Saturday, October 18, 2014

7 digital vaccines used in Ebola crisis

Even developed countries, with state-of-the-art facilities such as the US, have experienced infected health workers. On the ground in Africa, rumours abound, one being the ‘salt-cure’ where taking large doses of salt was seen as a cure, resulting in cases of hospitalisation. Traditional animistic views of medicine have also seen people turn away from the treatment they need. There’s even political scare stories around conspiracies by government and foreign powers, where the virus is seen as a deliberate extermination tactic. At it’s most extreme Ebola-denial has taken root in Liberia, where some deny the very existence of the virus. So education, at this point, may be the most important digital vaccine against the spread of the disease.
The advantage of digital apps is their mobile delivery on platforms such as Twitter and Whatsapp, that are already being used in situ. With web-based learning, people can receive their education and training at a distance as well as avoiding potentailly hazardous group sessions. This is not to say that practical learning by doing is unnecessary, only that much of the blend can be delivered online, using images, video and audio. At the moment there is no cure or vaccine, so digital apps and education may be the best vaccines we’ve got.
1. Ebola & Whatsapp
What’s Whatsapp got to do with Ebola? Trusted information provided free by the BBC, a respected brand in Africa, is now available on Whatsapp. Peter Horricks of the BBC is right when he states the simple but obvious fact that “Information will literally save lives.” It’s accessible, it works and it’s free. If you subscribe, you get three pushed messages a day in a number of media including audio and images, as literacy is an issue. Wisely, they are also delivering the messages in English and French, which matters, as some of the countries where the outbreaks are at their worst are French speaking. Indeed, the majority of the bordering countries around the outbreak are French speaking. 
2. Ebola App
The free Ebola Prevention Mobile Application is available on the Android Play store. There is also as an SMS-based application available on CloudSMS. It has four features:
Affected Area Mapping
App shows you your location in relation to affected areas around you on map, And gives you preventive measures to help you prevent this deadly disease
Ebola Hot zone detection
The App tells you from time to time if your current location has been affected by Ebola Virus Disease Outbreak
Preventive Measures
When you are in an Ebola affected area, The App notifies you and gives you preventive measures based on the proximity of your current location to affected areas
Latest Info
Up to date Information on Ebola around you and from all over your region
Another great dimension to this story is the fact that it is being developed in West Africa by Innovative Technologies for Development Foundation (IT4D) a Nigerian Based Not for Profit organization, that was behind, a crowd sourced platform for ordinary people to prevent election rigging in Nigerian Elections.The Ebola app work coordinated by Akinmade Akintoye, CTO of CloudWare Technologies.
3. Podcasts
Audio is the forgotten medium in learning, but in areas where literacy is a problem and you want to deliver authoritative voices on an important topic, it can be a powerful medium. Radio is still an important information channel in Africa. The BBC have already delivered a series of free podcasts, twice a week, debunking the myths along with other topics. So far the podcasts have covered the testimony from Sierra Leonean Ebola survivor, Yusif Koroma, the first person to have survived at the new treatment centre in Freetown's Connaught Hospital. It provides a rare insight into coping with the virus and recovering from it. In another they look at the importance of clear and accurate information which can help minimise the spread of the virus.
4. Ebola MOOC
A more formal approach is taken by the free MOOC Understanding the Ebola Virus and HowYou Can Avoid It, by the Irish organisation Alison. With over 10,000 completions, which can be taken on a range of devices, even mobiles, it provides a valuable educational experience. As Mike Feerick of Alison says "If new information is discovered about Ebola, or how to treat or avoid it, we can instantly relay it to a huge number of learners worldwide." A MOOC is a great way to get formal courses out, as they’re scalable, free and updatable. This is not possible through traditional educational institutions. There’s already a French version, with Arabic in the pipeline.
5. Healthmap app (data & algorithms)
This app was credited with identifying the Ebola outbreak before the WHO. Remarkably, the first signs of fever were tracked in the forested areas of Guinea on March 19. They contacted the WHO, who got their first report many days later. The Boston Children’s Hospital Team behind the app, use algorithms to find and filter thousands of social media, news and government sites. It is this unique use of big data and algorithmic selection that is coming to the fore in disease outbreak reporting and prevention.

6. Ebola Project App (Twitter)
Armil Smailhodzic’s of West Kentucky University developed an app that draws on Twitter data to identify outbreaks and news on Ebola. This app takes a different approach, in that it only tracks Twitter data. Interestingly, they found that, although people in rural Africa are suspicious of Government and outside agencies, they do Tweet. The trick was to filter Tweets from actual sources out from Tweets that just talk about those countries and sources. The app provides maps along with a Twitter feed on prevention and reports.
7. Facebook
Indirectly, all of you Facebookers have contributed to Ebola prevention, as Mark Zuckerberg has donated $25 million to the cause.  The Gates Foundation have chipped in $50 million and Paul Allen another $12 million. Between them, they have contributed more than China, Russia, S America, Australasia and the Middle East put together. This matters, as raw money matters and Governments have been known to make promises, then take the money fromother budgets or not pay up. 
Other informative, authoritative and helpful online resources include Wikipedia, WHO and the CDC. Far from being an uninformative mess, the web provides a remarkably consistent set of resources and tools that play a significant role in prevention and control. What I like about these efforts, is the delivery is often in at least two languages, although it’s all very well to do English and French, local language production may also be necessary. This is already realised on local TV broadcasts, but surely the budget could be found to apply it to the other media, especially podcasts. Sensitivity to literacy issues has also been shown through the use of different media. I’ve taken the MOOC and looked at the other apps. It was quick and easy. This is solid stuff, well designed, well written with appropriate media and a variety of delivery channels, many to mobile. The fact that its scalable, accessible and free, with the ability to update in the light of new information, is a godsend.

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Thursday, October 02, 2014

10 things that algorithms can do that teachers can't

Algorithms trump data
Love them or hate them, you use them and algorithms are here to stay. Algorithmic power drives Google, Facebook, Amazon, Netflix and many other online services, including many more professional services you use, such as communications, finance, health, transport and so on. There is some confusion here, as data is being touted as the next big thing but data is dead in the water if not interpreted and then used to change something. If data is the new oil, algorithmic power is the new turbo-charged engine.
What role for algorithms in learning?
So far the most promising use of educational data is through algorithms. Yet algorithms are faceless and anonymous, hidden from view. As a user, you will usually be unaware of the role they play in your life. Like icebergs, their power lies hidden beneath the surface, with only the user interface visible above the waterline. So let’s make them a little more visible.
It’s like using a satnav in your car. It knows where you’ve come from, where you’re going and how to get you back when you go off course. It may even know when you need a rest and whether you’re comfortable with driving on the motorway or would be best routed through other roads. Satnavs are massively algorithmic, and personalised, as is adaptive, algorithmic teaching. In a learning journey, something similar can be implemented, where ensembles of algorithms can analyse data about the student and content, leading to real-time improvements in both. Note that they can do this in real time and also learn as they go, matching the most appropriate content to the student at any given time. This can lead to quicker course completion, lower drop-out rates, higher attainment and lower costs.
How do they work?
(You can skip this if you have no interest in the background maths.)
Bayes theorem
In 1763 a posthumously published essay by the Reverend Thomas Bayes, presented a single theorem that updated a probability when presented with new evidence. This gives you the ability to continue to update probabilities in the light of new evidence, new predictors and so on, all into a single new probability.
In learning, this allows an algorithmic system to continue to update predictions and recommendations for students and content configuration over time. Interestingly, this often reduces the probability as intuition, through cognitive bias, often exaggerates probabilities, through inadequate analysis.
In addition to the use of Bayesian data analysis is the use of a Bayesian network. This is a model that has ‘known’ and ‘unknown’ probabilities from, say student data, behaviour and performance. The network has nodes with variables (known and unknown) and algorithms can both make decisions and even learn within these networks. It’s basically the application of Bayes theorem to solve complex problems, such as the optimal path for personalised learning. The network will therefore recommend the optimal content going forward.
Enter another important name Andrey Markov, a Russian mathematician who introduced the Markov network. Whereas a Bayesian network is directed and not cyclic, a Markov network is undirected and can be cyclic. Markov models can be used to determine what the learner gets as they attempt a course based on previous behaviours. You may be unaware, for example, that these techniques are already used to present you with a different web page from others from major providers.
Quite separately, Corbett introduced a Bayesian knowledge-tracing algorithm, directly into the learning field, which is more directly associated with data mining, from, for example, learning management systems, which produce large amounts of data about learner behaviour. This can be used to come to a conclusion and make a decision about what is needed next. Note that all of these approaches (and there are many more) are very different from rule-based adaptive systems. The difference between these systems is explained well in this paper by Jim Thompson.
We should note that this field has 250 years of mathematical thinking behind it and has an enormous amount of mathematical complexity. Nevertheless, having born fruit in other online contexts there is every reason to think it will bear fruit in learning. Learning algorithms can, through algorithms. embody evidence-based learning theory, to increase the productivity of the teaching. But what really drives algorithmic, adaptive learning are the advantages they afford to the learner:
1. Gender, race, colour, accent, social background
Algorithms are blind to the sort of social biases (gender, race, colour, age, ethnicity, religion, accent, social background) we commonly see, not only in society through sexism, racism and snobbery but also in teaching where social biases are not uncommon. In education, it is useful to distinguish between subtle and blatant biases, in that the teacher may be perceived to be unbiased and not be aware of their own biases. We know, for example, that gender bias has a strong effect on subject choice and that both gender and race affect teacher feedback. Algorithms can be free of such social biases.
2. Free from cognitive biases
Cognitive biases around ability versus effort, made clear by the likes of Carol Dweck on fixed versus growth mindsets, clearly affect teacher and learner behaviour leading to self-fulfilling predictions on student attainment. Considerable bias in marking and grades has also been evidenced. There may also be ingrained theories and practices that are out of date and now disproven, such as learning styles, that heavily influence teaching. Algorithms, build on sound theory and practice, can, over time, based on actual evidence, try to eliminate such biases.
3. Never get tired, ill, irritable or disillusioned
To teach is human and teacher performance is therefore variable. That is not a criticism of teachers but an observation about human nature and behaviour. Algorithm behaviour is only variable in the sense that it uses variables. Algorithms are at the top of their game (albeit limited) 24/7/365. Of course, one could argue that the affective, emotional side of learning is not always provided by algorithmic learning. That is true but good design can ensure that it is a feature of delivery. Even here, algorithmic techniques around gesture recognition, attention and emotion are being researched and built.
4. Algorithms can do things that brains cannot
Seems like a bold claim, but the number of variables, and sheer formulaic power of an ensemble of algorithms, in many areas, is well beyond the capability of the brain. In addition, the data feeds and data mining opportunities, as well as consistent and correct delivery of content may also be beyond the capability of many teachers. The problem is that most teaching is not one-to-one and therefore those tacit skills are difficult to apply to classes of learners, the norm in educational and training institutions. For the moment there are many tacit skills in teachers that algorithms have not captured. That has to be recognised but that is not a reason for stopping, only a reason for driving forward.
5. Personalises the speed of learning
A group of learners can be represented by a distribution curve. Yet suppose we use a system that is sensitive not just to the bulk of learners but also the leading and trailing tail? Algorithms treat the learners as an individual and personalise the learning journey for that learner. You are, essentially streaming into streams of one. The consequence is the right route for each individual that leads to learning at the speed of ability at any given time. The promise is that learners get through courses quicker.
6. Prevents catastrophic failure & drop-out
Slower learners do not get left behind or suffer catastrophic failure, often in a final summative exam when it is too late, because the system brings them along at a speed that suits them. This can lower drop-out, something that has critical personal, social and financial consequences.
7. Personal reporting
Such systems can produce reports that really do match personal attainment, through personal feedback for the learner than informs their motivation and progress through a course. Rather than standard feedback and remedial loops, the learner can feel as though they really are being tutored, as the feedback is detailed and the learning journey finessed to their personal needs.
8. They learn
Teachers learn, though many would question the efficacy of INSET days or current models of rushed or absent CPD. Algorithmic systems also learn. It is a mathematical feature of machine learning that the system gets better the more students that take the course. We must be careful about exaggerated claims in this area but it is an area of intense research and development.
9. Course improvement
Courses are often repeated, without a great deal of reflection on their weaknesses, even inaccuracies. Many studies of textbooks have shown that they are strewn with mistakes. Adaptive, algorithmic systems can be designed to automatically identify erroneous questions ,weak spots, good resources even optimal paths through a network of learning possibilities. One further possibility is in courses that are semi-porous, where learners use an external resource, say a Wikipedia page or video, and find it useful, thereby raising its ranking in the network of available options for future learners.
10. Massively scalable
Humans are not scalable but algorithms are massively scalable. We have already seen how Google, Facebook, Amazon, Netflix, retailers and many other services use algorithmic power to help you make better decisions and these operate at the level of billions of users. In other words there is no real limit to their scalability. If we can apply that personalisation of learning on a massive scale, education could break free of its heavy cost burden.
The algorithmic, adaptive approach to learning promises to provide things that live teachers cannot and could never deliver. All of the above is being realised through organisations like CogBooks, who have built adaptive, algorithmic systems. This is important, as we cannot get fixated by the oft repeated mantra that face-to-face teaching is always a necessary condition for learning - it is not. Neither should we simply stop at the point of seeing technology as merely something to be used by a teacher in a classroom. It can, but it can be more than this. This approach to technology-based learning could be a massive breakthrough in terms of learning outcomes for millions of learners. It already operates in the learning sphere, through search, perhaps the most profound pedagogic change we have seen in the last century. For me, it is only a matter of when it will be used in more formal learning environments.

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Friday, September 26, 2014

MOOC physics students outperform campus students

Prof Dave Pritchard
This guy teaches both a campus and MOOC course on physics. He is no amateur, a world-class researcher in atomic physics, winner of four major research prizes and three students who won Nobel prizes. When he compared their learning gains, he was astonished at the results. “I had hoped that because they (campus cohort) went to our classes, they would learn a lot more”.
Given the many positive announcements from vendors and negative reactions from academics about MOOCs, there is a remarkable lack of research on learning outcomes. Why this lack of interest? Well one feature of academic research is that it rarely looks at itself, for fear of what it will find. The lecture is the stand-out example. Despite many decades of research showing that lectures, and hterefore the professional job title ‘lecturers’, make little pedagogic sense, most academics will dive into the trenches of irrational indignity in their defence. But reson will prevail and on MOOCs we are now seeing some wonderful research by the likes of MIT and the University of Edinburgh.
Standout study
One standout study by MIT compares campus with MOOC students on the same course. It tackles the key issue; do students learn using MOOCs and how does this compare to the much more expensive campus course? If it is true that students learn more or as much on a MOOC, then the cost differential is such that it makes absolute sense to use MOOCs for these courses. This is a solution to the ballooning costs in HE. Even of the students learn less, there is still a strong argument for using MOOCs in terms of costs. Far too many researchers ignore this key issue – the massive reduction in cost.
Enter this study on Physics. It is a introductory, review course on mechanics with an emphasis on strategic problem solving. It starts with straight line motion through to momentum, mechanical energy, rotational motion, angular momentum and harmonic oscillation. The interesting addition is the content on problem solving that requires several different concepts and approaches leading to a single solution.
1000 students who completed the MOOC (Certificates of Completion) were compared with their similarly scored counterparts on the campus course. The relative progress across all groups was roughly equal across all groups. Identical pre- and post-tests were given to assess learning gains.
What surprised the researchers was that the online students did as well as the campus students, and this is the interesting part – regardless of previous academic experience, whether they had a PhD, Masters, Bachelors or high-school diploma. In fact, the MOOC generated “slightly more conceptual learning than a conventionally taught on-campus course”. But there was a further piece of analysis that was even more surprising. When comparing MOOC students with MIT campus students , even though the campus students had received a lot of extra input and instruction, the relative results were the same with “no evidence of positive, weekly relative improvement of our on-campus students compared with our online students”.

This is the only study of its type that I know but would be pleased to hear about more if they focus on learning outcomes. At last we are beginning to see some sensible research that cuts through both the hype and defensive posturing. Good, level-headed academics and institutions are doing what should have been done years ago – researched the learning and cost outcomes. The researchers are now going on to look at what caused the learning. This is good work and long overdue.

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Wednesday, September 10, 2014

Spaced practice – 8 practical ways to make it happen

Spaced practice, despite being well known since Ebbinghaus first suggested it as a solution to his forgetting curve, in 1885, remains a rarely practiced technique in learning. The reasons are obvious enough. Most education and training delivers isolated doses of learning, lectures, presentations, classroom courses and the learners walk out of the door at the end, job supposedly done. Teachers had no real way of getting to them after the event had finished.
Spaced practice needs to be habitual. John Locke and William James both emphasised the key role that ‘habit’ plays in learning, lessons we’ve ignored.
Good learners, in my experience have developed good learning habits. They always have something to read in their pocket or bag. They tend to be obsessive note takers, often with a long series of filled notebooks. They habitually elaborate what they hear and actively try to remember. They replay and recall in their own minds, through dialogue and re-reading their notes. They also tend to kick start new learning habits, such as blogging, using Evernote and so on.
So how can we make spaced practice habitual?
1. Start and end
As a teacher, if you deliver a series of lectures, classes, modules, whatever, the simple practice of summarising what was taught in the last lecture, period, class or event and doing the same at the end of the lecture/class/event, gives two reinforcement events for the price of one. There’s a double dividend in that you take advantage of primacy and recency (also discovered by Ebbinhaus), the fact that learners tend to remember the first and last things more than what comes in between.
2. Notes
As a learner, get into the habit, not only of taking notes, but rereading and rewriting those notes. Blogging is, in this sense, a massively effective way to reinforce learning. It’s one of those things that, when it becomes habitual, is massively effective as an aide memoire. Wittrick and Alesandrini (1990) found that written summaries increased learning by 30% through summaries and 22% using written analogies, compared to the control group. If you take notes AND review them, you do better on assessments (Kiewra 1989, 1991). Interestingly, Peper and Mayer (1978) found that note taking increased skills transfer and problem solving in computer programming and science(1986). Shrager and Mayer (1989) found similar effects in college students, learning about cameras. It would seem that note taking allows learners to relate knowledge to experience.
3. Sleep
One of the most effective methods of habitually delivering spaced practice is to encourage learners to get into the habit of a little practice and recall just before they go to sleep. This takes discipline but studies show that it is very effective as the brain appears to consolidate memory during sleep.
4. Exercise
If you exercise regularly, that is the chance to recall and reinforce whatever you want to retain. A podcast through your headphones? Simply record your own lists, notes, reinforcement events and replay on demand. Get into the habit and you’ll get both physical and psychological gains.
5. Places
There are other things in life you do regularly, like eat, go to the toilet, leave the house and so on. One old trick I’ve heard used in language learning, is to put up vocabulary lists, grammatical rules etc, on the back door of the toilet. A couple of minutes every day, while you go through your ablutions may just prevent the natural excretion of that knowledge you worked so hard to remember.
6. Email
If all of your learners use email then this is an easy and efficient way to deliver spaced practice events.  Group emails, set up and timed for release, can get whatever reinforcement event you wish to design straight to your audience. A simple text email, infographic, question, video, even piece of e-learning; anything that makes them rethink, will help fix the learning in long-term memory.
7. Facebook
Given the fact that 1.5 billion people are on Facebook there’s a good chance that your learners are easier to reach on Facebook than they are in your institution, library or any other physical space. The notifications system on Facebook is superbly efficient and that little red circle with a number in it is a strong stimulus for attention. Simply message your students with a series of cues from the lecture or course.
8. Mobile
Systems, like ENCORE, deliver reinforcement events, spaced between any two times to your learners’ mobiles. You can choose what to deliver when using a variety of media. This system gets to that powerful, personal, portable device in the pocket of every learner.
Spaced practice is arguably the most powerful, yet most overlooked benefit in learning. Implemented properly and it is possible to have huge gains in productivity, namely the retention and recall of whatever has been learnt. I’d go further and say that if you don’t have a spaced practice strategy, you don’t have a properly designed course.

So let’s have that list again….

Start and end


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Monday, September 08, 2014

Jeff Haywood: stunning analysis of the future of online in HE (MOOCs etc)

When it comes to online learning, the University of Edinburgh, has become the most active and interesting HE institution in the UK. This is something of a surprise, after its disastrous performance in student surveys on teaching, perhaps even, a response to this problem. They do, however, have two great advantages, Tim O’Shea and Jeff Haywood, who have led the charge towards the sort of experimentation and strategic, economic thinking that put the rest to shame.
Haywood keeps an eye on the data emerging from online activity in HE and points towards a steady rise in its use and acceptance by students and faculty (one third have taught an online class, 71% used OER). He points towards an increasing portion of younger people taking MOOCs with a rise towards 1 in 5 students taking online courses. There is a growing recognition that online may even be as good, if not, at times, better that the traditional campus course.
Students not staff
In his recent ALT talk, Haywood gave a masterful talk on how to think strategically about online learning in HE. I have written about their bold move with six Coursera MOOCs, along with their excellent publication of the resulting data. This is only one strand of a strategy that looks towards 2025. It is a strategy that takes the business case, the economics, seriously.
Where he is way ahead of most commentators in he is that 90% of the tech is used by students not staff. The focus on staff is, to his mind a bit myopic and a bit of an EU obsession. This movement is “student oriented rather than institution oriented”.
Love or hate them MOOCs have forced open a debate at policy level on digital education”.  Spot on. David Willetts, with Martin Bean, took the bull by the horns and kick started Futurelearn. There is no doubt that this action has stimulated both debate and action in HE. Unfortunately, he has been sacked, and there is no sign that the Shadow Minister for Education Tristram Hunt, is anywhere near as interested.
Unlike many in academe, Haywood speaks with authority based on real and substantial experience, with substantial data, when he makes the important statement that MOOCs “touch learners much more than you might think”. As an aside he also mentions that it has forced Universities to up their game on marketing. We have learnt he says, that “courses can be run at surprisingly large scale” and that “charismatic teachers can reach learners”. Accelerated innovation has also been seen with “a range of technological innovations doing things at scale – peer assessment, comparative judgement online etc.”.
One word matters above all in this analysis “scale”. Haywood is right to place it at the centre of his analysis. Sure we know that faculty have low digital skills and that there are low rewards for teaching but we must get to scale and move beyond the “bijou and niche
Where Edinburgh and Haywood have the intellectual upper hand is in their honest appraisal of the economic implications of online learning. In my view the majority of academics are stuck in an analysis that focuses only on quality, ignoring the real issue of cost. Seb Schmoller and others have been arguing for some time, with estimated figures for cost per student, that online, while it may not match the quality, is so much cheaper, that even a weak result makes it worthwhile. I’d argue that the quality issue is fast disappearing, with degree delivery by the likes of IDI. His vision looks to on demand, self-paced, location flexible, relevant to your future, global & local, personalised, affordable, value-added learning; “Without technology this is undoable”.
Walks the walk
At national and international level, policy level discussion is needed with a road map that has clear steps over the next ten years to 2025. This is also true within your institution. If not planned it will not happen. Without investment it will not happen. Without agility it will not happen. This is why “MOOCs and the children of MOOCs are so important”. But that’s just a fraction of the story.  Edinburgh’s MOOCs have racked up huge numbers, with 800k learners on 15 MOOCs and another 15 in the pipeline. Edinburgh has 30k students on campus but also with 50 odd fully online Masters Degrees. (2500 students). It’s a mixed strategy.
Vision by 2020
By 2020 they want 40k students with “all students taking one full online course” and all faculty and teaching staff will have some experience of teaching online. They have the ambition to try to reach 10s of millions of learners through increased online Masters degrees, OER and MOOCs. The means to the end is a series of real funded experiments and pilots, which are all potentially scalable.
Haywood is optimistic and thinks that he is swimming with the tide. The technology has matured, interest risen among learners and policy discussions are far more outcome oriented. One wit in the audience thought that CAVE dwellers (Colleagues Against Virtually Everything), were his biggest problem. Haywood thought that MOOCs had been useful in that those Universities that had taken this leap have found that MOOCs encourage faculty to come forward, as they know they will get support. He added that employers are clearly interested in MOOCs. In Scotland SMEs are interested. For Haywood this is about “opening up the boundaries of space and time – as campus education is limited on both”. He sees nothing wrong with pro bono working education with the secondary aim of recruiting students and charged services coming through.
Diana Laurrilard made a point she often makes and it is pertinent, that Universities have never really understood the cost of teaching. This is true, they don’t even know what is being delivered and to whom. Unfortunately, she has been on the warpath against MOOCs, but only on a straw man basis. She doesn’t believe that MOOCs will entirely replace current HE model. That’s fine, neither do most MOOC providers, including Haywood. Haywood’s response to her question on quality was entirely right. Sure, the tough part is supporting and nurturing students through their personal intellectual development but the answer may be in the middle way. We know that lectures can easily scale so what about the varying degrees of personalised support (something grossly exaggerated in HE). He thinks that technology is already providing solutions, allowing portions of courses to be run on their own, without tutor intervention. Haywood is keen to use intelligent technology at the kind of numbers we run on our MOOCs. He, unlike Laurrilardian sceptics, know a good deal more about the technology, such as adaptive learning, and rightly look for economies of scale, before making rash announcements.
The sort of leaps he sees in technology, that allow you to step back and let parts of course run on scale are being looked at, especially in their expanding masters programme. First, there’s online assessment, the Achilles’s heel in HE. The lecture model completely negates sensible formative assessment and the long-form essay, with slow, and often amateurish feedback, seems incredibly dated.
Adaptive learning
He is spot on in looking at learning analytics, especially adaptive learning, for scaling right across the institutions. This is the one technology that already offers hope in tackling the hard to deliver ‘tutor’ functions, pushing courses towards competence-based learning, where learners get personalised learning delivered at their own pace. This is already happening in the US, with considerable investment by the Gates Foundation and a rack of other institutions. Haywood and Edinburgh are the first UK institution to pick up on this and take it seriously. He is being true to his word in retaining some small group pedagogy where you need it but always looking for economies of scale.
Information technology has been extremely consequential in HE over the last 25 years but principally in ‘output enhancing’ ways that do not show up in the usual measures of either productivity or cost per student. Stanford 2012 Tanner Lecture
This is a great quote and recognises that technology has enhanced what we do but the economic are rarely understood. Haywood wants to find ways to use technologies to increase the throughput of students, move through curriculum at their own speed, and automate some parts of the curriculum where you can scale. We need to address the question of increasing productivity without decline in quality. If not we’re just polishing the current system without addressing access on scale.

At last we have a University and academic who is sophisticated enough to think big, be thoroughly strategic, agile, consider the economies of scale with an impressive focus on productivity and costs. One of the great failures in HE planning is a serious attempt to consider actual costs, to be specific, as Lewin and Blefield recommend on detailed cost effectiveness analysis. He also has a vision of the future University, not just being more “open on the boundaries“ but more productive and efficient.

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