoretical roots of the social portion of the theory, in Rousseau, Marx, and Marxists such as Gramsci and Althusser, as well as critiques of learning theorists Piaget, Vygotsky and Bruner. More specifically, I believe it is inefficient, socially inhibiting, harmful to some types of learners and blocks better theory and practice. Finally, I’ve seen it result in some catastrophically utopian failures, namely Sugata Mitra’s ‘hole-in-the-wall’ project and Negroponte’s Ethiopian farrago.

1. I don’t buy Rousseau (see Rousseau)

 

With Rousseau, we had the rebalancing of learning theory towards the learner, which was good but it may have led to an extreme reliance on naturalism and intrinsic motivation that is hard to apply in the real world. David Hume wrote, “He is plainly mad, after having long been maddish”, and although Rousseau's legacy has been profound, it is problematic. Having encouraged the idea of romantic naturalism and the idea of the noble and good child, that merely needs to be nurtured in the right way through discovery learning, he perhaps paints an over-romantic picture of education as natural development. The Rousseau legacy is the idea that all of our educational ills come from the domineering effect of society and its institutional approach to educational development. If we are allowed to develop naturally, he claims, all will be well. This may be an over-optimistic view of human nature and development, and although not without truth, lacks psychological depth. Emile, as Althusser claimed, now reads like a fictional utopia.

2. I don’t buy Marxism (see Marx, Gramsci, Althusser)

Although Karl Marx wrote little on educational theory, his influence on learning theory and practice has been profound. In The Communist manifesto Marx states that education has a ‘social’ context, which is both direct and indirect, ‘And your education! Is not that also social, and determined by the social conditions under which you educate, by the intervention direct or indirect, of society’. It was this idea that underpinned the entire communist world’s view of learning in the 20^th century, especially through Marxist theorists such as Gramsci and Althusser. In Soviet Russia and its satellite states education was remoulded around political aims and when the Cultural Revolution in China between 1949 and 1966 was unleashed, it had devastating consequences, the nadir coming with Pol Pot and the complete eradication of teachers and schools. Interestingly, when it came to re-education, Marxists states reverted to direct, didactic instruction. To this day Marxism, to a degree, persists in educational and learning theory, most notably in Gramsci, Althusser and the ‘social’ constructivism of Piaget, Vygotsky and Bruner.

3. I don’t buy Piaget (see Piaget)

Jean Piaget claimed that cognitive development proceeds in four genetically determined stages, and that they always follow the same order. This theory of child development, he called ‘genetic epistemology’, and it saw the minds of children as very different from those of adults. Importantly, this perception must be taken into account in teaching and learning. Big problem – he got it mostly wrong. His famous four ‘ages and stages’ developmental model has been fairly well demolished. How did he get it so wrong? Well, like Freud, he was no scientist. First, he used his own three children (or others from wealthy, professional families) and not objective or multiple observers to eliminate observational bias. Second, he often repeated a statement if the child’s answer did not conform to his experimental expectation. Third, the data and analysis lacked rigour, making most of his supposed studies next to useless. So, he led children towards the answers he wanted, didn’t isolate the tested variables, used his own children, and was extremely vague on his concepts. What's worrying is the fact that this Piagean view of child development, based on 'ages and stages' is still widely believed, despite being wrong. This leads to misguided teaching methods. Education and training is still soaked in this dated theory. However, on the whole, his sensitivity to age and cognitive development did lead to a more measured and appropriate use of educational techniques that matched the true cognitive capabilities of children.

4. Above all, I don’t buy Vygotsky

Lev Vygotsky, the Russian psychologist, was as influential as any living educational psychologist. In 'Thought and Language' and 'Mind in Society', along with several other texts, he presents a psychology rooted in Marxist social theory and dialectical materialism. Development is a result two phenomena and their interaction, the ‘natural’ and the ‘social’, a sort of early nature and nurture theory.

Ultimately the strength of Vygotsky’s learning theory stands or falls on his social constructivism, the idea that learning is fundamentally a socially mediated and constructed activity. This is a detailed recasting of Marxist theory of social consciousness applied to education. Psychology becomes sociology as all psychological phenomena are seen as social constructs. Mediation is the cardinal idea in his psychology of education, that knowledge is constructed through mediation, yet it is not entirely clear what mediation entails and what he means by the ‘tools’ that we use in mediation. In many contexts, it simply seems like a synonym for discussion between teacher and learner. However he does focus on being aware of the learner’s needs, so that they can ‘construct’ their own learning experience and changes the focus of teaching towards guidance and facilitation, as learners are not so much ‘educated’ by teachers as helped to construct their own learning.

In particular, it was his focus on the role of language, and the way it shapes our learning and thought, that defined his social psychology and learning theory. Behaviour is shaped by the context of a culture and schools reflect that culture. He goes further driving social influence right down to the level of interpersonal interactions. Then even further, as these interpersonal interactions mediate the development of children’s higher mental functions, such as thinking, reasoning, problem solving, memory, and language. Here he took larger dialectical themes and applied them to interpersonal communication and learning.

However, Vygotsky has a pre-Chomsky view of language, where language is acquired entirely from others in a social context. We now know that this is wrong, and that we are, to a degree, hard-wired for the acquisition of language. Much of his observations on how language is acquired and shapes thought is therefore out of date. The role, for example, of ‘inner speech’ in language and thought development is of little real relevance in modern psycholinguistics.

He prescribes a method of instruction that keeps the learner in the Zone of Proximal Development (ZPD), an idea that was neither original to him nor even fully developed in his work. The ZPD is the difference between what the learner knows and what the learner is capable of knowing or doing with mediated assistance. To progress, one must interact with peers who are ahead of the game through social interaction, a dialectical process between learner and peer. Bruner though the concept was contradictory in that you don’t know what don’t yet know. And if it simply means not pushing learners too far through complexity or cognitive overload, then the observation, or concept, seems rather obvious. One could even conclude that Vygotsky’s conclusion about mediation through teaching is false. Teaching, or peer mediation, is not a necessary condition for learning. A great deal is made of social performance being ahead of individual performance in the ZPD but there is no real evidence that this is the case. Bruner, as stated, was to point out the weakness of this idea and replace it with the concept of ‘scaffolding’.

The oft-quoted, rarely read Vygotsky appeals to those who see instruction, and teaching, as a necessary condition for learning and sociologists who see social phenomena as the primary determinant factor in learning. As a pre-Chomsky linguist, his theories of language are dated and much of his thought is rooted in now discredited dialectical materialism. For Vygotsky, psychology becomes sociology as all psychological phenomena are seen as social constructs, so he is firmly in the Marxist tradition of learning theory. One could conclude by saying that Vygostsky has become ‘fashionable’ but not as relevant as his reputation would suggest.

The resurrection of Vygotsky has led to strong beliefs and practices around the role of the teachers and collaborative learning and the belief that social context lies at the heart of educational problems. Here, it is clear that Marxist ‘class consciousness’ is replaced by ‘social consciousness’. We no longer have Marxist ideology shaping education, but we do have the ideas dressed up in sociology and social psychology.

5. Massively inefficient

Critics of social constructivism are rarely heard but the most damning criticism, evidenced by Merill (1997) and many others since, criticise social negotiation as a form of learning, as it quite simply wastes huge amounts of time to achieve collaborative and consensual understanding of what is taken by many to be right in the first place. This leads to massive inefficiencies in learning. Many, if not most, subjects have a body of agreed knowledge and practice that needs to be taught without the inefficiencies of social negotiation. This is not incompatible with an epistemology that sees all knowledge as corrigible, just a recognition, that in education, you need to know things in order to critically appraise them or move towards higher orders of learning and understanding. In addition, social constructivism largely ignores objective measures, such as genetically determined facets of personality, it is often destructive for introverts, as they don’t relish the social pressure. Similarly, for extroverts, who perhaps relish the social contact too much, social learning can disrupt progress for not only for themselves but others.

6. Damages the less privileged

Constructivist theory, even if correct, accelerates learning in the privileged and decelerates learning in the less privileged. Those with good digital literacy, literacy, numeracy and other skills will have the social support, especially at home, to progress in more self-organised environments. Those with less sophisticated social contexts will not have that social support and be abandoned to their fate. This, I believe, is not uncommon in schools. The truth is that much learning, especially in young people, needs to be directed and supported. Deliberate practice, for example, is something well researched but rarely put into practice in our schools and Universities. In fact it is studiously ignored.

7. Ignores power of solitary learning

Much of what we learn in life we learn on our own. At school, I enjoyed homework more than lessons, as I could write essays and study on my own terms. At University I learned almost everything in the quiet of my own room and the library. In corporate life, I relished the opportunity to learn on trains and planes, havens of forced isolation, peace and quiet. To this day I blog a lot and enjoy periods of intense research, reading and writing. It is not that I’ve learned everything in these contexts, only that they go against the idea that all learning needs to be social.

8. Blocks evidence-based practice

Social constructivism, is what Popper would call a ‘universal theory’, in that no matter what criticisms you may throw at it, the response will be that even these criticisms and everything we say and do is a social construct. This is a serious philosophical position and can be defended but only at great cost, the rejection of many other well-established scientific and evidence-based theories. You literally throw the baby, bath water and the bath out, all at the same time. Out goes a great deal of useful linguistic, psychological and learning theory. Out goes any sense of what may be sound knowledge and quick straightforward results. Direct instruction, drill and practice, reinforcement, deliberate practice, memory theory and many other theories and practices are all diminished in stature, even reviled.

9. Utopian constructivism

Sugata Mitra and Nicholas Negroponte have taken social constuctivism to such extremes that they simply parachute shiny objects into foreign cultures and rely on self-organised social behaviour to result in learning. It doesn’t. The hole-in-the-wall experiments did not work and Negroponte’s claims on his Ethiopian experiment are quite simply untruthful. The problem here is the slide from social constructivist beliefs to hopelessly utopian solutions. As Mark Warschauer reports “no studies have reported any measurable increase in student performance outcomes in reading, writing, language, science or math through participation in an OLPC program”.
10. Groupthink
I often ask what people who mention social contsructivism, what it emans to them, and almost universally get vague answers. I then ask for names, and often Vygotsky is mentioned. I then ask what Vygotsky texts they have read. At this point there's often a blank stare - they can rarely mention a title. My point is that social constructivism is itself a social construct, often just a phrase, certainly often a piece of groupthink, rarely thought through. It gets perpetuated in teacher training and many other contentxs as a universal truth - which it is not. It is a theory that on first hearing, flatters teachers as the primary 'mediators' in learning. In other words, it is a function of confirmation bias.

Conclusion

Why am I NOT a social constructivist – ALL OF THE ABOVE.

Big Data – bums on seats measures wrong end of learner

Education and training has always coveted data. But in any honest appraisal of this data collection we have to admit that it is largely the wrong data. There has, historically been too much focus on start and end point data. All dull inputs and outputs, it’s like judging a person simply by measuring what he eats and then excretes. It may even stretch to how long that process took to complete. What we need to focus on is the cognitive improvement of the learner. Here’s five examples of data. Mostly superficial, that accounts for the vast bulk of the data collected in education and training:

1. Bums on seats

To measure attendees, or bums on seats, is to measure the wrong end of the learner. Yet this is what so much ‘contact time’ is in our colleges and universities. I once attended a talk by the Head of Training for a global bank, where she proudly showed that x number of meals had been served in her canteen on the training campus. And they wonder why banks failed?

2. Contact time

Contact time is essentially an excuse for not measuring what is learnt. Turning up is hardly a measure of learning. Attendance is not attainment. In some cases the contact time is even more illusory, as in Higher Education in the UK they do not even count the number of students that turn up for lectures.

3. Course completion

Completion is not a measurement of attainment or competence, yet so many courses measure simply this. We have already seen how turning up is not a great measure but this is so often simply a measure of how many people just hung about until the end.

4. Summative assessments

The problem with final test and exam data is that it’s all too late. The deed is done. Exams are too often the final act in learning and an end-point. As Professor Black has shown, this final mark so often stops even the best learners from trying any harder and marks the poorer students out as failures. There’s also the problem of cramming and short-term memory.

5. Happy sheets

The evaluation of education and training is plagued with end-point data. None more futile than the obsession with happy sheets, as they measure nothing. It’s a staple of classroom courses and often the only data that is collected. Yet it says nothing about what has actually been learnt.

6. SCORM

Even in online learning SCORM was really just a package and delivery tracking mechanism for LMS vendors, build on the false premise of learning objects and although it provided a ‘standard’ for interoperability, it largely measured simple inputs and outputs.

Missing data

What’s so often missing is the data on competence. We teach what is easy to test and test what is easy to teach. That means lots of academic knowledge which is tested through paper tests, from multiple choice to essays. The actual competence measured is often just the ability to cram and remember data to pass tests, quickly forgotten.

Conclusion

Most data collection in education and training skates over the surface with data about superficial attendance, end-point assessment and opinion. What’s missing is hard data on actual performance, competences and retained knowledge. What really matters is data collected from learners as they learn. This is when data really is needed so that we can help learners succeed. So much data focuses on the deficit model in education – failure, drop-outs.

Big data: Ten level taxonomy in learning

Big Data, at all sorts oflevels in learning, reveals secrets we never imagined we could discover. It reveals things to you the user, searcher, buyer and learner. It also reveals thing about you to the seller, ad vendors, tech giants and educational institutions. Big data is now big business, where megabytes mean megabucks. Given that less 2% of all information is now non-digital, it is clear where the data mining will unearth its treasure- online. As we do more online, searching, buying, selling, communicating, dating, banking, socializing and learning, we create more and more data that provides fuel for algorithms that improve with big numbers. The more you feed these algorithms the more useful they become.

Among the fascinating examples, is Google’s success with big data in their translation service, where a trillion word data-set provides the feed for translations between over a dozen languages. Amazon’s recommendation engine looks at what you bought, what you didn’t buy, how long you looked at things and what books are bought together. This big data driven engine accounts for a third of all Amazon sales. With Netflix, their recommendation engine accounts for an astonishing three quarters of all new orders. Target, the US retailer, know (creepily) when someone is pregnant without the mother-to-be telling them. This led to an irate father threatening legal action when his daughter received a mail voucher for baby clothes. He returned a few days later, sheepishly apologizing!

Why is Big Data such a big deal in learning?

Online learning, by definition, is data, it can also produce data. This is one of the great advantages of being online, that it is a two-way form of communication. For many years data has been gathered and used in online learning. De facto standards even emerged making this data interoperable, namely SCORM and now TinCan.

However, something new has happened, the awareness that the data produced by online learning is much more powerful than we ever imagined. It can be gathered and used to solve all sorts of difficult problems in learning, problems that have plagued education and training – formative assessment, drop-out, course improvement, productivity, cost reduction and so on.

Learning = Large data

So how relevant is big data to learning? We need to start with an admission, that big data in learning is really just ‘Large data’. We’re not dealing with the unimaginable amounts of relevant data that Google bring to bear when you search or translate. The datasets we’re talking about come from individual learners, courses, individual institutions and sometimes, but rarely from groups of institutions, national tests and examinations and rarer still, from international tests or large complexes of institutions.

Ten level taxonomy of data

Data can be harvested at 10 different levels:

 

1. Data on brain

We’ve seen the commercial launch of some primitive toys using brain sensors (see my previous post) but we’ve yet to see brain and situation really hit the world of learning. Learning is wholly about changing the brain, so one would expect, at some time, for brain research to accelerate learning through cheap, consumer brain and body based technology. S Korea is developing software and hardware that may profoundly change the way we learn. With the development of an ’emotional sensor set’ that measures EEG, EKG and, in total, 7 kinds of biosignals, along with a situational sensor set that measures temperature, acceleration, Gyro and GPS, they want to literally read our brains and bodies to accelerate learning. There are problems with this approach as it’s not yet clear that the EEG and other brain data, gathered by sensors measure much more than ‘cognitive noise’ and general increases in attention or stress, and how do we causally relate these physiological states to learning, other than the simple reduction of stress. The measures are like simple temperature gauges that go up and down. However, the promise is that a combination of these variables does the job.

2. Data on learner

This is perhaps the most fruitful type of data as it is the foundation for both learners and teachers to improve the speed and efficacy of learning. At the simplest level one can have conditional branches that take input from the learner and other data sources to branch the course and provide routes and feedback to the learner (and teacher). Beyond this rule-sets and algorithms can be used to provide much more sophisticated systems that present, screen-by-screen, the content of the learning experience. There are many ways in which adaptive learning can be executed. See this paper from Jim Thompson on Types on Adaptive Learning. In adaptive learning systems, the software acts as a sort of satnav, in that it knows who you are, what you know, what you don’t know, where you’re having difficulty and a host of data about other, useful learner-specific variables. These variables can be used by the software, learner or teacher to improve the learning journey.

3. Data on course components

One can look at specific learning experiences components in a course, such as video, use of forums, specific assessment items and so on.  Peter Kese of Viidea is an expert in the analytics from recorded lectures and his results are fascinating. Gathering data from recorded lectures improves lectures, as one can spot the points at which attention drops and where key images, points and slides raise attention and keep the learners engaged. When Andrew Ng, the founder of Coursera, looked at the data from his ‘Machine Learning’ MOOC, he noticed that around 2000 students had all given the same wrong answer – they had inverted two algebraic equations. What was wrong, of course, was the question. This is a simple example of an anomaly in a relatively small but complete data set that can be used to improve a course. The next stage is to look for weaknesses in the course in a more systematic way using algorithms designed to look specifically for repeatedly failed test items. At this level we can pinpoint learner disengagement, weak and even erroneous test items, leading to course improvement. At a more sophisticated level, in a networked learning solution where the learning experiences are presented to the learner based on algorithms, screen-by-screen, items can be promoted or demoted within the network.

4. Data on course

A course can produce data that also shows weak spots. It can also show dropout rates and perhaps indications of the cause of those dropouts. One can gather pre-course data about the nature of the learners (age, gender, ethnicity, geographical location, educational background, employment profile and existing competences). During the course time taken on tasks, note taking, when learning takes place and for how long. Physiological data such as eye tracking and signals from the brain. This pre-course or initial diagnostic data can be used to determine what is presented in the course. At a more sophisticated level, it can be used as the course progresses, much as a satnav provides continuous data when you drive. Course output data from summative assessment is also useful, however, the big data approach pushes us towards not relying solely on this as was so often the case in the past. This is important for two reasons the learner themselves, knowing what they’ve achieved, not achieved, and the tutor, teacher, trainer, who can use personal data to provide formative assessment, interventions and advice based on such data. In this sales course for a major US retailer, sales staff are given sales training in a 3D simulation which delivers sales scenarios with a wide range of customers and customer needs. Individual competences are taught, practiced and tracked, so that the actual performance of the learners is measured within the simulation. Sales in the stores where staff received the simulation training were 6% greater than the control group who did traditional training. This is a good example of fine-grained data being gathered

5. Data on groups of courses

MOOCs, in particular, have raised the stakes in data-driven design and delivery of courses. In truth, less data is gathered about learners than one would imagine by the likes of Coursera and Udacity but MOOC mania has accelerated the interest in data-driven reflection. The University of Edinburgh have produced a data-heavy report on their six 2013 Coursera MOOCs taken by over 300,000 learners. The report has good data, tries to separate out active learners from window shoppers and not short on surprises. It’s a rich resource and a follow up report is promised. This is in the true spirit of Higher Education – open, transparent and looking to innovate and improve. Rather than summarise the report, I’ve plucked out the Top Ten surprises, that point towards the future development of MOOCs. If I were looking at MOOCs, I’d pour over this data carefully. That, combined with the useful information on resources expended by the University, is an invaluable business planning tool. Lori Breslow, Director of MIT Teaching and Learning Laboratory has looked at data generated by MOOC users provide clues on how to design the future of learning using massive data from “Circuits and Electronics” (6.002x), edX’s MOOC, launched in March 2012 which includes IP addresses of 155,000 enrolled students, clickstream data on each of the 230 million interactions students had with platform, scores on homework assignments, labs, and exams, 96,000 individual posts on a discussion forum and an end-of-course survey to which over 7,000 students responded.

6. Data on institution

At this organizational level, it is vital that institutions gather data that is much more fine-grained than just assessment scores and numbers of students who leave. Many institutions, arguably most have problems with drop-outs, either across the institution or on specific courses. One way to tackle this issue is to gather data to identify deep root causes, as well as spot points at which interventions can be planned.

7. Data on groups of institutions

Perhaps we should be a bit realistic about the word ‘big’ in an educational context, as it is unlikely that many, other than a few large multinational, private companies will have the truly ‘big’ data. Skillsoft, Blackboard, Laureate and others may be able to muster massive data sets, but a typical school, college or university may not.  The MOOC providers, such as Coursera and Udacity are another group that have the ability and reach to gather significantly large amounts of data about learners.

8. Data on national

National data is gathered by Governments and organisations to diagnose problems and successes and reflect on whether policies are working. This is most often input data, such as numbers of students applying for courses and who those students are and so on. Then there’s output data, usually measured in terms of exams and certification. This misses much, in terms of actual improvement and often leads to an obsession with testing that takes attention away from the more useful data about the processes of learning and teaching.

9. Data on international

At international leve the United Nations, UNESCO and others collect data, such as PISA, PIAC and OECD data, produced to compare countries performance. It is not at all clear that this data is as reliable as its authors claim. Within countries politicians then take these statistics, exaggerate their significance, cherry-pick the comparative countries (Singapore but not Finland) and use it to design and implement policies that can, potentially do great harm. PISA, for example, has huge differences in demographics, socio-economic ranges and linguistic diversity within the tested nations. The skews in the data, include the selection of one flagship city (Shanghai) to compare against entire nations. Immigration skews include numbers of immigrants, effect of selective immigration, migration towards English speaking nations, and first-generation language issues. There’s also the issue of taking longer to read irregular languages and selectivity in the curriculum. (see Leaning Tower ofPISA: 7 skews)

10. Data on web

Google, Amazon, Wikipedia, YouTube, Facebook and others gather huge amounts of data from users of their services, This data is then used to improve the service. Indeed, I have argued that Google search, Google translate, Wikipedia Amazon and other services now play an important pedagogic role in real learning. There are lessons here for education in terms of the importance of data. One should always be looking to gather data on online learning and Google Analytics is a wonderful tool.

Conclusion

Big data is changing learning by providing a sound basis for learners, teachers, managers and policy makers to improve their systems. Too much is hidden so more and more open data is needed.  Data must be open. Data must be searchable. Data must also be governed and managed. There is also the issue of visualization. Big data is about decision making by the learner, teacher or at an organizational, national or international level and must be understood through visualization. However, data is also being used to do great harm. Big data in the hands of small minds can be dangerous (see When Big Data goes bad: 6 Epic fails).

When Big Data goes bad: 6 epic fails

Data, in the wrong hands, whether malicious, manipulative or naïve can be downright dangerous. Indeed, when big data goes bad it can be lethal. Unfortunately the learning game is no stranger to both the abuse of data. Here’s six examples showing seven species of ‘bad data’.

1. Data subtraction: Ken Robinson

Don’t let the selective graphical representation of data, destroy the integrity of the data. A good example of blatant data editing is the memorable ‘ritalin’ image used by Sir Ken Robinson in his TED talk at 3.47. This image is taken from its RSA animation.

Compare Robinson’s graph with the true source.

His has no legend and he’s recalibrated states to look as if there’s zero prescriptions. To understand this data you have to look at its source to understand that the white areas represent states that did NOT participate in the study or did not have reported prescription data. It’s a distortion, an exaggeration to help make a point that the data doesn’t really support

In fact, much of what passes for fact in Sir Ken Robinson’s TED talks are not supported by any research or data whatsoever.

2. Data addition: Bogus learning theory

Ever seen this graph, or one like it? It used to be a staple in education, training and teacher training courses. Only one problem - it’s bogus.

A quick glance is enough to be suspicious. Any study that produces a series of results bang on units of ten would seem highly suspicious to someone with the most basic knowledge of statistics.

But it’s worse than nonsense, the lead author of the cited study, Dr. Chi of the University of Pittsburgh, a leading expert on ‘expertise’, when contacted by Will Thalheimer, who uncovered the deception, said, "I don't recognize this graph at all. So the citation is definitely wrong; since it's not my graph." What’s worse is that this image and variations of the data have been circulating in thousands of PowerPoints, articles and books since the 60s.

Further investigations of these graphs by Kinnamon ((2002) in Personal communication, October 25) found dozens of references to these numbers in reports and promotional material. Michael Molenda ( (2003) Personal communications, February and March) did a similar job. Their investigations found that the percentages have even been modified to suit the presenter’s needs. 

The one here is from Bersin (recently bought by Deloitte). Categories have even been added to make a point (e.g. that teaching is the most effective method of learning).

The root of the problem is an image by Edgar Dale’s depiction of types of learning from the abstract to the concrete. He has no numbers on his ‘cone of experience’ and regarded it as a visual metaphor implying no hierarchy at all.

Serious looking histograms can look scientific, especially when supported by bogus academic research. They create the illusion of good data. This is one of the most famous examples of not ’Big’ but ‘Bad’ data in the history of learning.

3. Claims beyond the data – University League Tables

University league tables are used by politicians, Universities, parents and students. But they contain a dark, dirty, data secret. They claim to rank universities but, astonishingly, tell you absolutely nothing about ‘teaching’. They often claim to have ‘measures’ on teaching, but they actually draw their data from proxies, such as employment and research activity and use nothing but indirect measures to measure teaching.

The Times rankings are a case in point. They claim that their ranking scores include teaching. In fact, only 30% is based on teaching but they use NO direct metrics. The proxies include student/staff ratios (which is skewed by how much research is done) and, even more absurdly, the ratio of PhDs to BAs. It is therefore a self-fulfilling table, where the elite Universities are bound to rise to the top. There is no direct measurement of face to face tome, lecture attendance or student satisfaction.

4. Skewed data - PISA

Like the real Leaning Tower of PISA, the OECD PISA results are built on flimsy foundations and are seriously skewed. Nevertheless, they have become a major international attraction for educators, and regularly spark off annual educational ‘international arms’ races.

Both left and right now use the ‘sputnik’ myth, translated into the ‘Chinese competitiveness’ myth, to chase their own agendas – more state funding or more privatisation. This is a shame, as the last thing we need is yet another dysfunctional , deficit debate in education. Nations have different approaches to education, different demographic and social mixes and different agendas.

The problems in the data are extreme as PISA compares apples and oranges. PISA is seriously flawed because of the huge differences in demographics, socio-economic ranges and linguistic diversity within the tested nations. There are many skews in the data, including the selction of one flagship city (Shanghai) to compare against entire nations. Immigration skews include numbers of immigrants, effect of selective immigration, migration towards English speaking nations, and first-generation language issues. There’s also the issue of taking longer to read irregular languages and selectivity in the curriculum.

Sven de Kreiner Danish statistician says PISA is not reliable at all. In the reading tests 28 questions were supposed to be equally difficult in every country. PISA has failed here as differential item functioning - items with different degrees of difficulty in different countries - are common. In fact he couldn't find any that worked without bias. Items are more difficult in some countries. He used his analysis to show that the UK moves up to 8 or down to 36. PISA assumes that DIF has been eliminated but not one single item is the same across the 56 countries

Politicians and activists distort PISA to meet their own ends. They cherry pick results and recommendations, ignoring the detail. Finland is famously quoted by the right as a high performing PISA country. Yet, it is a small, homogeneous country with no streaming, high levels of vocational education, no substantial class divisions and no private schools. Facts curiously ignored by PISA supporters.

5. Faked data

Eysenck worked with Cyril Burt at the University of London, the man responsible for the introduction of the standardised 11+ exam in the UK, enshrined in the 1944 Butler Education Act, an examination that, incredibly, still exists in parts of the UK. Burt was subsequently discredited for publishing largely in a journal that he himself edited, falsifying, not only the data upon which he based his work, but also co-workers on the research. To be precise, Burt's correlation coefficients on IQs in his twin studies were the same to three decimal places, across articles, despite the fact that  new data had been added twice twice to the sample of twins. Leslie Hearnshaw, Burt’s friend and official biographer, claimed that most of Burt's data after World War II were fraudulent or unreliable.

This is just one of many standardised tests that have become common in education but many believe that tests of this type serve little useful purpose and are unnecessary, even socially divisive. Many argue that standard tests have led to a culture of constant summative testing, which has become a destructive force in education, demotivating and acting as an end-point and filter, rather than a useful mark of success. Narrow academic assessment has become almost an obsession in some countries, fuelled by international pressure from PISA.

6. Dirty data deeds

One example of data gathering in education stands out as truly evil. In 1939, the CEO of IBM, Thomas Watson, flew across the Atlantic to meet Hitler. The meeting resulted in the Nazis leasing the mechanical equivalent of a Learning Management System (LMS). Data was stored as holes in punch cards to record details of people including their skills, race and sexual inclination and used daily throughout the 12 year Reich. . It was a vital piece of apparatus used in the Final Solution, to execute the very categories stored on the apparently innocent cards - Jews, Gypsies, the disabled and homosexuals, as documented in the book IBM and the Holocaust by Edwin Black. They were also use to organise slave labour and trains to the concentration camps.

This is not the first time the state has recoded educational details to keep tabs on potential dissent. It was common in the Stasi infused East Germany. I shared a room at University with someone who became a Stasi spy in the UK and have taken some interest in their methods. Perhaps the most meticulous storing of data ever taken by a state, right down to smells in jars, from clothing and towels placed under interviewees during interrogation. The idea was that dissenters could be found by dogs, when necessary.

Conclusion

I am a fan of Big Data in education, even though it’s really closer to ‘large or little’ sets of data. However, we must be wary of data when it is used to exaggerated claims through addition or subtraction or spearhead prescriptive programmes and extreme testing. I am appalled at the way politicians and educators take up PISA, PIAC and OECD data, with little or no detailed examination of their assumptions or relative values and use it to shape prescriptive policies that do more harm than good. Big Data in the hands of little brains is downright dangerous.

WISE 2013 – Reinventing education

The World Summit on Education in Doha, Qatar brings together educators from around the globe. They literally fly you in, put you up in a fine hotel, feed you and let you rip. Networking here, and that is its strength, is as global as you can get, as you’re guaranteed to speak to people from every continent. It is arguably, as George Siemens says, “world’s most important education conference”.
‘Reinventing education’ was this year’s theme, an admirable goal and badly needed as we know that the Millennium goals will be missed, that the existing model is flawed, costs too high and that demand is exceeds supply. So what happened?
As I said when I blogged the last WISE conference I attended (this is my 3^rd), “Education’s a slow learner - it may be more accurate to say that education has learning difficulties. The system is fixed, fossilised and, above all, institutionalised, so the rate of change is glacial. People are, by and large, trapped in the mindset of their institution and sector. In truth, small pools of innovative practice are patchy and stand little chance of wide scale adoption. Many of the speakers repeated platitudes about education being the answer to all of the world’s problems. What they were short on were solutions. Education is always seen as the solution to all problems. The problem with all this utopian talk is that it dispenses with realism.”

4 pillars of education

The plenaries were, well, institutionalised, UNESCO, in my opinion, have become part of the problem and low on solutions. They dominated many of the sessions and regurgitated old reports, clichés and truisms, none worse that their 4 Pillars of Education ‘to be, to know, to do, to live together’. This is fine, but fails on a number of counts. ‘To be’ is a banal abstraction that has no real purchase in education. ‘To know’ is an obvious truism – of course education is about knowing – but knowing what? ‘To live together’ is better but not best taught in school and classrooms. The last, ‘To do’ is good but largely ignored as education gets ever more abstract and academic, treating vocational learning as an afterthought. What we needed was the Samson of innovation to push over the UNESCO pillars and enter the temple of institutional thought to upturn a few tables that have been selling the same tired, old stuff for decades. Sorry - I’ve mixed up two parables in one sentence!

Morin – disappointing ‘discourse’

Morin opened the conference with an abstract, rambling précis of his old UNESCO paper. He’s 92 and struggled to handle his notes and microphone. It was stratospheric, a piece of French philosophy, totally detached from the real world. It’s a type of ‘discourse’ (as French philosophers like to call it) that remains rooted in dualist abstractions and dialectic, with the occasional apercu. But this approach fails to deliver concrete ideas that one could take away and apply in the real world. When asked for some real suggestions and detail, he couldn’t and fumbled through with some more discourse on ‘strategy’. Worse, it set the wrong tone for the summit. One of abstractions and a failure to address real problems.

Literacy & numeracy

But things got much better with a hard hitting session which delivered some surprises for me. First some brilliant insights from Helen Abadzi, that around 18 our minds become less plastic and open to learning literacies. You can learn the letters but it is difficult to see them come together as words. You can experience this for yourself when you learn a new language as an adult. As you rarely reach a reasonable reading speed, of around 60-80 words a minute, you forget the start of sentences before you’ve reached the end. The implications of this research are huge, that we may be wasting too much time and money trying to solve an insoluble problem. The second was that the whole literacy push in Africa and the developed world is being thwarted by poor textbooks and teaching. I have seen this for myself in Cambodia, where a literally unusable textbook was being used in a country classroom. There was a call for the abandonment of traditional ‘English’ and ‘Middle-class’ teaching methods and texts for a literal ‘letter by letter’ approach in the local language, which is rarely as irregular a English. When done well it takes around 100 days. The other issue is teacher feedback, which is often poor and misdirected in schools, focussing on the best not the worst performers in the class. As for numeracy, it’s a different class of problem, as we are all born numerate. New born babies are numerate but not literate. In truth this side of the debate wasn’t covered at all.

Small-minded debate on Big Data

This session bordered on the bizarre. As one of the most important current topics in education it deserved better. What we got were idiosyncratic, personal and to be frank, not very informed, views on the subject. John Fallon, of Pearson, was reasonably articulate and tried to keep to topic, but the other three were amateurish. I saw one ‘analytics’ expert in the room leave after 15 minutes.

For John Fallon we need to collect, analyse and interpret data give opportunities to look at education like never before and transform outcomes. We’re not short of data, it’s just that most of it is inputs such as spend, enrolments, millennium goals, broadband connectivity and so on. As I always say, to measure bums on seats is to measure the wrong end of the learner. Then there’s the outcomes; PISA PIAC, high stakes tests, artificial once year events. What we don’t use it for, said John, is to enhance learning. How do I know what’s going on in students’ minds. Big Data needs to scale. Thousands of individual interactions each and every day, across informal and formal learning. He was the only one on the panel who had any real grasp on the detail.

Divina Frau-Meigs, a sociologist, and self-styled activist for media literacies (stretching the meaning of the word activist), gave an idiosyncratic presentation based on her own flimsy research. At one point she included drawing mindmaps on paper as Big Data. It’s called BIG data for a reason. Her statement that it’s mostly dashboards and data mining missed the point. Emilio Porta an economist from Nicuaragua was obsessed with global data – UN, UNESCO, PISA and so on. He couldn’t see the flaws in having created a sort of arms race as the leaning Tower of PISA data is hopelessly skewed. htp://buff.ly/1aCLCSb Politicians distort and exaggerate these stats for their own ends. This was a very low level chit chat about a complex and serious subject. I’m not sure that any of the panel had the expertise to do it the justice it deserved.

Mindgraphs - Hans Rosling

Hans Rosling has a great TED talk on the animation of statistics. But what matters is what those statistics tell us. Rosling stunned us with his assertion that our common perceptions about population, poverty and education are worse than that of chimps! He did this with enthusiasm and humour.

What is the global literacy rate?

80%

60%

40%

20%

(answer at bottom)

Again and again he showed us that our common perceptions are misconceptions. Population is not increasing exponentially as birth rates have and are falling and the number of children in the world has stopped growing.

On education he also scotched a few myths around figures quoted by notables on the panels. What is worse, he asked POVERTY or GENDER in education? Poverty is the clear answer. Above all, we no longer have developed v developing nations but a range. Rosling should have been the opening keynote, he set the tone for a proper debate, based on real figures.

What if Finnish teachers taught in your schools?

Pasi Strahlberg posed a few questions to show that you must tackle improving your educational system holistically. It is not JUST about quality teachers, the mantra we so often hear. It’s a wide range of social issues around scrapping the private sector, not rushing things and avoiding early years ‘schooling’. Let them play until they’re 7 or 8. Don’t get obsessive about testing. This flies in the face of almost everything we do in education in the UK. We have become trapped in an arms race, where the solution to everything is more ‘competition’, more ‘schooling’, more ‘league tables’ and more ‘testing’. I also noticed that this was in direct contradiction to Julia Gillard’s prescriptive ‘testing’ approach.

Monsters and misconceptions

This was a revelation, quick fire talks on all sorts of topics and solutions, some good, some great, some awful. Let’s get the awful stuff out of the way. The talk by the Observer journalist was an anecdotal rant about how women rule the world and hapless men need to listen to them, as ‘men can’t collaborate, women do’. This was a statement so general and awful that it deserves a response. I played football nearly every night as a child, I’ve managed companies, worked in teams and have little to learn from a hackneyed journo, who has spent most of her life in solitary confinement typing out articles on subjects to a deadline. She obscured an interesting point about the feminisation of education in early years and primary by caricaturing men. OK, got that out of my system.

To counter this, we had superb presentations on hard hitting topics, like child marriages, self-sustaining schools in Uganda, MOOCs in China, Amazigh education in Morocco and the Khan Academy. This quick-fire stuff needs to be promoted and given more status, maybe themed. I particularly enjoyed the iThra talk, about an after school science programme. He had a stunning quote, “The education system is a monster, by fighting it we would have become monsters ourselves”.

Mozilla – tinker, share, make

Mark Surman showed us how to present. Face the audience, stand up, look people in the eye, speak knowledgeably but from the heart, don’t use notes and deliver a clear message. Compare this to Morin and others on the many panels that delivered the same old platitudes. Motivate, engage and excite learners. Get them to tinker, share and make things. It’s a learn by doing model that allows young minds to understand the technological world in which they live and use that technology to learn, do things and make things. What gave his message clout was the fact that he was doing this through the Mozilla Foundation, around the world, in Mozfests and Maker events.

Educators are always going on about 21^st C skills. For Surman the 4th literacy is web literacy, as the web is the new classroom, 21st century skills - 5 Cs (Classroom is not one of them)  communicate, create, culture, collaborate, community on the WEB. These skills are not well taught in schools and universities, where learners are herded into classrooms and lecture theatres, online communication tools and devices often banned and creativity rare, often squeezed by the obsession with STEM subjects. Educators are also always trying to force storytelling. Young people tell stories daily - it's called Facebook. Lifelong learning is Google, Wikipedia, Social networking and YouTube - life is not a course it's informal learning.

MOOCs

Excellent input from the knowledgeable George Siemens and the Chief Scientist for EdX. These guys know their stuff but the other two participants clearly knew nothing about MOOCs and astonishingly, had never taken a MOOC. How do I know this? I asked them and the chair. Neither had taken a MOOC. They both spoke like amateurs because they were amateurs, trotting out clichés about human interaction and drop-out without any grasp of the detail. Siemens was clearly frustrated by their uninformed negativity and explained why drop-out is not the problem people imagine it is, that pedagogy is varied and evolving and that the experience is richer than people imagine and, above all, people like them and use them. For the first time in 1000 years education that delivers quality education to massive numbers, at low cost, that people want and enjoy.

MOOCs are a wake-up call for Higher Education. MOOCs flip universities. Siemens is right, MOOCs are a supply response to a demand problem. We’ve seen more action in 1 year than last 1000 years and MOOCs will produce dramatic systemic and substantial change. Certification is NOT the point in MOOCs - only 33% wanted certification in Edinburgh MOOCs and there are plenty of ways they can be monetised.

Human interaction is an issue but in the 6 MOOCs I've taken this has been great - teaching seems intimate, peer-to-peer interaction strong, forums lively and physical meetups possible. Siemens got a little tetchy when drop-out was mentioned – rightly so. The sceptics seemed determined to look at everything within the deficit model. What about the hundreds of thousands of drop-ins? I’m absolutely amazed that so many have taken so many courses from so many places. Online experience need not be inferior. As Siemens said, let's hold classrooms and lectures to same standards as online!

There’s real fears around dominance by the private sector, but if that delivers cheaper, faster, better education, so be it. In my opinion, however, the future of MOOCs is: open platforms, open content, open pedagogies and the opening of minds. African MOOCs may unlock a billion more brains HOOKs VOOKs - High school and Vocational MOOCs are also being delivered as this is not just about HE and degrees. The MOOC session by far best at WISE talking about real reinvention and a real phenomenon.

Conclusion

This is my third WISE summit, and as usual, I met some amazing people. Thanks George Siemens, Mark Surman, Cathy Lewis Long, Derek Robertson, John Davitt, Davod Worley, Jef Staes and all of the new people I met in Doha. The Souk was a hoot (try the Iraqi restaurant there), the gala dinner hilarious (the lack of alcohol made us almost hysterical) and on the rides on the bus to and from the conference I had some of the best impromptu sessions.

Overall however, you can see the problem, a failure to engage with the real problems head-on; costs, relevance, technology, that faculty and existing teaching systems biggest barrier to progress in learning.  86% of the delegates want reinvention of education but time and time again the panels reflected and reinforced old ideas and practices, with the audience clapping every time the word ‘teacher’ was mentioned. Teachers matter, but until we recognise that teaching is neither a necessary nor sufficient condition for learning and look for some other additional solutions, WISE will forever be focussing on the wrong thing – teachers, not learners. The fear, that students may ‘manage to learn without me’ and of technology in general, is holding us back. Next year, less administrators, more innovators. The good news is that the Qatar Fundation has been doing brilliant work across the globe and announced a focus on innovation this year, with financial support for such innovation. They may be on to something here.

PS

Session on University Rankings was in Room 101!

 (Answer 80%)

MOOCs – the flipped University?

MOOCs are a phenomenon, a wake-up call for Higher Education and wake-up calls, create a sense of urgency, the first step in the process of change. Here are ten MOOC flips that explain why they may be turning traditional Higher Education on its head.

1. Flip from supply to demand

As George Siemens says, MOOCs are “a supply response to a demand problem”. They have tapped into an immense amount of frustration in that the system has remained immune to change, closed, inward looking and increasingly expensive. A global flood of learners has turned up for courses on every imaginable topic. All sorts of people, from high school kids, parents, alumni, people in the developed world to the retired, have signed up in their millions. Lifelong learning has, at last, surfaced and been made visible. This demand is not going away, it will only get bigger.

2. Flip from offline to online

Like Lady Gaga, MOOCs are a ‘phenomenon’. They came from nowhere, well almost but mostly from online learning, OER and pioneers like Siemens, Downes and Khan. We should be glad of this ‘phenomenon’ effect because I can think of only a handful of examples of similar educational ‘phenomena’ – Open University, Google search, Wikipedia and YouTube. All of these have been flips from offline to online.

3. Flip from horizontal to vertical

Suddenly, we have VOOKs (Vocational MOOCs), HOOKs (High School MOOCs) and African MOOCs. MOOCs have gone viral and spread downwards into and upwards into workplace and lifelong learning. Rather than staying within the horizontal band of higher education, traditionally based on the 18 year old undergraduate, they have flipped out of the horizontal to a vertical model.

4. Flips teaching to learning

You can’t choose your teachers at school or University but you can with MOOCs. The focus on MOOCs is not on the teacher or lecture but the learner, who chooses what course they want to do, when to start, when to watch lectures and formative work, and whether they want certification or not. They are fundamentally more leaner-centric.

5. Flips assessment

MOOC assessment is flipped in several senses. First, peer assessment flips tutor only assessment to learners assessing each others work. Second, many do MOOCs without any interest in paper certification, reversing the universal certification in the old system - it’s learning for the sake of learning. Third, you don’t need to be assessed on one day a year in a physical location within the University. You can choose when and by what method, either online through services such as ProctorU or at centres around the world through Pearson VUE.

6. Flip the standards

MOOCs have little to fear from being benchmarked against traditional campus-based courses. The online experience need not be inferior and may, in many cases, be  superior to such courses. As Siemens said, let's flip this and “hold classrooms and lectures to same standards as online!” On costs – they’re cheaper,traditional courses expensive. On numbers – they’re massive, traditional courses tiny. On access – many are available to start at any time, not once a year. On interaction with others – many have structured peer-to- peer contact, lively forums, social media offshoots and physical meetups.

7. Flips drop-out to drop-in

Drop-out in MOOCs is not the problem people imagine – it’s a category mistake, taking the old-school concept of high-school drop-out or University drop-out. and applying it wrongly in this new domain. I’m just amazed that millions have dropped in! let’s celebrate the fact that tens, even hundreds of thousands turn up for these experiences before pointing the finger. I have stopped in some MOOCs, I’ve also stopped in lots of things – that’s life. I’ve rightly stopped reading some books and watching some movies. Stopping is rational, continuing with a course you don't like for months or years is not.

8. Flips criticism

Before they’ve had a chance to breathe, sections of academia have tried to throttle the infant at birth. They have failed of course. The fact that MOOCs have been such a gadfly is as sure a sign as any that they are having impact. Let’s flip this. The immediate criticisms, on drop-out, pedagogy, inclusion, human contact, assessment and monetisation express a deeper fear, that these things may not be so great in offline Higher Education. The pedagogy is varied and evolving experience is richer than most people imagine and, above all, people like them and use them.

9. Flip from inward to outward

MOOCs are not only the outward facing democratisation of education but internally a facilitator for change and internal improvement. They hold a mirror up to Higher Education and force us all to ask more searching problems about the purpose, methods and costs. In this sense MOOCs have flipped from inward to outward delivery then flipped back into internal catalysts for change.

10. Flipped University

For the first time in 1000 years we have flipped education to delivers quality education to massive numbers, at low cost, that people want and enjoy. And what’s the response by the curmudgeons? Not in my back yard. It’s intellectual nimbyism. The reason MOOCs have succeeded, when so many smaller, even funded initiatives by and within Universities have not, is that it boldly flips the model. Inward looking attitudes have turned outwards, internal courses have flipped into external courses, the 18-year old undergraduate has been flipped into lifelong learners. The agricultural calendar has been flipped into anywhere, anytime. The offline only model has been flipped into the online model. The extortionate costs have been flipped to zero at point of delivery. What’s not to like here?

Conclusion
You might assume from above that I’m being critical of Universities. Well, to a degree, as they have their serious deficiencies in pedagogy, some poor teaching and high costs. In fact I want more and better Higher Education, not a system premised on scarcity and elitism. Like the flipped classroom, MOOCs may be the best thing that’s happened to Higher Education in the last two hundred years. It may encourage growth in higher education, based, not on the paper chase for degrees, the cramming, the poor teaching and primitive assessment but to a newer model, where students are drawn from a wider world and can try and take courses when they want from where they want. Are we in not the learning game? Can’t we celebrate access and low costs for learners? AMEN to the MOOC!