Socrates (469-399 BC) – Socratic method, man of dialogue...

Socrates was one of the few teachers who died for his craft, executed by the Athenian authorities for supposedly corrupting the youth. That in itself has earned him eternal fame. Most learning professionals will have heard of him through their acquaintance with the ‘Socratic method’ but few will know that he never wrote a single word describing this method, fewer still will know that the method is not what it is commonly represented to be.

How many have read the Socratic dialogues? How many know what he meant by his method and how he practised his approach? 

Socrates, in fact, wrote absolutely nothing. It was Plato and Xenophon who recorded his thoughts and methods through the lens of their own beliefs. We must remember, therefore, that Socrates is in fact a mouthpiece for the views of others. In fact the two pictures painted of Socrates by these two commentators differ somewhat. In the Platonic Dialogues he is witty, playful and a great philosophical theorist, in Xenophon he is a dull moraliser.

Socratic method

That the teacher should be an intellectual midwife to the student’s own thoughts is his great educational principle. His mother was indeed a midwife and he was among the first to recognise that, in terms of learning, ideas are best generated from the cognitive effort of the learner in terms of understanding, realisation and retention. Education is not a cramming in, but a drawing out.

He would claim that he taught nothing as he had nothing to teach and his lasting influence is the useful idea, that for certain types of learning, questioning and dialogue allows the learner to generate their own ideas and conclusions, rather than be spoon-fed. 

What is less well known is the negative side of the Socratic method. He loved to pick intellectual fights and the method was not so much a gentle teasing out of ideas, more the brutal exposure of falsehoods. He was also roundly ridiculed in public drama, notably by his contemporary Aristophanes in Clouds, where he uses the Socratic method to explore idiotic ideas using petty, hair-splitting logic.

Socratic philosophy of education

Beyond the famous Socratic method, he did have a philosophy of education that included several principles.

Knowledge and learning were seen by him as a valuable pursuit, with a ruthless pursuit of questioning even basic assumptions. This was achieved socially through dialogue, not by lecturing or the transfer of knowledge from teacher to learner. The aim of learning was to pursue, with a ruthless intellectual honesty, answers to difficult questions. Ultimately, and this was almost always Socrates main aim, was to get the learner to realise that they didn’t know as much as they thought they knew, the realisation of our own ignorance.

Socrates concerns himself largely with high-end, critical thought. His legacy in not so much in his method as being used by a model, by Plato, of the free and open thinker, unafraid to question the most basic suppositions. It is this spirit of inquiry, seen in Greek thought, most intensely by Socrates, that fueled education for the next two Millenia.

Influence

The Socratic method has transformed itself into the idea of discovery learning, but there have been severe doubts expressed about taking this method too far. We wouldn’t want our children to discover how to cross the road by pushing them out between parked cars! In practice, it is most often no more than a teacher using open or inductive questions. In fact, when used crudely it can frustrate learners, especially when not combined with genuine dialogue and feedback. To ask open questions about facts can be pointless and result in those awful classroom sessions where the teacher asks a question, hands shoot up and the few who already know the answers, answer the question, while the rest feel foolish. When used well, however, especially in subjects such deal with abstract thought and for uncovering conceptual clarity, it has lots to offer.

There is still a great deal of discussion and controversy around whether learning should be a process of exploration and discovery, as opposed to direct instruction. There are extremes on both sides. Discovery learning was taken up with enthusiasm in the modern age, while Universities in particular have stuck rigidly to direct instruction through lectures as their primary pedagogy. In practice, depending upon the age of the learners, type of learning and context both have their place.

Online learning

Interestingly, the Socratic approach is also often to be found in online learning. Roger Schank has taken the method forward into online designs based on questions which access indexed content, especially videos. One could also argue that search based inquiry through Google and other online resources allows the learner to apply this questioning approach to their own learning, Socratic learning without a Socratic teacher. Chatbots, which now support and deliver learning are now being used to emulate the Socratic model and deliver personalized support, tutoring and even mentoring to learners. Adaptive learning systems, truly account for where the learner has come from, where they are going and what they need to get there. Sophisticated online learning allows us to realise the potential of a scalable Socratic approach without the need for face-to-face teaching. Interestingly, it is only in the last few decades, through the use of technology-based tools that allow search, questioning and now chatbots and adaptive learning, that Socratic learning can be truly realised on scale.

As someone who abhorred didactic, talk and chalk teaching and learning, Socrates would be appalled at current education and training. He was not an institutional figure, practiced his teaching in the public space of the Agora and thought that experts were normally fooling themselves by believing they had immutable knowledge to impart to their students. The unexamined life may not be worth living but neither is a life of absolute certainty. 

Of course, if we were to behave like Socrates in the modern school, college, university or training room, we’d be in front of several tribunals for bullying, not sticking to the curriculum and failing to prepare students for their exams. Not to mention his pederasty. We can perhaps put this to one side as a feature of the age! 

Bibliography

Hamilton, E., Cairns, H. and Cooper, L., 1961. The collected dialogues of Plato. Princeton University Press.

Tarrant, H. ed., 2003. The last days of Socrates. Penguin.

Mackendrick, P., 1974. Aristophanes. Lysistrata. The Acharnians. The Clouds. Trans. AH Sommerstein.(Penguin Classics.) Harmondsworth: Penguin. 1973. Pp. 255. 40P. The Journal of Hellenic Studies, 94, pp.185-186.

Ferguson, J., 1970. Socrates: a source book.

Woodbridge, F.J.E., 1934. The Son of Apollo (Boston and New York, 1929)

Talk To Me by James Vlahos; great read for those working in or interested in bots...

We have Alexa in three rooms in our house – living room, kitchen and bedroom and they’re all used every day. I use it for work (calculations for VAT, invoices, scheduling), cooking (timers), shopping (lists), lights (off at night), robot vacuum cleaner and lots of queries. Google Assistant on my Pixel phone is now my PA. Voice, through use and habit, has become part of my life – my frictionless interface – easy and convenient. 

As one of the great triumphs of AI voice is on our phones, in our cars and in our homes. Amazon, Google, Microsoft and Apple all see it as a strategic technological advance. We take years learning how to read and write, yet we listen and speak almost effortlessly, grammatical geniuses aged three.

So it was great to come across a readable book that dealt with the territory to date. The history of voice and chatbots is well covered as it did not spring out of nowhere but from centuries of maths, statistics and probability theory, then pioneers who applied the maths, and AI, to the recognition, understanding and generation of language and voice.

Vlahos explains why all the moral hysteria around the gender of voice assistants is misplaced. Far from being a patriarchal plot; Siri, Cortana, Alexa and Google Assistant were all extensively researched and all but one give users the choice of gender. Turns out that even in the womb, a woman’s voice is liked and trusted. We are not only wired for speech but for female speech. The research showed that female voices win hands down. There are also fascinating insights into the personas chosen, all very different. 

The chapter on AI, machine learning, deep learning, backpropagation, supervised and unsupervised learning is told well, not too technical. The technology behind speech recognition, language understanding and speech generation is also readable. Good also to see the issue of information retrieval from both structured and unstructured data dealt with - search, knowledge graphs, the Stanford Question Answering Dataset. 

He then moves on to the next level with a chapter on ‘conversation’ describing progress through competitions to win the Alexa Prize, Loebner Prize and Winograd Scheme Challenge, These show how difficult it is to sustain conversation in chatbots, with the need for human scripting as well as an ensemble of programming and AI techniques. Above all you learn that the data gathering makes these systems better and better. As Vlahos says, “Voice AIs blur boundaries” of intimacy, privacy, mind and machine, fact and fiction, life and death.

The implications when voice becomes a dominant force may be the weakening of ad revenue as a business model. How do you get your voice heard? Mobile is accelerating voice as are home devices and the demand for voice search and smart assistants. Voice is here to stay and although people think that AI has a heart of stone, chatbots and voice bring humanity to technology. It is an illusory humanity, of course, but it can represent and reflect us, making technology at least more humane, certainly more usable.

Not much has been written on ‘voice’ despite its dramatic rise in consumer technology so for anyone who is involved in AI, chatbots, IOT and wants a feel for this particular strand of technology, this book mines the voice vein rather nicely.

PS

It is a pity that this is not being adopted more widely in online learning, beyond language learning. We have ‘voice recognition’ working in WildFire, an AI content generation tool that creates online learning in minutes not months.

Re-engineering Humanity by Brett Frischmann and Evan Seliger - slippery slope argument that tech steals into our lives with 'cheap bliss'......

Mildly dystopian critique of technology and AI that suggests that we are on a slippery slope, as technology steals into our lives, resulting not in enlightenment but ‘cheap bliss’ and a loss of control and judgement. It is not that we are creating robots but that we are becoming more robotic through what they call techno-engineering.

Like Postman in Amusing Ourselves to Death, and McLuhan, the authors claim that we are being lulled into outsourcing to technology allowing its allure to trap us into its patterns, not ones we freely choose, but patterns of surveillance and obedience. This gradual creep of technology destroys our humanity as contemporary technology invades our minds at three levels; micro- fitness trackers, meso- smart transport and macro- Facebook.

Nicholas Carr in the Foreward, describes the book as a ‘balanced examination…’ but I'm not convinced. It is singular in intent and displays the anti-corporate line that is common in academia. The tech companies are the ‘Frightful Five’ and Taylorism is critiqued to death but, in the end, a bit of a straw man. True, we fetishize technology, or at least the devices, but the idea that Taylorism and Fordism (those old canards) make us impotent puppets is a stretch. Learning faster, saving time and productivity seem like admirable aims to me. There are plenty of administrative and repetitive tasks, student support and marking in education for example, that could do with a dose of efficiency using AI.

Surveillance creep is weakly argued through examples like Fitbits and tracking kids at school, where the benefits seem to be ignored in favour of a position on privacy that few would get worked up about. That aside, it perks up on the dangers of passivity, decreased agency, decreased responsibility, increased ignorance and detachment. Drone parents and brain sensors test moral boundaries, although the usual argument that GPS weakens cognitive control is now a bit tired. 

The Chapter on extended mind theory; extended body, extended cognition, distributed cognition and cognitive technology is informative. Boden, Chalmers, Clark are all explained in detail – how the mind or consciousness can be redefined and widened by technology. This cleverly opens the door for the slippery slope arguments as they rough up mass media and, inevitably ‘surveillance capitalism’ is invoked, before beating up Facebook, the IoT and the quantified self.

They see themselves as policing determinism and fighting harmful influence through two principles:              

1. Freedom to be off
2. Freedom from engineered determinism

Nozick’s ‘experience machine’ is constantly hauled in and although a fine thought experiment, I doubt that it has more than instrumental use to bolster the dystopian future they fear. Their suggested new framework cleverly proposes a reverse Turing test, where we test to see if we humans are becoming machines. This drifts off into a discussion of free will and engineered determinism but I fear that good philosophy has been sacrificed on the altar of their slippery slope hypothesis.

It does have some innovative ideas, such as a BBC style social network and more mainstream ideas like net neutrality and legal reforms strengthening the rights of individuals and regulation. They end, for example, with GDPR’s principle of consent as a good example of how things should evolve. But all too often it slips down its own slippery slope towards giving good old capitalism, markets, Taylorism and Fordism a kicking. 

To be fair, their case is detailed and well argued. I found the book thought provoking and although it clearly has some truths, the slippery slope is, in reality, perhaps more of a dialectic between minds and machines. By presenting a rather one-sided analysis, largely ignoring the benefits of new technology, they weaken their objectivity. That said, it is a good exposition of what could be called a weak dystopian position, stopping short of the full dystopian visions of an existential AI apocalypse.

This book will also introduce you to some interesting thinkers such as Searle, Weizenbaum, Chalmers, Clark, Nozick and many others but Daniel Dennett seems like a bizarre omission. 

Human Compatible by Stuart Russell - go to guy on AI - a must read..

Unlike Bostrum’s Superintelligence, this book is eminently readable. Russell is not only an AI guy, he is a renowned educator and has won awards as an educator. Along with Peter Norvik, he wrote the classic, globally recognised textbook AI Artificial Intelligence: A Modern Approach and has been in AI long before most of the arrivistes currently bashing out books on AI and ethics.

He anchors his ethical arguments on the problem of ‘control’, machines that achieve our not their objectives. Unlike many of the popular books on ethics in AI, from contrarians like Cathy O’Neil, Safiya Noble and Shoshana Zuboff, who tend to throw the babies and bathwater out with the bath, he is level-headed and draws upon his decades of experience as a pioneer in the field to explain the flaws, limitations and dangers, as well as the power, promise and potential of AI.

He takes on his opposition directly and unlike Kevin Kelly, Andrew Ng and Stephen Pinker, he thinks that Superintelligence is a possibility. This is a brilliant summary of the main positions with Musk, Hawking and Gates at one end of a spectrum and Pinker at the other. Russell is in the middle, scared but offering a change of direction and solutions.

AI and learning
He points towards significant advances through AI in health, finance and education. The Global Learning XPRIZE is cited (you never hear about this but it’s important) and gives us 80 years before human-level AI arrives. Note that super-competent teaching AI may arrive decades before this. Understanding language is essential as is understanding and building hierarchies of abstraction. He scotches the idea of robots carrying around their own brains (therefore robot teachers) in favour of connected computing, EaaS, Everything as a Service. The low hanging fruit may well be in accelerating learning in humans. AI tutors, he thinks, will deliver learning to all, at negligible cost.

Unlike most treatments of ‘bias in AI’ Russell remains cool and objective in surfacing the problems but also patiently explaining the solutions. He avoids the usual teeth gnashing about capitalism, ethicswashing and ethicsbashing, for a typically balanced analysis of the problems but also the solutions – pre-and post processing, embedding fairness criteria and so on. 

Beneficial AI
Provably beneficial AI systems will need clear principles around altruism, humility and reference to human preferences. Beneficial AI is AI where we know what the purpose is. This is not as easy as it sounds and he uses AlphaGo to show how tricky it is -  is the goal 'winning' or 'reinforcement learning where winning is the reward'?

Preference Utilitarianism
The complication in all this is we humans. His take on moral philosophy is utilitarian, or rather Harsanyi’s preference utilitarianism where AIs only purpose is to manifest human preferences. His defence of this position is impressive and if you want to have a position on AI and ethics this is well worth reading as he rightly takes ethics (moral philosophy) seriously – many don’t which leads to fantasy castles of certainty built on quicksand.

Everyone and their uncle
The old model of technology that meets an objective is no longer valid. We need technology that defers to humans and can be switched off. Rather critical that ‘everyone and their uncle’ is setting up an AI and ethics board, department, framework etc., he proposes we give the top institutions a chance here – AAAI, IEEE, Partnership on AI and EU. It is not that others do not matter, only that there is too much low-level noise.

The strength of the book is that it is neither dystopian nor utopian. His line is realism, both about the dangers and opportunities. This is a book of some depth as he faces up to the possibility of an existential threat not with platitudes, anthropomorphism or manifestos butvreal solution in terms of moral philosophy and practical steps.

Of all the books I’ve read on AI and ethics, this, along with pieces from Dennett and Pinker have been the best. These three have truly shaped my own opinions on ethics in AI. I lean perhaps more to Dennett and Pinker in thinking the problem of superintelligence is not as likely, extreme or unmanageable as many suggest but this book is as good as you get on facing up to the perils, even if they are improbable. AI poses risks he but remains optimistic that we can retain control and harness the technology for good. 

My mate Doug - things I've learnt Part III....

My mate Doug…

“Hell is other people” he said, that French bloke… but heaven is time on your own with your dog. Sometimes it’s just him lying there on the carpet, throwing the occasional glance my way. Sometimes it’s on a walk, him steaming ahead, a glance back to see that I’m still there. Sometimes we step into The Flour Pot, a café where he sits beneath the table checking all who enter. We like it there. Mostly it’s just the satisfying sense that we’re together, mates really.

We got locked out the other day – went for a walk and forgot our keys. Had to sit on the doorstep for about 40 minutes waiting on Gil to return. Doug sat coiled in my lap and fell asleep. We kept each other warm. A woman stopped, ‘Are you alright?’ she said…. ‘Never better’ was my reply…

There are times, on my own, when Doug brings his orange lama toy to me. It squeaks when he presses it in his jaw. I pull, he pulls, and we have a little tug of war. Then it comes free and I throw it to the other end of the room. He turns and zooms down to grab it, shakes it, and brings it to me. I haven’t really played for years, now I play every day and you know what… it’s OK to play. 

He stole Gil's blusher the other day - chewed it to bits (see pic). She went mental but when she left the room we had a good laugh together. You see I also speak to him… a lot. Sure he hasn’t a clue what I’m saying but that’s not the point. The point is that I’m saying stuff I really mean, not that chit-chat you get between humans but the real deal – mate to mate.

At times Doug looks at me with his black eyes then tilt his head and we commune. I have no idea what’s in there but it’s something simple, nice and loving. There’s no narcissism in dogs… it’s more give than take. Descartes thought that dogs had no soul, no consciousness, just cold machines. Descartes was a damn fool. John Searle has four dogs Frege, Russell, Ludwig, Tarski... and he loves them, although, as he admits, they are all hopeless with logic.

Part I
Part II

Video for learning –15 things the research says – some may shock you…

Video for learning is great at some things, not so great at others. Some time back I attended a session on video for learning and found that neither the commissioners nor the video production company had looked at a single piece of research on video and learning. They told us that people loved the work and since then I’ve seen a lot of 'Netflix for learning’ delivery. That’s fine but let’s step back a minute..What I didn’t see was much evidence that people were actually ‘learning’ much. I have been involved in video production for 35 years, from corporate videos delivered on VHS cassettes, interactive videos on Laservision (in many ways way beyond what we currently see), CD-ROMs and now streamed. We even made a feature film The Killer Tongue – lost a pile of money on that one and lesson learnt.

Video is great for emotional impact and so attitudinal shift, showing processes and procedures, moving objects, going inside things down to the micro-level or to places we can’t get to like space or the inside of a nuclear reactor. It can also present people, talking to you. But what works best for LEARNING? The evidence shows that many of the things we do in video are just plain wrong. There’s decades of research on the subject, that mostly remains unread and unloved. For a good summary, read Brahme (2016). So let’s surface a few of these evidence-based pointers.

What can we learn from Netflix?

It wiped out Blockbuster and has become a behemoth in the entertainment industry because it adopted a demand-AI-driven, technological and personalised solution in a world that was stuck in scheduled, supply-side delivery. Netflix is easy to use, searchable and personalized, extensively A/B tested, tiled and has an AI recommendation engine behind the interface. It is also time-shifted, streamed and multi-device. But what really makes it sing is the data-driven algorithmic delivery. This is why learning and video folk need to pay attention to technology, especially AI. That other behemoth, YouTube, the largest learning platform on the planet, is also searchable, personalised and time-shifted.

But Netflix is entertainment, not learning content, so what more do we have to learn, from cognitive science about what makes great learning video?

Episodic v semantic memory

Many can recall famous scenes from their favourite film. Mine is the Rutger Hauer scene from Bladerunner, where he sits, dying on the roof in the rain and gives a moving soliloquy about the joys of life, forgiveness and death. Many recall the scene well but few can remember what he actually said. That is because our episodic memory is strong and video appeals to that form of memory, but video is terrible on semantic knowledge, what we need to know in terms of language. I can remember the scene and literally play it back in my head but I can’t remember the speech. This is why video is not so good at imparting detail and knowledge. There is a big difference between recalling episodes and knowing stuff. Yet so often we see talking heads and text on screen that tries to do exactly that.

Shooting star

Learning is a lasting change in long-term memory and video suffers from the lack of opportunity to encode and consolidate memories. Your working memory lasts about 20 seconds, can only hold three or four things in the mind at one time and without the time to encode it is quickly forgotten Sweller (1988). Our minds move through video like a shooting star, where the memories burn up behind us. Without additional active, effortful learning, we quite simply forget. A big warning here, people think they have learnt from video but as Bjork (2013) and many others have shown, much of this is illusory learning, mistaking the feeling that you’ve learnt things but when tested, you have not.

How long do learners engage?

We know from Guo (2014), who had a large data set of learning video data gathered from MOOCs, that learners drop out in large numbers at around six minutes. It drops dramatically down to 50% at 9-12 minutes and 20% beyond this. Evidence from other studies on attention, using eye-tracking, confirm this quite rapid drop in arousal Risko (2012). Keep videos at 6 minutes or less – the less the better.

Student control of video?

Should you give learners control of video? Yes. In fact giving them 'chapter' points beyond the usual forward, back and pause controls proves useful, as it matches the structure of the learning experience Zhang (2006).

Video, audio, text on screen?

It is vital to reduce cognitive load in video. Mayer (2003) and others have shown that text plus audio plus video on the screen, commonly seen in lecture capture, actually inhibits learning. Don’t put captions, text or scripts on the screen while the narrator or person on the screen is talking. One exception to this (rules are there to be broken) is in language learning, where the ability to jump from voice to text can prove useful. Also avoid complex backgrounds and background music, as they also inhibit learning Brahme (2016).

Talking heads or not?

This comes as a bit of shock to many but talking heads, that staple of lecture capture and MOOCs, is often not such a great idea. It can be useful if the person is a respected and well-known expert and there is evidence that the mere knowledge of them as famous experts increases retention and there is a social learning effect. Inspirational talks, well-delivered with emotional impact can also work as do short introductions, links and goodbyes, to take advantage of the social effect. But there is nothing worse, in terms of learning that an autocued talking head, mechanically delivering a scripted piece to camera in the expectation that people will remember what he or she has said – they won’t Chaohua (2019). Fiorrella and Mayer (2018). We have ample evidence for this in the Khan Academy where you don’t see Salmon Khan’s face, only the working through of examples or images. Cognitively, the face is often just noise, what you want to see is the actual maths, with the hand drawing and writing on screen. For subjects that are semantically rich, getting rid of the face is a good thing as it reduces cognitive load. Social cueing does seem to work, for example, at start and end of lesson and, of course, for subjects where visuals are less useful - story-telling, philosophy and so on.

Recommendations on speech?

Avoid over-formal, TV-news like presentation from autocue. The evidence suggests that  informal is good, enthusiastic is good, personal ‘I’ ‘you’ ‘your’ is good. Speaking between 185–254 words per minute seems to be optimal and no background music Mayer (2008), Brahme (2006).

Perspectives in learning video?

When showing the manipulation of objects by hand or, in general, procedures and processes, video shot from the perspective of the learner, is better that third-person viewpoints. This may upset traditional Directors but it means that you get cognitive congruence Florella (2017).

How does image size affect learning?

This should worry those whose video as a great medium on smartphones. The smaller the screen the less you learn. Or the larger the screen the more you learn. Nass and Reeves (1996) showed 60 video segments to 125 adults on different screen sizes and tested them a week later. Those that had watched the larger screen remembered “significantly more”.

How does video quality affect learning?

Video quality doesn’t appear to matter as much as audio quality. That’s because our vision has evolved to cope with twilight, different light conditions and so on. Our hearing, on the other hand, is designed for close proximity speech Nass and reeves (1996). Their research showed that on attention, memory and evaluation, video quality had “no psychological advantages” whereas audio quality was critical.

How do video cuts and pace affect learning?

Cuts raise attention, about 1 sec after the cut but too many cuts lowers attention

So be measured with cuts and make sure they match the ‘need to know’ learning objectives. It is worth going at a slower pace with cuts, as the learners needs to process information before it goes from working to long-term memory Nass and Reeves (1996).

Should we chunk video?

OK here’s the big one. Learning improves when there are “visual rests” and memory is enhanced when ”people have a chance to stop and think about the information presented". We need to stop and think about that a little. It seems as though micro-chunking video down to smaller segments is a good thing for learning Florella (2019).

What should the relationship be between video and active learning?

This may seem obvious but it is often ignored in practice. After video the effortful learning must be very closely related to video content. A large data mining exercise showed that this is not always the case and that when the two are too loosely related it affects student attainment MacHardy (2015).

What do you do after/between video chunks?

Don’t get them to draw stuff, get them to explain what they think they’ve learnt. You can do multiple choice but this is the weakest of the techniques. Retrieving key concepts is better Szpunar (2013), Roediger (2006), Vural (2013) and typing in actual explanations, that can be semantically interpreted by AI is even better.

Wildfire uses AI to grab the transcript of the video and produce retrieval-based online learning. It does it quickly and cheaply with links to external content, making the learner type in concepts, numbers or full sentences, which are interpreted by AI.

Conclusion

To produce effective videos for learning we must look at learning research. That not only tells us about the cognitive basis of good learning, it also gives us sound recommendations about what we need to do to increase retention, some of it surprising and counterintuitive. You have to break some of the rules of traditional video production and directing to adjust video towards learning. Oh... and rules are there to be sometimes broken... but only with good reason.

Bibliography

Sweller, J., 1988. Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), pp.257-285

Bjork, R.A., Dunlosky, J. and Kornell, N., 2013. Self-regulated learning: Beliefs, techniques, and illusions. Annual review of psychology, 64, pp.417-444.

Brame, C.J., 2016. Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), p.es6.

Guo PJ, Kim J, Robin R.  L@S’14 Proceedings of the First ACM Conference on Learning at Scale.New York: ACM; 2014. How video production affects student engagement: an empirical study of MOOC videos; pp. 41–50.

Risko, E.F., Anderson, N., Sarwal, A., Engelhardt, M. and Kingstone, A., 2012. Everyday attention: Variation in mind wandering and memory in a lecture. Applied Cognitive Psychology, 26(2), pp.234-242.

Zhang, D., Zhou, L., Briggs, R.O. and Nunamaker Jr, J.F., 2006. Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & management, 43(1), pp.15-27.

Mayer, R.E. and Moreno, R., 2003. Nine ways to reduce cognitive load in multimedia learning. Educational psychologist, 38(1), pp.43-52.

Chaohua, O, Joyner, D, Goel, A., 2019. Developing Videos for Online Learning: A 7-Principle Model. Online Learning

Mayer, R.E., 2008. Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American psychologist, 63(8), p.760.

Brame, C.J., 2016. Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), p.es6.

Fiorella, L., van Gog, T., Hoogerheide, V. and Mayer, R.E., 2017. It’s all a matter of perspective: Viewing first-person video modeling examples promotes learning of an assembly task. Journal of Educational Psychology, 109(5), p.653.

Reeves, B. and Nass, C.I., 1996. The media equation: How people treat computers, television, and new media like real people and places. Cambridge university press.

Fiorella, L., Stull, A.T., Kuhlmann, S. and Mayer, R.E., 2019. Fostering generative learning from video lessons: Benefits of instructor-generated drawings and learner-generated explanations. Journal of Educational Psychology.

MacHardy Z, Pardos ZA. Evaluating the relevance of educational videos using BKT and big data. In: Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M, editors. Proceedings of the 8th International Conference on Educational Data Mining, Madrid, Spain. 2015

Szpunar, K.K., Khan, N.Y. and Schacter, D.L., 2013. Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), pp.6313-6317.

Roediger III, H.L. and Karpicke, J.D., 2006. The power of testing memory: Basic research and implications for educational practice. Perspectives on psychological science, 1(3), pp.181-210.

Vural, O.F., 2013. The Impact of a Question-Embedded Video-based Learning Tool on E-learning. Educational Sciences: Theory and Practice, 13(2), pp.1315-1323.

OEB Berlin… AI, video, learning analytics, data, and schnapps!

Been going for many years… and like the vibe… fruity mixture of tech, academic, government from 73 countries. There's the added attraction of Berlin, a Christmas market right across the street and free drinks at the Marlene Bar!

Gave three talks, all on AI theme… AI and ethics (the overblown hysteria), Learning Analytics (how to and real examples), Video and AI (research and how to). A number of the sponsors were companies that use AI and it was a solid theme this year, rightly, as it has already changed why, what and how we learn.

As is usually the case at conferences I found the smaller presentations and conversations more useful than the keynotes. Great start with Julian Stodd, who was his usual articulate and incisive self. He talked about the weirdness of HR trying to ‘impose’ values and compliance training on people, attacking people’s sense of self and agency. But one phrase that really resonated with me was the ‘humility to listen’. There’s a lot of depth to those three words…

The opening keynotes were a trio of very different fruits. The Max Planc/MIT guy gave a solid talk, showed the Frey and Osborne report (2013), but got the date wrong – it wasn’t 2016 – this matters as it was a paper that predicted the 47% jobs at risk of automation over a decade in the US. We are 6 years in and there is pretty much full employment in the US. Toby Walsh eviscerated this report and talked at this conference two years ago – so we seemed to be going backwards. The Chinese guy was clearly giving a sales pitch but at least he had data and citations to back up his case. Audrey Watters gave her standard  ‘it’s largely agitprop, ideology and propaganda’ replete with soviet posters. Oddly she mentioned being jeered at a summit in Iceland. I was there - it was a very small audience and the first question she was asked (by a woman) was whether she was throwing the baby out with the bathwater, neither was there a 3D cat. She rightly showed some claims that were unsubstantiated but out of context and actually several are evidence-based but Audrey’s was so keen to show that everyone else was ideological that she missed the fact that hers was the most ideological talk of the three. But oh how academe clapped.

The keynotes on the second day (HE session) tackled the future of HE. Professor Shirley Alexander showed the shocking costs, debts and default rates of HE – it is basically out of control on costs. But her solution, literally on the next slide was a huge, spanking new building they’ve just erected and some writing feedback software. I was convinced by nether the erection nor software, which has been around for decades. Bryan Alexander is always up for some fun and opened his talk in a Death Metal voice. Had a great conversation with Bryan about AI afterwards and he did the futurist thing – 3D printing, drones etc…didn’t really see any scalable solutions that tackled the cost issue.

One feature of learning conferences is a general refusal to face up to political issues such as cost and inequality. It is assumed that education is an intrinsic good, no matter what the cost.  No reflection on WHY Brexit, Trump, Gilet Jaunes and other political upheavals are happening, only a firm belief that we keep on doing what we do, no matter the cost. This is myopic. Bryan Caplan tried with his keynote last year, with real evidence, but once again we seemed to have gone backwards. I had a ton of conversations in the bar, in restaurants and over coffees on these issues. A refreshingly straight talk with Mirjam Neelen was one of many.

I liked the practical sessions on learning analytics. It is complex subject but offers a way forward that builds on a platform of data that can be used to describe, analyse, predict and prescribe learning solutions. With smart software (AI), it frees us from the fairly static delivery of media, which online learning has done for over 30 years. Speaking with the wonderfully named Thor and Christian Glahn, we opened up the world of xAPI, LXPs, LRSs and adaptive learning. Here lies some real solutions to the problems posed by the keynotes. 

Sure there are ethical issues and I gave a session explaining that AI is not as good as you think and not as bad as you fear. We went through a menu of ethical issues: Existential, employment, bias, race, gender and transparency. Every man, woman and their dog is setting up and ethics and AI committee, pouring out recommendations and edicts, often based on a thin understanding of ethics and the technology. Many seem designed to give people an excuse to avoid it and do nothing.

Enjoyed Mathew Day’s session on the use of video which is uploaded to the International Space Station, which they use just before they do a task. That’s what I call cosmic, performance support. I was on just before him and showed the evidence in learning theory on why video on its own is rarely enough for deep learning, as well as key evidence on what makes a good learning video, much of it counterintuitive – POV, slower pace, edit points, not so much talking heads, maximum length, adding active learning and so on.

So many interesting chats with people I knew and met for the first time. What I did walk away with was a sense that people are waking up to the possibilities of AI in learning, especially for teaching, Henri Palmer of TUI gave a great case study, showing how one can deliver a large project, super-fast at a fraction of the cost using AI created online content. Great to hear that her team won a Gold Award for that project the night before in London. 

Final dinner in Lutter and Wegner, an old German restaurant was great. Harold did his pitch-perfect Ian Paisley impression at full volume with much clinking of glasses… wine and schnapps. When you’re sitting next to people from Norway, Poland, France, Belgium and Trinidad – you can’t go far wrong.

BIG thanks to Channa, Astrid, Rosa, Rebecca, Harold and the team for inviting me… open people who not only do a great job organising this event but are also open-minded enough to encourage critical thinking…

Learning - lessons from AI...

AI in one sense means doing what human do when they learn. The field is thick with references to ‘learning’, the most common being machine learning, deep learning and reinforcement learning.
Machine learning uses algorithms and statistical models and applies patterns and inferences to perform tasks, so that it learns from experience.
Deep learning is a machine learning technique that uses layered neural networks with data, supervised, semi-supervised or unsupervised, to perform tasks.
Reinforcement learning operates on maximising reward by exploring existing knowledge and exploring new knowledge. 

Digging deeper there is decision-tree learning, lazy and eager learning, supervised and unsupervised learning, incremental, feature, federated and ontology learning. The noun is used so frequently, in so many contexts, for so many techniques that it has become a general term for software that gets better as it proceeds. This is why it AI is a topic of immediate interest to those in the learning game. 

John McCarthy and Marvin Minsky convened a conference in 1956, whose aim was to 

“to proceed on the basis of the conjecture that every aspect of learning (my bold) or any other features of intelligence can in principle be so precisely described that a machine can be made to simulate it”. 

There were successes, like Arthur Samuel’s checkers software, but the promise was never realised and the first AI winter arrived in the 60s. The early 80s saw a resurgence of interest in expert systems and a second winter came. It was only when probability and statistics were literally introduced into the equations that deep learning gave us translation, speech recognition and image recognition. But AI, with exponential growth in processing power, data and devices can now deliver on some of those early promises.

We are now revisiting, and implementing that 1956 objective, with a focus on how AI can be used to accelerate learning. AI makes us rethink learning. It holds the possibility that we do not have to learn some old knowledge and skills, it may help us learn new knowledge and skills, even improve the process and speed of learning. What greater objective that AI helping us to learn; learn to deal with the consequences of this bountiful technology, learn to solve intractable problems, learn to control AI, if necessary. 

Stuart Russell, a major figure in AI, rightly claims in 'Human Compatible', that 

“With AI tutors, the potential of each child, no matter how poor can be realised. The cost per child would negligible and that child would live a far richer and more productive life.” I think he’s right. Even if he’s only part right, this is the right direction of travel.

Let's give Technology an -ology...

We see technology as a noun, not a discipline or subject. There is no -ology for techn-ology, stuck as it is somewhere between science and engineering. Yet this is an area of human endeavour that has shaped history, economics, sociology, psychology and philosophy.

The tendency is to see technology in mechanical, material terms, to be stuck in the old paradigm, much as in this vision of robot cleaners in 1899, when the artist tried to imagine the year 2000. What we actually got was an AI driven Roomba. We also see this in the many books about technology, such as Usler's The History of Mechanical Invention and Brian Arthur's The Nature of Technology, although the latter is far more sophisticated in seeing combinations of technology as the deep driver. The word technology comes from the Green Tekhne (art, craft) and logia (writings). We still see technology as ‘tech’ not ‘ology’.

Economics

In economics, from Adam Smith’s The Wealth of Nations and his earlier The Theory of Moral Sentiments, the role technology in economic development became obvious. For Smith, the division of labour accelerates technological innovation as processes and procedures are automated, resulting in lower levels of employment and higher profits. He warned us of the dangers, as “People of the same trade seldom meet together, even for merriment and diversion but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices.” This is almost oracle-like and can be applied directly to tax evading and rapacious tech companies. What Smith uncovered was the simple fact that technological progress is inevitable but what we do with it is politically optional.

Jump a century and Marx gives us a deeper critique but still sees technology as an object that diminishes labour and allows the exploitation of production by capitalists. In a fascinating Fragment on Machines (from his notebooks, the Grundrisse) he prophesises a knowledge economy, where social knowledge becomes a commodity. This transcends classical Marxism and predicts what actually happened with the internet and now AI. His exact words on the effects of technology were “general social knowledge has become a force of production… under the control of the general intellect”. This idea is elaborated in Paul Mason’s Postcapitalism and has been described as ‘Marx beyond Marx’, a third form of capitalism, described by Antonio Negri’s followers as “cognitive capitalism”. What constitutes ‘value’ in this new economy has changed. It is no longer physical but psychological transactions, attention, eyeballs, knowledge, analysis, prediction, prescription, minds.

On the mechanics of technological change, a seminal text is Schumpter's Theories of Economic Development, bwhere cycles of economic development are seen as being driven by innovative technology as their cause.Carla Perez in Technological Revolutions and Financial Capital expands on the idea to identify specific cycles of over-effusive investment, slumps, then a period of fruitful investment that results in significant improvements in productivity. In other words, we overestimate technology in the short-term, underestimate it in the long-term.

Sociology

Beyond the mechanics of economics, we have had a deep analysis of the sociology of technology by, among many others, Marshall McLuhan and Neil Postman. McLuhan gave us the phrases ‘medium is the message’ and ‘global village’ which have so much resonance that they almost tip over into cliché. He was both an analyst of media and technology but also a visionary, predicted the web, invented the word ‘surfing’ for casual fragmentary media browsing and although he was dealing with the media a decade before the internet, his ideas, endure, through works like The Gutenburg Galaxy: The Making of Typographic Man and Understanding Media: The Extensions of Man. Postman in Amusing Ourselves to Death and Technolpoly, laid the ground work for subsequent analysis of the effects on technology on society. They saw the dialectic between technology and minds as having complex personal and social dimensions and consequences. Media and messages, tools shaping our minds, the dangers of amusing ourselves to death, with the advent of the web and AI, these issues have become even more complex, with even more profound consequences.

Psychology

At a deeper, and more detailed level, the psychology of technology has been studied  in works like The Media Equation by Nass and Reeves. The cognitive change from passive to active media is explored in Cognitive Surplus by Clay Shirky and a slew of works on the cognitive interaction between technology and the mind. It is important that we continue to read the literature from cognitive science on the role technology can play in improving teaching and learning. Without this bedrock of science we will be forever stuck in the world of fads and bogus and outdated ideas, like learning styles, Myers-Briggs and IAT tests on unconscious bias.

Philosophy

At an abstract level, the philosophy of technology was also been addressed by Descartes, Leibniz and Hobbes, more recently by Turing and Searle, then Sartre in Being and Nothingness and Heidegger in The Question Concerning Technology, where he questions the instrumental view of technology and searches for a deeper understanding of a relationship that has become much more problematic. This relationship between mind and machine was expanded in a seminal paper The Extended Mind, by Clark and Chalmers in 1997, with the idea of extending mind and cognition into the technosphere. Thomas Malone in Superminds, also sees in technology the formation of a network of immense power. This idea of a single network has been tempered by Niall Fergusson in The Square and the Tower, a reinterpretation of history around the idea of networks, where horizontal agoras or squares have been build but also vertical, hierarchical networks of power that attempt to control these structures. He thinks that we need a balance between these types of networks. Daniel Dennett has taken an even more expansive view, in his synthesis of the mind, natural world and technology, within the context of evolution, in From Bacteria too Back and Back. 

On the back of this interest in the economic, sociology, psychology and philosophy of technology, moral philosophy (ethics) has come to the fore. Dennett sees technology as being ‘competent without comprehension’ and is more sanguine about the dangers than some others. Re-engineering Humanity by Frischmann and Selinger is one such text, a detailed analysis of the slippery-slope of technological creep that may undermine society without us even being aware of its influence. Stuart Russell, in Human Compatible, also sees the problem as one of control.

The reason I have attempted to uncover these lines of literature, is that those of us working in the field, I feel, need sometimes to take to the higher ground. Far too much debate takes place at the level of us versus them, ignoring the complexity and subtleties of the field. Too often we get simplistic futurism or contrarianism. I have only touched upon the rich seams of literature in each of these strands. If we weave them together we get a strong rope by which we can pull the subject up into a more respectable level and see techn-ology as an -ology in itself. 

This piece was inspired by Nigel Paine, who interviewed me on this very topic last week for Learning TV. Thanks Nigel.

Doug – a meditation - Things I've learnt from Doug Part II

I’ve never been one for meditation, mindfulness or mentors, never been one for fads… but you know what I've learnt from Doug… to be a little more contented...

I sometimes wake during the night and feel him curled up on the bed, warm asleep. Even better when I open my eyes in the morning and there’s Doug, looking down on me, waiting… you know the day will be all the better for having him around.

Doug, of course, loves a walk. I say a walk, it’s more like being pulled forward by a small locomotive. He springs along like a lamb, checks this, checks that, a judicious sniff here or there, no tree left unexamined, always ahead but often looks back to see that I’m still there. We’re a team, Doug and I. Occasionally he’ll walk behind me, usually mulling over some new scent but we had an ‘incident’ my son and I, when I heard a strange sound behind me, an odd bumping and scraping. Looking round we found that Doug has slipped his harness and was off for a sniff in the bushes. We were literally walking his empty lead and harness for about 30 yards. Lesson learnt.

On walks to Preston Park, we pass the Preston Park Tavern and he veers off, as if on dog autopilot, towards the door of the pub. This is no accident. You see, my sons and I have been taking him to the pub. He loves a flop on the warm, wooden floor, gets the occasional head rub from a punter and generally keeps an eye on what’s going on. But I think what he really loves is that warm, hoppy, beery smell. Good pubs need a dog. I don’t mean bars, I mean pubs. Doug likes pubs, not places with music, too many young people, fishbowls of gin, but pubs and punters. So… you know that old guy in the pub on his own… looking perfectly content… that’s me that is…

Then there’s The Flour Pot, a little café up at the Fiveways. We go there, sometimes, after our morning walk, for a coffee and a read of the paper… Doug will snuggle down on the velvety cushion next to me and just watch, head perfectly still, eyes checking out anyone who passes our spot, like a two little searchlights… they have a little jar of dog treats. He likes that.

Now there comes a point in the day, when I sit down and try to stop the tumble of thoughts in the washing machine that is my mind. As I get older I’ve even taken to the occasional nap in the afternoon. Doug has this covered. He loves a nap. And a nap is all the better for doing it together. It helps when Doug flops down on the sofa next to me, his body pushed up against me in a show of solidarity, even better when he places his head gently across my thigh and closes his eyes. That is a moment of pure joy. Sometimes I catch him dream; a quiver, little chew, a wee moan. It’s good to know that we both have dreams. The difference, I suspect, is that his always come true.

For my previous piece on Doug - see here