How to get 10 million learners by 2020?

15 Sep

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In this stunning talk Jeff Haywood outlines the University of Edinburgh’s current and future plans to reach 10 million learners by 2020, through:
-Adaptive learning
-Online Masters
-Blend of online/campus

He explains that they have reached 800,000 learners already through MOOCs. “Love or hate them MOOCs have forced open a debate at policy level on digital education” and as he explains, they “touch learners much more than you might think”. The main point is that they are convinced that “courses can be run at surprisingly large scale” and that MOOCs have “accelerated a range of technological innovations”. But MOOCs are part of a wider strategy where, to meet and grow demand, they have to take online seriously, “Without technology this is undoable”. Watch the whole talk here or read a summary and analysis by CogBooks Director Donald Clark here.

Free short open online maths course going live

3 Sep

Citizen Math Logo UFI
Free short open online maths course going live

A new online maths course has the potential to give people throughout the UK much-needed skills to solve day-to-day problems in the home, at work and in school or college.

One in three adults in the UK, miss out on the power of mathematics to solve problems in everyday life and work, even though they understand the basics of numeracy. But that could change with the introduction of Citizen Maths – using a model of online learning already employed with success to help hundreds of thousands of people across the world to learn computer science.

For huge numbers of adults, a face-to-face course is not an option: the cost of providing tutored courses on the scale needed is prohibitive; even if the money was available, there isn’t the capacity in the system to provide such courses; and for many adults their work and social lives are too varied to make attending a regular face-to-face course feasible.

Citizen Maths is open for registrations now at and will launch officially on 12 September as a free, open online course, funded by the Ufi Charitable Trust, and developed by Calderdale College, with CogBooks, maths education researchers from the University of London Institute of Education, and the awarding body OCR. It has gained advice along the way from Google, on whose platform Citizen Maths relies.

The course is primarily aimed at adults who want to improve their grasp of maths, said Seb Schmoller, who directs the project for Calderdale College. “The course has been made with independent self-motivated learners in mind: that is, people who are not enrolled on a formal course and who have decided to improve their maths.”

However, the development team also identified a far wider group who would also benefit, including teachers, tutors or trainers who would find that Citizen Maths is a useful adjunct to their own work. They should give their students active encouragement to use Citizen Maths too, he said.

“It may also be that parents of school-aged children decide to use Citizen Maths to develop their own maths so that they can help their children. And we know that people involved in mathematics education or in online learning are already signing up for Citizen Maths simply to ‘check it out’. That is fine by us.”

Part of the vision of such courses was that they should be free and open to all. Such an approach to learning this kind of maths had never been tried before, so it made sense to use a model that has already been tried and tested in the teaching of computer science, said Schmoller.

“We’ve adapted that model to help people learn maths at an intermediate level. The course videos give you a feeling of having a one-to-one lesson with a helpful teacher. Online apps allow you to try out new ideas through hands-on activities. At any point you can discuss what you are learning, and share problems and solutions with other like-minded learners.”

Citizen Maths is open for registrations now at It is based around powerful ideas in mathematics, the first of which is the idea of “proportion” – the subject matter of the course that will go live on 12 September.

Professor Dave Pratt, from the Institute of Education, explained: “Proportion matters because it sits behind so many aspects of every-day maths, for example when you are sharing out costs, or altering a mixture, comparing amounts, or scaling something up or down. It is fundamental to being able to understand and solve a wide range of problems.”

The course on offer from September should take between 5-10 hours to complete, spread over a few days, or a few weeks. Further course sections, covering other powerful ideas in mathematics, are due to be produced by the team in 2015, once the impact of the current course has been assessed.

Commenting on the launch, Rebecca Garrod-Waters, CEO of funder the Ufi Charitable Trust said: “The Trust’s major aim is to scale up the use of technology-based learning. Citizen Maths has tremendous potential to help us meet this aim, and to contribute to the broader challenge of helping self-motivated people improve their grasp of maths.” Mike Ellicock, CEO of the independent charity National Numeracy, said: “I’m excited about the launch of Citizen Maths; it fits really well with the National Numeracy Challenge and I hope that together we can start to help the 78% of adults who are currently working below Level 2.”

Meanwhile, Chris Jones, Principal and Chief Executive of Calderdale College, commented: “We know that there are many in the UK who need and want to improve their maths skills, and for whom enrolling on a face-to-face course is not practicable. That’s who Citizen Maths is for.”

New partnership between UK exam board OCR and adaptive learning platform Cogbooks to personalise first school Computing MOOC

29 Jul

Exam board OCR and adaptive learning experts Cogbooks confirmed today they are working together to add personalised adaptive learning to the pioneering Cambridge GCSE Computing MOOC. The partnership brings free of charge to UK secondary schools an exciting learning technology that is making headlines in US Higher Education.

Since its launch in 2013, over 100,000 users across the world have accessed the Cambridge GCSE Computing MOOC, developed by OCR in collaboration with Cambridge University Press and Raspberry Pi, and based on OCR’s GCSE Computing curriculum.

From September 2014, the addition of CogBooks adaptive technology will allow every student to progress through the MOOC in a way which suits their individual needs. CogBooks has pioneered the use of adaptive learning since 2003, and it was most highly rated in a recent report on adaptive learning commissioned by the Bill and Melinda Gates Foundation.

CogBooks adaptive technology constantly analyses a student’s activities and then uses algorithms to recommend the most helpful next step for them. The ‘personalised’ Computing MOOC supports teachers by providing them with in depth analysis of each student’s progress and helps learners move at their own pace.

Liam Sammon, OCR’s Director of Education and Commercial Services, said: “This partnership will keep students and teachers at the heart of technology. We firmly believe that technology must be harnessed in the interests of education, and that by making the technology free, the reach is potentially massive. CogBooks is a high quality, innovative company that shares our vision and we are delighted to work with them.”

Jim Thompson, Chief Executive of Cogbooks and a Former Research Fellow in Physics at Cambridge University said: “OCR combines its long history in education with an incredibly forward-looking vision for supporting teachers and students in the 21st Century. CogBooks is excited to be part of that vision and we share OCR’s commitment to making the latest advances in learning technology available to a mass audience. CogBooks was founded with the aim of bringing scientific methods to on-line learning. We do this by creating learning experiences and technology based on sound research and providing teachers with the data to inform how they support their students.”

OCR was recently recognised in a DfE report (‘MOOCs: opportunities for their use in compulsory-age education’) as one of the few providers currently offering a MOOC for secondary age learners.


For all press enquiries, please contact Rosie Applin: / 01223 376491 or Sophie White: / 01223 552767.

  1. In September 2014, a full curriculum of GCSE Computer Science content will be available in the enhanced MOOC using the CogBooks intelligent personalisation technology. Students can work through course materials at their own pace and receive automated support when they need extra help. At the same time, teachers will benefit from dashboards and in-depth reports to track the exact progress and capabilities of each student and target the most appropriate support for their class. The system is also suitable for working with ‘flipped classroom’ approaches to teaching and provides social learning support through integrated online forums and other features.

About OCR
OCR (Oxford Cambridge and RSA) is a leading UK awarding body, providing a wide range of qualifications to meet the needs of learners of all ages and abilities. OCR qualifications include AS/A Levels, GCSEs, Cambridge Nationals, Cambridge Technicals, Entry Level qualifications, and vocational qualifications in areas such as IT, business, languages, teaching/training, administration and secretarial skills.

Each year more than three million students gain OCR qualifications, which are offered by 13,000 centres including schools, sixth form colleges, FE colleges, training providers, voluntary organisations, local authorities, and businesses ranging from SMEs to multi-national organisations.

OCR is part of the Cambridge Assessment Group.

Cambridge GCSE Computing Online (The Computing MOOC):

About Cogbooks
The CogBooks adaptive learning platform intelligently personalises learning for each student in real time in order to improve learning outcomes and retention. CogBooks is used widely by publishers and in post-secondary education and corporate training. We are engaged in a range of high profile on-line education projects in work with the Bill and Melinda Gates Foundation, Ufi Charitable Trust, OCR Exam Board and others. Our mature cloud-based technology can be used as a massive scale stand-alone on-line learning solution or integrated with our customers’ existing systems such as MOOCs or Learning Management Systems.

We are committed to advancing education through the application of research-led methods. CogBooks learning technology is based on a systematic, data-driven, approach that makes us an ideal partner for forward thinking educators and publishers.

To learn more about CogBooks learning technologies, please visit or email

Preliminary findings at Shoreline Community College presented by Northeastern University

19 May

Adaptive learning through algorithmic, personalized software is relatively new and it is fair to demand that research and evidence be produced that shows its efficacy in terms of student outcomes.

Northeastern University research
Northeastern University, in Boston, have just published their preliminary findings on the Winter 2014 cohort at Shoreline Community College, who took 1 or 2 courses (Microeconomics and English) online, via an implementation of the CogBooks platform, integrated with the Canvas LMS.

As students progress through the readings and assignments, the adaptive learning software provides supplemental tutorials as needed to individual students, records student time on activities and modules, gathers student assessment data to inform the learning path for each student, and provides reports to instructors. Instructors map course content in the adaptive learning platform using CogBooks to answer questions students pose in the system, monitor student performance, and guide class discussion and emphasis.



Qualitative Findings – English 100
“At this preliminary stage students are illustrating noted improvement over previous sections of English 100 in two areas. First, the quality of their essay revisions shows solid understanding of revision strategies and the ability to apply those strategies to improve their writing. Second, their writing also shows increased awareness of sentence-level issues such as word choice, phrasing, grammar, and mechanics.”

Student Feedback
Students’ positive perception of the value of the adaptive learning system and its effectiveness in their learning increased throughout the term. On completion of the final outcome, students were overwhelmingly positive about the value of the adaptive learning integration and its impact on their understanding of the key reading and writing outcomes in the course. One student commented “I have taken lots of online courses and this is the first one that feels like an online course ought to be”.

Intro to Microeconomics – January 2014
Comparing this adaptive Spring 2014 version with the Fall 2013 non-adaptive class; both courses had 30 students. I have seen that this quarter (in the adaptive class), the students are progressing to the next level of content with greater ease…without the same stumbling blocks as in previous quarters. Microeconomics is tough for many students and    typically gets really tough just past the mid-point when students have to apply their learning. Analyzing market structures, for example, relies on understanding of concepts from week two to week five. These results and conclusions are tentative – we have tests    and test results coming up but students across the board seem to have mastered these concepts better than in previous quarters.

Gates Commissioned Report
In ‘LEARNING TO ADAPT: Understanding the adaptive learning supplier landscape’ the Gates Foundation commissioned EGA to provide some real analysis and insights into the different solutions on offer in the market. Each of the eight suppliers deliver different types of adaptive solutions, so the authors try to apply credible judgment criteria and look at the landscape through three lenses; Approach, Taxonomy, and Maturity.

They start with a general definition of adaptive learning around a personalized learning experience adjusting to the learners needs and wisely point out that this is a radical departure from traditional online learning that needs a “fundamental redesign of the course experience”.

Six pedagogic attributes
This is a very thorough analysis of business and instructional models of eight suppliers and aims to objectify the analysis by applying six pedagogic attributes to all eight of the adaptive learning solutions:

1. Learner Profile is a structured repository of information about the learner used to inform and personalize the learning experience

2. Unit of Adaptivity refers to the structure of the instructional content and the scale at which that content is modified for specific learner needs

3. Instruction Coverage refers to the pedagogical flexibility of a product to deliver an adaptive learning experience and the scope/scale of that experience within the context of a course

4. Assessment is the frequency, format, and conditions under which learners are evaluated

5. Content Model describes the accessibility of the product’s authoring environment to instructors or other users and their ability to add and/or manipulate instructional content in the system

6. Bloom’s Coverage highlights to what extent a product can support the learning objectives within the Cognitive Domain of Bloom’s Taxonomy

We are pleased to see that we, at CogBooks, are included as one of the eight suppliers, and do rather well on the six attributes. We are seen as a cloud-based learning platform that optimizes sequence and speed using “prerequisite sequencing, retention, cognitive load and attention, and level of engagement, among others”. Uniquely we use prerequisites along with repetition to achieve retention. At first the learning activities are constructed manually with a default path and if the student has difficulty a different learning journey is presented. The system is student profile and algorithm-driven to deliver dynamic learning paths. In fact, the learner’s path is remapped after every screen, responding to the learner’s current profile. The system supports content creation within the platform as well as that developed in third-party systems, and open-source resources from the web.

Free copy of the full report ‘LEARNING TO ADAPT: Understanding the adaptive learning supplier landscape

Cogbooks platform
The Cogbooks platform uses algorithms, based on scientific learning theory, to personalise learning by constantly working out, in real-time, what the student should do next. Ever screen and learning experience that is presented has been judged to be personally appropriate at that exact moment.

The content is not stored as a series of flat and linear screens but as a network of learning. Students vector through this network depending on a whole raft of factors, each student taking different routes. It is like having your own satnav as you progress, constantly working out where you’ve come from, where you need to get to and getting you back on track if you go off track.

Cogbooks interface


These results are tentative, but positive, and show improvements not only in attainment but also in halting drop-out (withdrawals). If these results continue to be corroborated, we will have evidence that adaptive learning, using algorithmic, personalised software may result in huge rises in student attainment. This paves the way for using algorithms and student data to hugely accelerate learning.

Algorithms are more powerful than news editors

24 Mar

EliPariserAt SXSW a session that caught a lot of attention was with the CEO of Upworthy, Eli Pariser, on the future of Journalism. He said some astounding things including the fact that, on news “algorithms are now more powerful than editors”. What he meant was not only the fact that algorithms now determine what news you are likely to receive but also, incredibly, how that news is written.

Algorithmic news delivery
With so many news sources online, from Facebook, Twitter, Google and lots of other news specific sites, like Upworthy, what news you see is now likely to have been preselected for you by algorithms that get to know what you want and aggregate data to make decisions about what people like you want. An ensemble of algorithms, invisible but potent, determine what you’re fed. This may sound frightening but some argue that this is far better than the self-selected editorial class responsible for what you see on TV news and newspapers. Any editorial process is subject to bias, inherent in the editorial group. Algorithms, arguably, can be designed to be more objective. Sure, it shifts the bias from editors to algorithm designers but at least there’s continuous improvement.

News junkies have never had it better
With 24 hour news channels and continuous feeds on the web “news junkies have never had it better”. If anything it’s a matter of realtime, editorial aggregation from multiple online sources.
Upworthly LogoUpworthy has 50-60 million users a months and is now more powerful than a lot of the most powerful editors in traditional media. Their key metrics have moved from unique visitors and page views to what they call “attention minutes” based on importance, satisfaction and quality. But the guerilla on the online news front is still Facebook. They can choose to tweak their algorithms to attack any competitor, as they have such a massive audience. Whatever the outcome, there is no doubt that news is now data and data can be mined, repurposed, repackaged and delivered on a massive scale.

Algorithmic news production
Stats Monkey ImageMore shocking is the fact that news is already being written by algorithmic software. Stats Monkey took baseball data and statistical models, one of the most data-driven sports on the planet, mined that data for key plays and players, then hauled in weather reports and strung it together into a factual report of the game. They added narrative arcs and styles, so that these stories had an angle – convincing win leading from the start, come from behind to win, to and fro to narrow win and so on. They could be written as straight reports, more humerous, from one side or the other. Quotes can be pulled in to make it seem as if it is written by a real journalist. You can even choose different narrative styles aimed at different audiences. The project came out of a joint effort by Medill School of Journalism and the McCormick School of Engineering at Northwestern University through the Center for Innovation in Technology, Media and Journalism.

Narrative Science
Narrative Science LogoOut of this came Narrative Science, with its quill product. They have moved beyond sport into financial reporting, as it is quite lucrative. The have refined and finessed the process. It’s software mines the data, gets the facts, determines the angles, builds the structure and polishes the narrative. It’s often hard to tell whether the piece was written by a person or machine and the pieces can be syndicated out.

News and learning
Interesting stuff and it makes one wonder whether the same process can’t be used for knowledge. Currently, as a teacher or learner, you have to curate your own content, that comes in lumps of pre-set media – Wikipedia articles, papers, YouTube videos, images, graphics, diagrams, photographs. Imagine a software programme that searches, finds, filters and reconstructs knowledge , personalised for your own needs. We have, at present, algorithmic software that delivers software based on ensembles of algorithms that understand who you are and what you need next on your learning journey. The next step is to automate the build of the content itself.

Things are moving fast. In news we’ve gone from a fixed time, once a day newspaper or TV news programme, delivered in real time, to rolling news on TV, to web delivered news and now algorithm determined delivery of that news, even algorithmic software that produces news stories. As this gets better, it may well be the case that the news delivered by software has such good market intelligence from its instantaneous data mining that it is, by definition, better than a human writer. We may even see smart narrative arcs and styles that are beyond that of the standard hack. As Eli Pariser says, he fully expects “a piece of software to win the ‘Pulitzer Prize’”.

CogBooks White Paper – Big Data

20 Dec

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Big Data, at all sorts of levels 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.

To download this White Paper, click here.

Adaptive learning – Eight Key Questions

13 Dec

Jim Thomson gave a talk on adaptive learning at Online Educa in Berlin this month. Titled ‘Adaptive Learning: Eight Key Questions’

Why is adaptive learning so important? he asked and argued that students need improved learning effectiveness through personalized support and reduced learning times through personalised pathways. He also argued that instructors need enhanced data and tools for helping students and increased automation of teaching tasks. In addition, University / Organizational Administrators need higher completion rates, reduced delivery costs and increased use of data driven methods

The eight key questions he asked, to help you to define the type of adaptive learning tool more specifically, were:

1. When are the data gathered and recommendations made ?

2. What data are used to drive adaptation?

3. What is adapted?

4. What methods drive adaptation?

5. What end use applications?

6. How extensive and scalable?

7. How open is the content model?

8. How accessible are the data and architecture?

Donald Clark – Why Adaptive Learning?

11 Dec

Donald Clark explains why algorithms and data can be used to great effect in delivering content in a personalised manner to learners, as well as providing optimal paths through learning content using powerful back end software.

Source: UFI charitable trust

Jim Thompson – Personalised Adaptive Learning at Turing Festival

3 Dec

Turing 2013 took place at the height of the Edinburgh Festival, the world largest arts and cultural event. The Festival brought together digital technology and the web within the world’s largest arts and creative gathering in a celebration of digital culture and creativity. Jim Thompson, CEO of CogBooks, was invited to speak at this prestigious event, presenting Personalized Adaptive Learning. It was chaired by another CogBooks Director, Donald Clark. 

Jim explains how adaptive learning is a game-changer in education, as smart software guides the learner through a network of content, constantly checking to see that each learner gets the content most suited to them at every moment of the learning experience. 

Can maths find you love? eHarmony’s love algorithm

18 Nov

Eharmony algorithm Could maths find you love? The dating site eHarmony, who claim to have been responsible for a staggering half a million marriages, use algorithms for just that purpose. They claim they are responsible for over 500 marriages a day and have data from 44 million people looking for love. What’s new are 200 items they collect in questionnaires from their premium customers. They claim that this data harvests six variables:

1. Agreeableness

2. Closeness with partner

3. Sexual and romantic passion

4. Spirituality

5. Extraversion and openness

6. Optimism and happiness

The algorithm then looks for similar scores.

Psychology of matching
But it’s a complex business this match making, as the psychology literature shows. Agreeable, open and optimistic people may just be better at getting on with anyone and it is not clear that dyadic (matching) effects have any real predictive quality. However, Dr. Gonzaga, the Chief Scientist at eHarmony, claims that studies he’s performed shows that couples who match are satisfied four years later.

Lack of controlled trails
This is all very well but what psychologists want to see is a controlled trial and an interesting issue has arisen around the reason for a lack of any randomly, controlled trials. eHarmony claim that it is just too difficult and unethical to randomly pair people who are looking for love, a betrayal of their trust in the service. They have a point. Fragile people looking for partners are not fodder for algorithmic tests.

Data gathering
If you look closely at the six variables, they go beyond the traditional well-established personality traits in psychology, especially on ‘sex and passion’, which eHarmony claims is a key variable. And who would argue? For general users they collect data on hundreds of traits, such as time spent on the site and response times to emails, also geographical data, as people in, say Manhattan, won’t go far for dates. This makes sense, as real people in the real world collect data on potential partners, mostly through awkward questioning on a first date, when it’s too late. EHarmony are in the Big Data game, like Google, Facebook and Amazon, gathering data and feeding that data through algorithms that make recommendations, based on their predictive power. They are right in looking to harvests Big Data, rather than take the tradition statistical route. Their data set may me messier but it will be large and it’s large data that counts. We are clearly only in the foothills of algorithmic matching but eHarmony seem to be leading the trek upwards and have established a good base camp, upon which further work can be based.

Love and learning
Does this have any import for learning? I think it does. We’ve gone through a hideous period in educational theory when ‘learning styles’ dominated the debate. We now know that these are dangerous fictions, with no real evidence-base, that pigeonhole learners and may actually inhibit learning. The good news is that the data one can gather on personality may well be useful in learning as it appears that learning styles do not exist, personality traits do.

In matching learners with learning experiences, data is also clearly useful.

1. One set of learner data could be personality type as this has a strong causal effect on actual motivation and behaviour, learning being a bundle of motivations and behaviours.

2. Algorithms can also be used to determine the learner’s background educational attainment i.e. what the learner already knows, the equivalent of competence levels from formative assessment

3. During the learning process data can be gathered, similar to eHarmony’s approach, such as time on task, response times and so on. This can be used to guide the learner, dynamically, towards more useful and compatible content.

4. In peer-to-peer learning and assessment, dating algorithms may well be adapted to for use in matching peers.

5. Data on failure may be equivalent to data on failed first dates or relationships and used for improvement of interpersonal skills for the next attempt.

Looking for knowledge may not that different from looking for love!


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