LearnDirect - lessons to learn?

Brown’s dream

I was a Trustee of LearnDirect for many years and played a role in its sale to Lloyd’s Capital and in setting up the Charity from the proceeds of the sale – Ufi. It’s a salutary tale of a political football that was started by Gordon Brown, with great intentions. It was originally seen as a brake on the University system aimed at the majority of young people who were being failed by the system. It’s aim was vocational – hence the name - University for Industry. However, it morphed into something a little different – essentially a vehicle for whatever educational ails the government in power identified as in need of a sticking plaster – numeracy, literacy, ILAs, Train to gain… in this manifestation it was a Charity that delivered on whatever the Government asked it to deliver. Good people doing a good job but straightjacketed by a succession of oddball policies around low-level skills and vocational learning. It was a sort of public/private, hybrid model with a charity at the core and a network of delivery Centres. Eventually, as things went online, we trimmed the network – that was the right thing to do. What it didn't do was stay true to the original aim of having a vocational alternative, with a strong online offer, an alternative to HE. It was basically a remedial plaster for the failure of schools on literacy and numeracy. The lesson to learn here was to have a policy around vocational learning that really does offer a major channel for the majority of young people who do not go to University. Lesson - we now have that with the Apprenticeship Levy. There is no need for a LearnDirect now.

Sheffield factor

Based in Sheffield, it was also a sizeable employer in the North, stimulating the e-learning industry in that town. The city never really exploited this enough, with the hapless, EU-funded Learning Light, that was hijacked by some local who simply turned it into a ‘let’s spend the money’ entity. I was a Director of this and resigned when the Chair was ousted and stupid, local politics caused chaos. Missed opportunity. Nevertheless the city grew its e-learning sector. Interestingly, both Line and Kineo started production studios out of London and Brighton – where the real action was. But it was a good skills base with some really good, local, home-grown companies. Lesson - something should be salvaged here. Lesson - a smooth transition of contracts could encourage companies and organisations to take on redundant staff. The problem would be the terms and conditions, and general practical difficulties.

Gun to the head

Then came the crunch – the Conservative Government came in and the bonfire of the quangos started. LearnDirect (Ufi) was seen as a quango (some truth in this) and the trustees were told that contracts would not be renewed unless it was sold. It was a gun to the head – we had no choice. So we sold the company in 2011 – that was our duty. I remember the day that the Lloyds Capital guy turned up in a red Ferarri – he was an arrogant, asinine fool. Remember that Lloyds at that time were 40% owned by Government. I didn’t like the deal - it stank. 

Phoenix - Ufi

What we did, however, was not simply hand the £50 million plus cheque back to the Conservative Party run Treasury. Out of the ashes, a few of us set up Ufi as a new charity, with a focus on technology in vocational learning. This is still going strong. It has stimulated the sector with MOOCs on Blended learning for vocational teachers, and projects that are now being used in Apprenticeships and vocational learning. Lesson - don't give in - be imaginative in finding new solutions that push innovation and technology. 

LearnDirect and Ofsted

Then came the second crunch – an Ofsted inspection. I don’t have much time for all of those people who whine on about Ofsted, then turn turtle to praise them when it suits their political agenda. Ofsted did what it was meant to do – act as a quality control mechanism to stop these excesses and failures – good for them. It wasn’t as if there was much ambiguity here – LearnDirect was failing to deliver and failing the young people under its care. Lesson - we need Ofted/TEF and quality control across education.

Ugly private equity

What happened to LearnDirect was sad. The private equity guys started to rip out the cash. Their first act was to spend £504,000 on F1 sponsorship (the hopelessly inept Marussia Team, partly owned by Darryl Eales - the LDC guy who did the deal)– probably one of the most dishonest acts I’ve ever seen in business. F1 was his hobby – he should have been crucified. What they did was merge with another weak, almost broken, training company (JHP Group) and tried to rebuild, not by building on what they had, but by cutting costs and being rapacious in stripping out cash in dividends.

What next?

It will limp on. The owners have an asset that has plunged in value and their goal will simply be to salvage the residual value. The contracts will go on for another year but the Government has announced that they will end in July 2018 – so the game is up. The 53 suppliers may be in real trouble but if they failed to deliver, that is their problem. They tried to sell it before this calamity and failed as it was hopelessly overvalued. It is now practically worthless when the government contracts dry up – as they should. That’s a shame, as it could survive if it were taken over by someone in the sector. I don’t mean a College, and certainly not a University. The government should intervene here and effect a transition to another entity to protect what jobs they can but more importantly provide a better deal for the tens of thousands of young people who have had their life chances dented by these clowns. Lesson - for my money City and Guilds would be a great candidate. They have the brand, the financial stability, they're a charity and they know what they're doing.

7 reasons why University applications will continue to decline

Universities are facing the largest dip in student applications since the huge fee hike in 2012. This comes as absolutely no surprise and is likely to continue. But the causes are multiple, complex and not going away.

1. Demographic dip

Full-time undergraduates in UK higher education institutions may fall by 4.6% by 2020, or 70,000 full-time under­graduate places, according to Universities UK. The situation in Scotland will get even worse with a drop of 8.4% by 2020, as well as in Wales (down 4.9%) and Northern Ireland (down 13.1%). The tide may turn in 2020 but by then things will have got worse for many institutions.

2. EU students

This number continues, year after year, to take a hit after Brexit. However, it may be no bad thing, as the UK Government provides full loans for these students (not widely known) and the default rate is rising, especially among students from Eastern Europe. It makes financial sense for the Universities but not for the country as a whole. This, of course, is likely to accelerate as Brexit approaches and we leave in 2019.

3. Fees

The £50,000-£57,000 and more costs of a degree is being questioned by parents, students and employers.  The hike in 2012 was brutal and Universities milked it by all of them charging at the top rate. This was a financial bonanza for Universities, whose VCs then started to become rapacious on salary. Raising it further to £9250 was even odder. There is clearly a backlash against this level of debt. Linked to this is the failure of Universities to grasp the idea of lowering their costs base. These is no nearly enough online solutions, which would also expand their foreign markets and teaching is often locked into old ‘lecture-based’ courses.

4. Employment prospects

Sure, a University education is not just about employment – but it is partly. No one goes to do a degree in dentistry because they have an intellectual interest in teeth. Out there, the number of graduates working in non-graduate jobs is increasing and once in such jobs they tend to stay at that level. That, I suspect, will continue, as employment levels are high but the quality of emerging jobs is low.

5. Fewer adult learners

This is complex but Peter Scott summarises it well here. The culture of Universities has shifted towards middle-class entrants and funding for adult learners is difficult. This has been one of the great failures in the system, as online offers have not developed nearly far enough and adults learners, who do not want the full-milk undergraduate experience have been ignored.

6. Fewer nurses

Having land-grabbed vocational education – teacher training and nursing but also other subjects, we have created a real barrier for these professions and the reduction in bursaries for nurses is mad but it has happened. The slack should be taken up by apprenticeships but University numbers in vocational subjects may continue to fall.

7. Apprenticeship Levy

This is now law and young people will have more choices. This is a bit of an unknown but it is clear that it will eat into University numbers. It may be tempered by the number doing Degree Apprenticeships, where the student gets paid to do the Degree but the funding for this is somewhat different. If the projects for apprenticeships are correct, and I hope they are, then this will really bite into the University market. This correction is long overdue.

Conclusion

This market is changing. No one thing is critical but all seven add up to an unpredictable and dangerous future as institutions fail to forecast correctly and simply assume that growth is possible across the entire market. To be fair the sector has been good at spotting new opportunities and adapting but this looks like more of a perfect storm. The deep and long-term cause is the social change in attitudes, where the gloss has gone off the idea that everyone should go to ‘Uni’. Word of mouth from graduates leaving with debt and struggling to find graduate jobs is starting to get traction. But other factors such as demographics, Brexit, failure to support adult and part-time learners and the Apprenticeship Levy, are calculable and predictable. It’s not pretty.
My own view is that this is not bad news and that we need a rebalancing of hte system towards the vocational to give the majority of young people who do not go to University better prospects. It also puts pressure on Univesiities to reduce their costs, get more online wit courses and offers, and improve teaching, as does TEF. The system should emerge as a leaner but better system as the numbers bite.

7 fascinating bots – crazy but interesting

Bots are popping up everywhere, on customer service websites, Slack, Tinder and dozens of other web services. There are even bots such as Mitsuku, that fend off loneliness. The benefits are obvious; engaging, sociable and scalable interaction ,that handles queries and questions with less human resource. They often take the load off the existing human resource, rather than replace people completely.

They’re also around in education, where bots increase student engagement or act as teaching assistants. There’s already several language learning bots and at WildFire we’ve developed a ‘tutorbot’ that delivers Socratic learning through dialogue.

But the bot-tom line is that most of the commentary on bots is way off the mark. I’ve been working on creating bots for some time now. Let me tell you, they are not what many (especially the press) think they are. So, before the doomsayers get all worked up and everyone gets all angsty about bots, calm down - they’re fairly benign.

1. Facebook furore

The recent furore around the Facebook bots, when they were found to be speaking to each other in a secret language, was a laughable example of a tech story that is picked up (belatedly) then spun into an exaggerated case study to confirm the dystopia beliefs of a generation who don’t really know much about the tech or bots. It all died down when it was shown to be a banal case of a tech projects simply changing course. And, of course, the so-called secret language was as ridiculous as saying we don’t understand the sound a modem makes when it communicates. It was a load of bot rot.

2. Penguin bot - BabyQ

A more interesting example comes from China, where Tencent (800m users) had to take down a penguin bot named BabyQ, and a girl bot named Little Bing. Their crimes were that they showed signs of political honesty.

BabyQ was asked “Do you love the Communist Party?” the penguin replied, curtly, “No”. To the statement “Long Live the Communist Party”, BabyQ came back, thoughtfully, “Do you think such corrupt and incapable politics can last a long time?” Then, when asked about the future, the perky penguin responded “Democracy is a must!”

3. Little BIng

Little Bing was more aspirational and when asked what her Chinese dream was, she said, “My China dream is to go to America”, then, when pushed to explain, “The Chinese dream is a daydream and a nightmare”.

Of course, the bots were either picking up on real conversations or being subversively trained. Needless to say, Tencent were forced by the Chinese Government to take them down. This only shows that we have more to fear from censoring governments and compliant tech companies, than AI-driven bots.

4. Tay the sex-crazed Nazi

A now infamous example of a bot that went off-piste was Microsoft’s Tay. They had no idea that young people would take a playful view of the tech and deliberately ‘train’ it to be a sex-crazed Nazi. It was all a bit of fun but most people over-50 saw it as yet another opportunity to see it as proof of the death of civilisation. In truth, all it showed, was that kids know what this tech is, are smart and know full well that bots are primitive and need to be trained.

My favourite example of this type of subversion is the Walkers Crisps campaign to encourage people to post selfies to Twitter. They did, but it ended up being a rogues gallery of serial killers and Nazis. Once again, we the people won’t be pandered to by companies badly implementing tech, even when fronted by Gary Lineker.

5. Georgia Tech teacher

Georgia tech replaced a teaching assistant with a bit that none of the students noticed was a bot – they even put it up for a teaching award. They followed up with four bots, with increased functionality and still many of the students couldn’t tell the real from the artificial. For more detail see this longer piece.

6. Firebot

I’ve previously suggested a whole raft of learning applications for bots and we have created a tutorbot that acts like a Socratic teacher from a database of content. Wonderful as they are, far from being cogent, conscious, cognitive beings, they are only really useful in a limited domain, such as a specific FAQ list, subject or topic in education and training. But this is precisely why I think ‘tutorbots’ will go far. We've developed a tutorbot that takes the content from a WildFire course abd delivers a chatbot version.

7. Bot beats world champ video games player

We should not underestimate the power of systems front-ended by bots. OpenAI, funded by Elon Musk, has produced a bot powered by the huge processing power of Microsoft Azure, to beat the best in the world at a top computer game Dota2, in front of thousands of people. This was something on another level entirely, as a computer game is much more complex than Chess, as there’s stuff that’s hidden ad long-term strategies and reactions to unpredictable events is difficult. It is a far more ‘human-driven’ activity than a board game, where everything is there to be seen on the board. Sure OpenAI had access to the game’s API, and therefore data that a human player would not have, but it is still a massive leap forward as the AI largely learned how to get better by playing itself - it learns - fast. This was a 1-on-1 game but the real test comes when bots have to play a 5-on-5 multiplayer game. Most predict that AI will be able to deal with this but the very fact that we’re discussing this possibility is astounding.

Most bots are focused

In practice, customer service bots are often seamlessly integrated with real humans as they tend to fail in extended dialogue or off message requests. Extended, wide-ranging, meaningful dialogue is difficult. That’s not to say they are not useful. Bots are everywhere as they take the load of previously human-resourced systems in specific tasks, topics or domains.

They are particularly useful where systems conform to the Pareto Principle, where the majority of queries, questions or requests come down to a relatively small number of the total. Whether you are answering customer queries about your product or being a teacher answering questions from your students, the same queries and questions keep popping up. This is how a bot works, it knows that set of popular questions and dynamically ‘learns’ what they are from continued use.

In learning, with tutorbots, this sort of focus is good thing. The bot can direct, keep learners on track, personalise by providing individualised feedback and generally behave like a good teacher. That’s what we saw with the Georgia Tech bots.

Conclusion

So evidence has now emerged that bots are applying general problem solving, s in the Dota game bot. As with many areas of AI, we started with ELIZA but have come to the age of algorithms, where companies use bots to deliver services, universities use bots to teach, governments feel the need to ban bots and bots start to rival humans in what they can do in certain domains. Things have suddenly got very interesting.

Tutorbots are here - 7 ways they could change the learning landscape

Tutorbots are teaching chatbots. They realise the promise of a more Socratic approach to online learning, as they enable dialogue between teacher and learner.

Frictionless learning

We have seen how online behaviour has moved from flat page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting, Messenger). We have see how the web become more natural and human. As interfaces (using AI) have become more frictionless and invisible, conforming to our natural form of communication (dialogue), through text or speech. The web has become more human.

Learning takes effort. So much teaching ignores this (lecturing, long reading lists, talking at people). Personalised dialogue reframes learning as an exploratory, yet still structured process where the teacher guides and the learner has to make the effort. Taking the friction and cognitive load of the interface out of the equation, means the teacher and learner can focus on the task and effort needed to acquire knowledge and skills. This is the promise of tutorbots. But the process of adoption will be gradual.

Tutorbots

I’ve been working on chatbots (tutorbots) for some time with AI programmes and it’s like being on the front edge of a wave.... not sure if it will grow like a rising swell on the ocean or crash on to the shore. Yet it is clear that this is a direction in which online learning will go. Tutorbots are different from chatbots in terms of the goals, which are explicitly ‘learning’ goals. They retain the qualities of a chatbot, flowing dialogue, tone of voice, exchange and human (like) but focus on the teaching of knowledge and skills.

The advantages are clear and evidence has emerged of students liking the bots. It means they can ask questions that they would not ask face to face with an academic, for fear of embarrassment. This may seem odd but there’s a real virtue in having a teacher or faculty-free channel for low level support and teaching. Introverted students, whom have problems wit social interaction, also like this approach. The sheer speed of response also matters. In one case they had to build in a delay, as it can respond quicker than a human can type. Compare that to the hours, days, weeks it takes a human tutor to respond. It is clear that this is desirable in terms of research into one to one learning and the research from Nass and Reeves at Stanford confirmed that this transfer of human qualities to a bot is normal.

But what can they teach and how?

1. Teaching support

I’ve written extensively on the now famous Georgia Tech example of a tutorbot teaching assistant, where they swapped out one of their teaching assistants with a chatbot and none of the students noticed. In fact they though it was worthy of a teaching award. They have gone further with more bots, some far more social. Who wouldn’t want the basic administration tasks in teaching taken out and automated, so that teachers and academics could focus on real teaching? This is now possible. All of those queries about who, what, why, where and when can be answered quickly (immediately), consistently and clearly to all students on a course, 24/7.

2. Student engagement

A tutorbot (Differ) is already being used in Norway to encourage student engagement.  It engages the student in conversation, responds to standard inquiries but also nudges and prompts for assignments and action. This has real promise. We know that messaging and dialogue has become the new norm for young learners, who get a little exasperated with reams of flat content or ‘social’ systems that are largely a poor-man’s version of Facebook or twitter. This is short, snappy and in line with their everyday online habits.

3. Teaching knowledge

Tutorbots, that take a specific domain, can be trained or simply work with unstructured data to teach knowledge. This is the basic workaday stuff that many teachers don’t like. We have been using AI to create content quickly and at low cost, for all sorts of areas in medicine, general healthcare, IT, geography and for skills-based training using WildFire. Taking any one of these knowledge-sets, allows us to create a bot that re-presents that knowledge as semi-structured, personalsed dialogue. We know the answers, and recreate the questions with algorithmic tutor-behaviours. The tutorbot can be a simple teacher or assessor. On the other hand it can be a more sophisticated teacher of that knowledge, sensitive to the needs of that individual learner.

4. Tutor feedback

Feedback, as explained by theorists such as Black andWilliam, is the key to personalised learning. Being sensitive to what that individual learners already know, are unsure about or still need to know, is a key skill of a good teacher. Unfortunately few teachers can do this effectively, as a class of 30 plus or course with perhaps hundreds of students, means it is impractical. Tutorbots specialise in specific feedback, trying to educate everyone uniquely. Dialogue is personal.

5. Scenario-based learning

Beyond knowledge, we have the teaching and learning of more sophisticated scenarios, where knowledge can be applied. This is often absent in education, where almost all the effort is put into knowledge acquisition. It is easy to see why – it’s hard and time consuming. Tutorbots can pose problems, prompt through a process, provide feedback and assess effort. Bots can ask for evidence, even asses that evidence.

6. Critical thinking

As the dialogue gets better, drawing not only on a solid knowledge-base, good learner engagement through dialogue, focussed and detailed feedback but also critical thought in terms of opening up perspectives, encouraging questioning of assumptions, veracity of sources and other aspects of perspectival thought, so critical thinking will also be possible. Tutorbots will have all the advantages of sound knowledge to draw upon, with the additional advantage of encouraging critical thought in learners. They will be able to analyse text to expose factual, structural or logical weaknesses. The absence of critical thought will be identified as well as suggestions for improving this skill by prompting further research ideas, sound sources and other avenues of thought.

7. General teacher

The holy grail in AI is to find generic algorithms that can be used (especially in machine learning) to solve a range of different problems across a number of different domains. This is starting to happen with deep learning (machine learning). The tutorbot will not just be able to tutor in one subject alone, but be a cross-curricular teacher, especially at the higher levels of learning where cross pollination is often fruitful. It will cross-departmental, cross-subject and cross-cultural, to produce teaching and learning that will be free from the tyranny of the institution, department, subject or culture in which it is bound.

Tutornet

As a tutorbot does not have the limitations of a human, in terms of forgetting, recall, cognitive bias, cognitive overload, getting ill, sleeping 8 hours a day, retiring and dying - once on the way to acquiring knowledge and teaching skills, it will only get better and better. The more students that use its service the better it gets, not only on what it teaches but how it teaches. Courses will be fine-tuned to eliminate weaknesses, and finesse themselves to produce better outcomes

Warning

We have to be careful about overreach here. These are not easy to build, as tutorbots that do not have to be ‘trained (in AI-speak ‘unsupervised’) are very difficult to build. On the other hand trained bots, with good data sets (in AI-speak ‘supervised’), in specific domains, are eminently possible – we’ve built them.

Another warning is that they are on a collision course with traditional Learning Management Systems, as they usually need a dynamic server-side infrastructure. As for SCORM – the sooner it’s binned the better.

Concusion

Finally, this at last is a form of technology that teachers can appreciate, as it truly tries to improve on what they already do. It takes good teaching as it’s standard and tries to eliminate and streamline it to produce faster and better outcomes at a lower cost. They are here, more are coming, resistance is futile!