Podcasts - 20 reasons why we should be using more podcasts in learning

Even the Obamas are in on the podcast act, signing a deal with Spotify. Hardly surprising, as over 50% of Americans have now listened to a podcast, very much a medium for people of working age, with the listening figures dropping off in the 55+ age group. Reuters Journalism, Media and Technology trends highlights audio as a significant growth area and Spotify are investing $500 million in the medium.

I’m a podcast fan myself. Whether it is Talking Politics, where some of the best minds in political science discuss a contemporary political topic, or In Our Time, where history, philosophy, art and science is brought alive with a trio of academics. If I want an in-depth learning experience, this is often my medium of choice. For real depth I prefer text – books, papers, articles and blogs. For practical learning, video. For really practical learning – doing stuff and experience. But podcasts lie in that niche between long-form text and short-form video and  have their own special allure, as well as being so very convenient. So why use podcasts in learning? What type of content is suitable? How does one make one?

Content

Podcasts tend to be long form and content rich. They are, in a sense , the opposite of microlearning or the tendency to reduce things into small pieces. They also have different cognitive affordances from video, text or graphics. Video is great for ‘showing’ things such as drama, objects, places, processes and procedures with more of an emphasis on attitudinal or practical learning. Text may be better for semantic and symbolic knowledge, where the art of the wordsmith comes into play and subjects like maths. Graphics, of course can visualise data, show schemas; diagrams can illustrate what you want to teach and photographs give a sense of realism. Podcasts, however, tend to deal with more conceptual knowledge, where ideas and discussion matter. They seem better at allowing experts or leaders to explain more complex thoughts and issues, where genuine discussion or stories can reveal the learning, with deeper levels of reflection and different perspectives. Relying on spoken language alone often gives them a depth that other media don't carry.

Convenience

Many like to listen to podcasts when walking, running, in the gym, car or commuting. The sheer convenience of time shifting the experience, of using this dead time, in what Marc Augur called ‘non-places’, even if only to hold off boredom, is what makes podcasts a form of productive, mobile learning. I personally, like to listen while sitting down, with headphones, as I’m a note taker but many listen when they are doing other things. This convenience factor is a big plus.

Natural

Oral communication is more natural and feels more authentic than written text, as it has many of the human flavours of the speaker, such as tone, intonation, accent, emotion and emphasis. Technically, we are grammatical geniuses aged three, able to listen and understand complex language, without formal learning. This makes such content easy to access, especially for those with lower levels of reading literacy. It is, in this sense, a very natural form of learning. This more frictionless form of communication allows us to take deeper dives, through attentive listening (as opposed to hearing), making them potent learning experiences.

Eavesdropping

Listening to a podcast, especially with headphones, can be an intense, intimate and private affair. Many podcast fans report that sense of eavesdropping into an intimate conversation, you feel as though you get to know the people over time. There is a sense of focus and attention that the learner feels, as if one was part of the conversation, literally sitting there next to the participant(s). So in this process of eavesdropping, how many participants should one have in a podcast? 

Monologue

It is hard to hold full attention for long when it is a single podcaster. Imagine sitting in a plane, asking someone a question and they come back with a 40 minute reply. Although, as a fan of the comedian Bill Burr’s podcasts, it can be done. The difference is that Bill has decades of experience as a stand-up comic and can hold an audience’s attention.

Dialogue

A more popular format is the interview. Joe Rogan is a good example, with massive audiences - there are many others. He interviews an individual, drawing out their stories and anecdotes. The questions in an interview format act as breaks, chunking the content down into meaningful pieces, making them easier to learn. It sometimes feels as if it is you, as the learner is asking the questions, and in that sense, feels like a personal dialogue.

Discussion

Some of my favourite podcasts, the BBCs In Our Time and Talking Politics, often have three or four participants. Interestingly, they both have an anchor, Melvyn Bragg and David Runciman, who hold the discussion together and give it shape and direction. The advantage of this format is that it provides different angles on the same subject, sometimes different areas of expertise, even disagreements.

Episodes

Some of the most popular podcasts have been series, where they’ve built an audience over time. These segment the content and often have cliff hangers, to make you want to listen to the next one. In learning, of course, this has the advantage of splitting material over time, introducing spaced practice, by taking just a minute or so to recap on the previous episode and summarise that the end, to top and tail, improves retention.

Media rich is not mind rich

Mayer and others have, over decades, shown us, through pinpoint research with good controls, that rich media, used unwisely, can inhibit learning, as in learning 'less if often more'. This has much to do with the limitations of working memory but also with using up cognitive channels. Yet online learning seems to ignore that simple, popular, single-channel medium – the podcast. Podcasts have the advantage of low cognitive bandwidth and low costs, along with several other advantages in learning.

Construct

Audio has the advantage of taking up only one channel, the auditory channel, leaving the mind free to generate, through the imagination, your own interpretation, allowing the brain to integrate new knowledge with your prior existing knowledge. As working memory has a limit of 4 or so registers, which we can hold for around 20 seconds, keeping some free from imagery can, for some types of knowledge, be a powerful advantage, especially for conceptual content, as it gives your working memory some time to interpret, even manipulate ideas.

Take notes

Podcasts have one great advantage over video or text/graphics. For active learners, the simple fact that you don’t have to look at a screen allows you to take notes. Research shows that note taking can increase retention from 20-30%. In learning podcasts it is important that you recommend note taking, as you are hands and eye free, allowing more sophisticated notes in your own words.

Speed control

Many listen to podcasts at 1.5 times normal speed as they can still understand what is being said. We read faster than we listen and many find that they can still get the full meaning at speeds beyond that of spoken delivery. This variability of speed allows different learners to listen at different rates, giving learning, almost personalised advantages,

Content control

Another form of control is stop, back, forward and control over a visible timeline. Most find themselves doing a lot of this when using podcasts to learn, when you don’t understand something, want to reflect more, skip extraneous material, take more detailed notes and so on. This, again, allows the learner to process content at a much deeper level for retention.

Audio quality

Nass and Reeves, in research in their book, The Media Equation, showed that although one can get away with low fidelity images in video, this doesn’t work for audio. Poor quality audio has a significantly detrimental effect on learning, lowering retention. We have evolved to have visual systems that can adapt to twilight and distance. Our auditory systems are less forgiving, and expect high fidelity audio, as if delivered by a person speaking in front of you. Distance, volume, tinny sound, mechanical delivery, all diminish attention and learning. Experienced podcast producers will recommend either a studio or as quiet as possible an environment, with a good microphone to get best results. Some avoid table-top mikes and prefer lapel or head mounted.

Delivery

Reading content from a script can be a killer as delivery really does matter. Listeners want energy, passion and expert or academic gravitas. Humour often helps to punctuate, dwell, then move on. It is that sense of listening to an ‘expert’, also shown by Nass and Reeves to increase retention, that is so important in learning. Above all, podcasts seem to give authenticity to the ‘voice’ of the speaker. It must be and sound natural. Over-produced podcasts can often be counter-productive.

Preparation

A good podcast also needs god preparation. Make sure your technical set-up is clear. Then prep the participants. A structured script is useful, even if it just a series of agreed questions, along with advice on short answers to questions. Test the levels, make sure the atmosphere is relaxed to encourage good discussion.

Music

There’s an argument for having music as a lead-in, even leading out at the end, as it helps brands podcasts, especially if it is a series of episodes, but avoid laying down a music track behind the speakers – it just kills attention and retention.

While recording

‘Go again… this time shorter” if often good advice, editing out the longer version. Try to avoid recording over several session, as it is difficult to get the same levels and sound the second and third time around. And if you think you can simply drop a word into a sentence that may have been mispronounced or the wrong word, think again – this is notoriously difficult. For 'learning' podcasts, there is something to be said for more structure in the content and clear edit points for different learning objectives. There are also strong arguments for more recaps, summaries and repetition to increase retention.

AI generated podcasts

One can already generate speech from text with relative ease. This is passable but still a little ‘mechanical’. However, we are reaching a position where it will feel very natural, so automatic podcasts from text scripts will be quick and cheap to produce and one can change and update them by simply changing the text, without going back into a recording studio. We already do this in WildFire for short introductions to pieces of learning.

Transcription

Google is introducing real time transcription. This is a boon for note taking, as you can annotate, add your own words, summarise, mind-map, whatever. This is often difficult when you have to ‘watch’ a lecturer, PowerPoint or video. With WildFire we have also grabbed podcast transcripts, used AI to generate active online content to supplement the listening experience and solidify knowledge.

Conclusion

Before commissioning or producing podcasts, listen to a few. They’re everywhere on the web. But listen to those that are most popular. You will find all sorts of subjects, by all sorts of people and variations on formats. For learning, listen to some of the more serious podcasts, although there’s nothing wrong with lightening things up. I know of several companies who do ‘learning’ podcasts and have been on the end of quite a few. Given that it is a massively popular medium, cheap to produce, with significant advantages in terms of learning, why not give them a try?

Bibliography

Edison Report. (2019) https://www.edisonresearch.com/infinite-dial-2019/

Llinares D. (2018) Podcasting: New Aural Cultures and Digital Media 

Nass and Reeves. The Media Equation

Newman N. (2019) Reuters. Journalism, Media and Technology Trends and Predictions https://reutersinstitute.politics.ox.ac.uk/our-research/journalism-media-and-technology-trends-and-predictions-2019 
PS
For some interesting, and detailed research on podcasts, try Steve Rayson's blog. He has a strong learning background and is doing detailed research on who uses podcasts and why. Some of the ideas in this piece have come from his blog.

10 ways AI is used in video for learning – from deepfakes to personalisation

Video is the medium of the age and AI is the technology of the age. Combine the two and you have a potent mixture. I’ve been involved with both, working in a video production company, using video on all sorts of media, from interactive videotape machines, laserdiscs, compact discs, CDi to streaming, even making a feature film called The Killer Tongue (you really donl;t want to know). Believe me that last one lost me a ton of money. I now run an AI for learning company WildFire and am writing a book AI for Learning. I know these two worlds well. But how do they interact?

1. Edit

There are tools that allow you to edit video much faster and to higher quality. Different cameras shoot different colour balances – that can be fixed with AI. Same actor in different scenes with different skin tone, that can be fixed with facial recognition and skin tone matching - using AI. Need your music mixed down behind dialogue - use AI. AI is increasingly used to fix, augment and enhance moving images. 

2. Fake

Of course, easy editing with AI also means easy fakes. AI generated avatars as TV presenters have appeared reading the news using text to speech software. One can have Obama saying whatever you like from a voiceover artist mimicking his voice. Similarly with a fake teacher, won can deliver talking head content. Even more worrying is fake porn. Many famous actresses and actors have had their faces transposed to create ‘deepfake’ porn scenes. This mimics what is possible with fake homework, essays and text output using OpenAIs GTP-2 software, so dangerous that they decided not to release it. Just feed it a question and it produces an essay. 

3. Create

Beyond fakery lies the world of complete video creation. Alibaba’s Aliwood software uses AI to create 20 second product videos from a company’s existing stills, identifying and selecting key images, close ups and so on. The selected images are then edited together with AI and even change with musical shifts.  They say it increases online purchases by 2.6%. Some video creation software goes further and also adds a text to speech narration, with edits at appropriate points. Many pop videos and films have been made with AI tools that use AI tools such as Deep Dream for image creation along with style capture and flow tools. There’s even complete films made from AI created scripts. We already see services that create learning videos quickly and cheaply using the same methods.

4. Caption

Once you have created a video, AI can also add captions. This type of software can even pick up on dog barks and other sounds and is now standard on TV, YouTube, Facebook, even Android phones, increasing accessibility. It is also useful in noisy environments. Language learners also commonly report cationing as having benefits in self-directed, language learning. Although one must be careful here, as Mayer’s research shows that narration and text together have an inhibitory effect on learning. 

5. Transcribe

Speech to text is also useful in transcription, where a learner may want the actual transcription of a video as notes. Some tools, such as WildFire, take these transcriptions and use them to create online learning to supplement the video with effortful learning. The learner watches the video, which is good for attitudinal and affective learning, even process and procedures but poor on semantic knowledge and detail. Adding an online treatment of the transcript, created and assessed by AI, can provide that extra dimension to the learning experience.

6. Translate

One you have the transcribed text, translation is also possible. This has improved enormously, with reduced latency, from Google Translate to more sophisticated services. Google’s Translatotron promises to deliver speech-to-speech translation with an end-to-end translation model that can deliver accurate results with low latency. Advances like these will allow any video to be translated into multiple languages, allowing low-cost and quick global distribution of learning videos.

7. Filter

Ever thought why YouTube and other video services prevent porn and other undesirable material from appearing? AI filters that use image recognition to search and delete. Facebook claims that AI now identifies 96.8% of prohibited content. It is not that AI does the whole job here. Removing dick pics and beheadings relies on algorithms and image recognition but there’s also community flagging and real people sitting watching this stuff. AI is increasingly used to protect us from undesirable content.

8. Find

Want to know something or do something? Searching YouTube is increasingly the first option chosen by learners. YouTube is probably the most used learning platform on the planet. Yet we tend to forget that it is only functional with good search. AI search techniques are what gives YouTube its power. Note that YouTube search is different from Google search. Google uses authority, relevancy, site structure and organization; whereas YouTube, being in control of all its content, uses, growth in viewing, patterns in viewing, view time, peak view times, and social media features such as shares, comments, likes and repeat views. Search is what makes YouTube such a convenient learning tool.

9. Personalise

Video services such as YouTube, Vimeo and Netflix use AI to algorithmically present content. AI is the new UI and most video content is served up in this personalised fashion. Netflix famously handed out a £1 million prize for an algorithm and has since refined their approach. This is exactly what is happening in adaptive learning systems, where individual and aggregated data is used to personalise and optimise the learning experience for each individual, so that everyone is educated uniquely.

10. Analyse

Talking of Netflix, there is now a huge amount of data collected on global services that can inform future decision making. This can be data about when people cut out of a show, literally showing favourite characters and sub-plots., which can be used to inform future script writing. Data on stars, and genres can also be used to guide scripting and spend on original content. Similarly in learning, analytics around usage, cut outs and so on can inform decisions about the efficacy of the learning.

Conclusion

All of the above are and will affect the delivery of video in learning. Several are already de facto techniques. We can expect them all to develop in line with advances in AI, as well as learner demand. This is clearly an example of where the learning world has lots to learn and lots to gain from consumer services. Most of the above techniques are being built, honed and delivered on consumer platforms first then being used in a learning context.

How to turn video into deep learning

With video in learning one can feel as though one is learning, as the medium holds your attention but as you are hurtled forward, that knowledge disappears off the back. It’s like a shooting star; looks and feels great but the reality is that it burns up as it enters the atmosphere and rarely ever lands.

Video and learning

We have evolved to learn our first language, walk, recognise faces and so on. This primary knowledge was not learnt in the sense of being schooled or deliberately studied. It is embodied in our evolutionary past and evolved brains. Note that some of this learning is patently wrong. Our intuitive view of inertia, forces, astronomy, biology and many other things is intuitively wrong, which is why we, as a species, developed intellectual development, science, maths, literature and… education. This secondary knowledge is not easily learnt – it has to be deliberately learned and takes effort. This includes maths, medicine, the sciences and most forms of intellectual and even practical endeavour. That brings us to the issue of how we learn this stuff.

Working and LT memory

Let’s start with the basics. What you are conscious of, is what’s in working memory, limited in capacity to 2-4 elements of information at any time. We can literally only hold these conscious thoughts in memory for 20 or so seconds. So our minds move through a leaning experience with limited capacity and duration. This is true of all experience and with video it has some interesting consequences. 

We also have a long-term memory, which has no known limits in capacity or duration, although lifespan is its obvious limit. We can transfer thoughts from long-term meory back into working memory quickly and effortlessly. This is why ‘knowing’ matters. In maths, it is useful to automatically know your times table, to allow working memory to then manipulate recalled results more efficiently. We also use existing information to cope with and integrate novel information. The more you know the easier it is to learn new information. Old, stored, processed information renders working memory enormous through effortless recall from long-term memory.

All of this raises the question of how we can get video-based learning into long-term memory.

Episodic and semantic memory

There is also the distinction, in long-term memory, between episodic and semantic memory. Episodic memories are those experiences such as what you did last night, what you ate for dinner, recalling your experience at a concert. They are, in a sense, like recalling short video sequences (albeit reconstructed). Semantic memory is the recall of facts, numbers, rules and language. They are different types of memory processed in different ways and places by the brain.

When dealing with video in learning, it is important to know what you are targeting. Video appeals far more to episodic than semantic memory – the recall of scenes, events, procedures, places and people doing things.

Element interactivity

When learning meaningful information that is processed, for example in multiplication, you have 2-4 registers for the numbers being multiplied. The elements have to be manipulated within working memory and that adds extra load. Element interactivity is always extra load. Learning simply additions or subtractions have low element interactivity but multiplication is more difficult. Learning vocabulary has low element interactivity. Learning how to put the words together into meaningful sentences is more difficult.

In video, element interactivity, is very difficult, as the brain is coping with newly presented material and the pace is not under your control. This makes video a difficult medium for learning semantic information, as well as consolidation g learning through cognitive effort and deeper processing.

Video not sufficient

Quite simple, we engage in teaching, whether offline or online, to get things into long-term memory via working memory. You must take this learning theory into account when designing video content. When using video we tend to forget about working memory as a limitation and the absence of opportunity to move working memory experiences into long-term memory.  We also tend to shove in material that is more suited to other media, semantic content such as facts, figures and conceptual manipulations. So video is often too long, shows points too quickly and is packed with inappropriate content. 

We can recognise that video has some great learning affordances in that it can capture experiences that one may not be able to experience easily, for real – human interactions, processes, procedures, places and so on. Video can also enhance learning experiences, reveal the internal thoughts of people with voiceover and use techniques that compress, focus in and highlight points that need to be learnt. When done well, it can also have an emotional or affective impact making it good for attitudinal change. The good news is that video has had a century or so to develop a rich grammar of techniques designed to telescope, highlight and get points across. The range of techniques from talking heads to drama, with sophisticated editing techniques and the ability to play with time, people and place, makes it a potent and engaging medium.

The mistake is to see video as a learning medium in itself. Video is a great learning medium if it things are paced, reinforced but made greater if the learner has the opportunity to supplement the video experience with some effortful learning.

Illusion of learning

However, the danger is that, on its own, video can encourage the illusion of learning. This phenomenon was uncovered by Bjork and others, showing that learners are easily fooled into thinking that learning experiences have stuck, when they have actually decayed from memory, often within the first 20 minutes. 

Video plus…

How do we make sure that video learning experience is not lost and forgotten? The evidence is clear, the learner needs some effortful learning – they need to supplement their video learning experience with deeper learning that allows them to move that experience from short to long-term memory.

The first is repeated access to the video, so that second and third bites of the cherry are possible. Everything in the psychology of learning tells us that repeated access to content allows us to understand, process and embed learning for retention and later recall. While repeated watching helps consolidate the learning it is not enough and an inefficient, long-winded, learning strategy.

The second is to take notes. This increase retention significantly by up to 20-30% of done well as deeper processing comes into play as you write, generate your own words, draw diagrams and so on.

WildFire

The third, is far more effective and that is to engage in a form of deeper, effortful learning that involves retrieval and recall. We have built a tool, WildFire,that does exactly this.

How do you ensure that your learning is not lost and forgotten? Strangely enough it is by engaging in a learning experience that makes you recall what you think you’ve learnt. We grab the transcript of the video, put it into an AI engine that creates a supplementary learning experience, where you have to type in what you ‘think’ you know. This is both simple concepts, numbers but also open input sentences, where the AI also semantically interprets your answers. This powerful form of retrieval learning, not only gives you reinforcement through a a second bite of the cherry bit also consolidates the learning. Research has shown that recalling back into memory – literally looking away and thinking about what you know, is even more powerful than the original teaching experience or exposure. In addition, the AI creates links out to supplementary material (curates if you wish) to further consolidate memory through deeper thought and processing.

‘Machines Like Me’ by Ian McEwan – a flawed machinage a trois

Ian McEwan’s 'Machines Like Me' is a machinage a trois between Charlie, Miranda and Adam. Now Ian can pen a sentence and, at times, writes beautifully but this is a rather mechanical, predictable and, at times, flawed effort.

Robot Fallacy

The plot is remarkably similar to the 2015 threesome-with-a-robot movie Uncanny (also has an Adam) which is somewhat better than this novel. But the real problem is the Robot Fallacy – the idea that AI is all about robots – it’s not. AI, even robotics, is not all about creating interesting characters for second rate novels and films and is not on a quest to create anthropoid human robots as some sort of undefined companions. Art likes to think it is, as art needs characterisation and physical entities. AI is mostly bits not atoms, largely invisible and quite difficult to reveal, it is mostly online but that's difficult for authors and film makers. That’s why the film Her was also superior to this novel – it doesn’t fall into the idea that it’s all about physical robots. McEwan’s robot and plot limits any real depth of analysis as it’s stuck in the Mary Shelley Frankenstein myth, with Turing as the gratuitous Frankenstein. In fact, it is a simple retelling of that tale, yet another in a long line of dystopian views of technology. McEwan compounds the Robot Fallacy by making Adam appear, almost perfectly formed, from nowhere. In reality, AI is a long haul with tons of incremental trials and failures. Adam appears as if created by God. Then there’s the confusion of complexity with autonomy. Stephen Pinker and others have pointed out the muddle-headed nature of this line of thought in Enlightenment Now. It is easy to avoid autonomy in the engineering of such systems. It tries to introduce some pathos at the end but ultimately it’s an old tale not very well told.

Oddities and flaws

Putting that aside, there are some real oddities, even clangers, in McEwan’s text. The robot often washes the dishes by hand, as if we have invented a realistic human companion but not a dishwasher. In fact, dishwashers are around, as one pops up, oddly as an analogy, later in the book. The robot can’t drive yet (self-driving cars appeared but didn’t work because of a traffic jam!). Yet self-driving cars make an appearance later in the book.

Counterfactuals are tricky to handle as it makes suspension of disbelief that much harder and in this case it the entire edifice of losing the Falklands war and muddling up political events seems like artifice without any real justification. One counterfactual completely threw me. It’s one thing to counterfactually ‘extend’ Turing’s life, another to recalibrate someone’s birth date , taking it back a couple of decades, as in the appearance of Demis Hassabis (of Deepmind fame). Hassabis pops up as Turing’s brilliant young colleague in 1968, odd as he wasn’t born until 1976 (as stated on the final page)!

Then there’s an even odder insertion into the novel – Brexit. McEwan is a famous Leave campaigner and for no reason, other than pettifoggery, he drags the topic into the narrative. I have no idea why. It has no causality within the plot and no relevance to the story. It just comes across as an inconsequential and personal gripe.

The yarn has one other fatal flaw – the odd way the child in introduced into the story, via a manufactured incident in the park, a continuing thread in the story that is about believable as a chocolate robot. I’m not the first to spot the straight-up snobbery in his handling of this plot line  - working class people as hapless thugs.

To be fair there are some interesting ideas, such as the couple choosing personality settings for their robot in a weird form of parenting and this blurring of boundaries is the book’s strength. The robot shines through as being by far the most interesting character in the book, curiously philosophical, and there’s some exploration of loyalty, justice and self.

Conclusion

Did I learn anything about AI from this novel? Unfortunately not. In the end it’s a rather mechanical and, at times, petty work. It was difficult to hold suspension of disbelief, as so many points were unbelievable. McEwan seems to have lost his imaginative flair, along with his ability to surprise and transgress. His fictional progeny are more ciphers than people. In truth, AI is only software, and all of this angst around robots murdering us in our sleep is hyperbolic and doesn’t really tackle the main issues around automation and perhaps the good that come out of such technology.

The Geneva Learning Foundation is bringing AI-driven training to health workers in 90 countries

Wildfire is helping the Swiss non-profit tackle a wicked problem: while international organizations publish global guidelines, norms, and standards, they often lack an effective, scalable mechanism to support countries to turn these into action that leads to impact. What is required is low cost, quick conversion, high retention training.

So the Geneva Learning Foundation (GLF) has partnered with artificial intelligence (AI) learning pioneer Wildfire to pilot cutting edge learning technology with over 1,000 immunization professionals in 90 countries, many working at the district level. It is fascinating to see so much feedback come in from so many countries.

By using AI to automate the conversion of such guidelines into learning modules, as well as interpret open-response answers, Wildfire’s AI reduces the cost of training health workers to recall critical information that is needed in the field.. This retention is a key step, if global norms and standards are to translate into making a real impact in the health of people.

If the pilot is successful, Wildfire’s AI will be included in TGLF’s Scholar Approach, a state-of-the-art, evidence-based package of pedagogies to deliver high-quality, multi-lingual learning. This unique Approach has already been shown to not only enhance competencies but also to foster collaborative implementation of transformative projects that began as course work.

TGLF President Reda Sadki said: “The global community allocates considerable human and financial resources to training. This investment should go into pedagogical innovation to revolutionize health.”

As a Learning Innovation Partner to the Geneva learning Foundation, our aim is to improve the adoption and application of digital learning toward achievement of the Sustainable Development Goals (SDGs). Three learning modules based on the World Health Organization’s Global Routine Immunization Strategies and Practices (GRISP) guidelines are now available to pilot participants, including Alumni of the WHO Scholar Level 1 GRISP certification in routine immunization planning. 

Conclusion

World health needs strong guidelines and solid practices in the field. We are delighted to be delivering this training, using AI as a social good, deliverable on mobiles and in a way that is simple to use but results in real retention and recall.

Climate change: dematerialisation and online learning

The number of young adults with driving licences has fallen dramatically, so that over half of American 18-year-olds do not have a driving license. This is partly due to the internet and their alternative investment in mobiles, laptops and connectivity. This is good news. I have never, ever driven a car, having lived in cities such as Edinburgh, London and now Brighton. I’ve never really been stuck, in terms of getting anywhere. I walk, take trains or public transport more than most. This has meant I’ve habitually learnt on the move, largely in what Marc Auge calls ‘non-places’ – trains, planes, automobiles, buses, hotels, airports, stations. I’m never without a laptop, book or mobile device for learning. Whether it’s text, podcasts or video; m-learning has become my dominant form of informal learning. This has literally given me years of extra time to read, write and learn in the isolated and comfortable surroundings of buses, trains and planes. I actually look forward to travel, as I know I’ll be able to read and think, even write in peace. Being locked away, uninterrupted in a comfortable environment is exactly what I need in terms of attention and reflection. I calculate that over the last 35 years of not driving, I’ve given myself pretty much a couple of extra degrees. 

At the risk of sounding like a hobo, I also have only two pairs of shoes and a minimal amount of clothing. I never buy bottled water and have a lifelong principle – Occam’s Razor – use the minimal amount of entities to reach your given goal. 

Dematerialisation
More importantly, all my life I have worked in technology, which has delivered much to the world in terms of eradicating poverty, mindless labour, disease and hardship. Technology has dematerialised many activities. Mobile comms has replaced atoms with bits. Take music - we no longer have to listen on vinyl in paper sleeves (except for nostalgists) or unrecyclable compact discs, as most music is now streamed and literally has no substance.

Newspaper circulation has plummeted and my phone delivers an unlimited amount of knowledge and communications that, in the past, would have been infrastructure heavy and hugely wasteful. Paper production is a massive, global polluter on land, water and air. It is the third largest industrial, polluter in North America, the fifth biggest user of energy and uses more water per ton of product than any other industry and paper in landfill sites accounts for around 35% of all waste by weight. Recycling helps but even the deinking process produces pollutants. Paper production still uses chlorine and chlorine based chemicals and dioxins are an almost inevitable part of the paper production process. Water pollution is perhaps the worst, as pulp-mill, waste water is oxygen hungry and contains an array of harmful chemicals. Harmful gases and greenhouse gases are also emitted. On top of this the web has given us the sharing economy, where bikes, cars, rooms and so on can be reused and shared. It would seem as though we're nearing what Ausuble called 'Peak Stuff'. This is all good as the best type of energy saving is not using energy at all or at least minimising the effort and resources needed.

Online learning and climate change

I have spent the whole of my adult life delivering a green product – online learning – which stops the need to travel and reduces the need for carbon-intensive, physical infrastructure. This is a lesson, in particular, for Higher Education, where hte must be a move towards Blended Learning. The ridiculous Erasmus program at €28 billion paying to 'travel' is completely wrong-headed. Academic conferences wasteful.

More recently, we have built and delivered a large amount of online education around renewable technologies, targets, policies and solutions. Knowledge is power and with knowledge we have the power to solve this problem. That’s why this project was so important. We used AI to create online education content in minutes not months, from just a few basic documents. Most of the projects we’ve created in WildFire have been without face-to-face meetings and this was no exception. We plan, deliver and project manage online.

Additionally, on climate change, the power of online education is not only its green credentials but also its power to inform. Even active protesters show precious little awareness of what the Paris agreements were, how the technology works and what the science actually says. We need to move beyond the bombast to practical, pragmatic and informed solutions.

 

WindPower

First up was content on those huge triffid-like wind turbines. What do you call the thing that sits on top of the tower? (nacelle) What lies inside? (lots of things) What controls are there in terms of direction and so on? (Yaw, pitch and speed) What is the wind equation? (P=pav3 this explains exactly why wind speed is the key variable). We have a huge offshore wind turbine field just off Brighton and I’ll never see them in the same light.

Policies

It’s all very well demonstrating for zero emissions but this has to be achieved through practical policies. Decarbonising economies requires the adoption of the right policy levers and accelerating electrification is top of the list. Renewables are great but not enough, as rather than producing less we must minimise energy use per unit of economic output. Rather than ranting against the ‘man’ we must use technology and market based instruments. Without a change in mindset, this will be difficult, so it’s all hand to the political pump to get things moving.

Conclusion

Technology – wind turbines, solar, electric vehicles, battery technology, AI driven IOT and all sorts of future solutions will solve this problem but without cheaper, greener education and an awareness of what we have to do and why, this is unlikely to happen. One contribution is the rapid rise in online learning. This has already led to the disappearance of those large training centres I remember back on the day. Less people travel to train. It also means access to learning for anyone with online access.

Why ‘learning analytics’? Why ‘Learning Record Stores’?

There’s a ton of learning technologists saying their new strategy is data collection in 'learning record stores' and 'learning analytics'. On the whole, this is admirable but the danger is in spending this time and effort without asking ‘Why?’ Everyone’s talking about analytics but few are talking about the actual analysis to show how this will actually help increase the efficacy of the organisation. Some are switched on and know exactly what they want to explore and implement, others are like those that never throw anything out and just fill up their home with stuff – but not sure why. One problem is that people are shifting from first to sixth gear without doing much in-between. The industry has been stuck with SCORM for so long, along with a few pie charts and histograms, that it has not really developed the mindset or skills to make this analytics leap.

Decision making

In the end this is all about decision making. What decisions are you going to make on the back of insights from your data? Storing data off for future use may not be the best use of data. Perhaps the best use of data is dynamically, to create courses, provide feedback, adapt learning, text to speech for podcasts and so on. This is using AI in a precise fashion to solve specific learning problems. The least efficient use of data is storing it in huge pots, boiling it up and hoping that something, as yet undefined, emerges.

Visualisation
This is often mentioned and is necessary but visualisation, in itself, means little. One visualises data for a purpose - in order to make a decision. It is not a tinny in itself and often masquerades as doing something useful, when all it is actually doing is acting as a culture-de sac.
Correlations with business data
Learning departments need to align with the business and business outcomes. Looking for correlations between, say increases in sales and completed training, gives us a powerful rational for future strategies in learning. It need not be just sales. Whatever outcomes the organisation has in its strategy needs to be supported by learning and development. This may lift us out of the constraints of Kirkpatrick, cutting to the quick, which is business or organisational impact. We could at last free learning from the shackles of course delivery and deliver what the business really wants and that’s results.

Business diagnosis
Another model is to harvest data from training in a diagnostic fashion. My friend Chris Brannigan at Caspian Learning does this, using AI. You run sophisticated simulation training, use data analysis to identify insights, then make decisions to change things. To give a real example, they put the employees of a global bank through simulation training on loan risk analysis and found that the problems were not what they had imagined - handing out risky loans. In fact, in certain countries, they were rejecting ‘safe’ loans - being too risk averse. This deep insight into business process and skills weaknesses is invaluable. But you need to run sophisticated training, not clickthrough online learning. It has to expose weaknesses in actual performance.

Improve delivery
One can decide to let the data simply expose weaknesses in the training. This requires a very different mindset, where the whole point is to expose weaknesses in design and delivery. Is it too long? Do people actually remember what they need to know? Does it transfer? Again, much training will be found wanting. To be honest, I am somewhat doubtful about this. Most training is delivered without much in the way of critical analysis, so it is doubtful that this is going to happen any time soon.

Determine how people learn

One could look for learning insights into ‘how’ people learn. I’m even less convinced on this one. Recording what people just ‘do’ is not that revealing if they are clickthrough courses, without much cognitive effort. Just showing them video, animation, text and graphics, no matter how dazzling is almost irrelevant if they have learnt little. This is a classic GIGO problem (Garbage In, Garbage Out). 

Some imagine that insights are buried in there and that they will magically reveal themselves  - think again. If you want insights into how people actually learn, set some time aside and look at the existing research in cognitive science. You’d be far better looking at what the research actually says and redesigning your online learning around that science. Remember that these scientific findings have already gone through a process of controlled studies, with a methodology that statistically attempts to get clean data on specific variables. This is what science does – it’s more than a match for your own harvested data set. 

Data preparation
You may decide to just get good data and make it available to whoever wants to use it, a sort of open data approach to learning. But be careful. Almost all learning data is messy. It contains a ton of stuff that is just ‘messing about’ – window shopping, In addition to the paucity of data from most learning experiences, much of it is odd data structures,odd formats, encrypted, in different databases,old, even useless. Even if you do manage to get a useful clean data set, You have to go through the process of separating ‘Personal’ from ‘Observed’ (what you observe people actually doing), ‘derived’ making deductions from that data, ‘Analysed’ (applying analysis to the data). You may have to keep it ‘Anonymised’ and the privacy issues may be difficult to manage. Remember, you’ll need real expertise to pull this off and that is in very short supply.

To use AI/Machine learning
If you are serious about using AI and machine learning (they are not the same thing), then be prepared for some tough times. It is difficult to get things working from unstructured or structured data and you will need a really good training set, of substantial size, to even train your system. And that is just the start, as the data you will be using in implementation may be very different.

Recommendation engines
This is not easy. If you’ve read all of the above carefully, you’ll see how difficult it is to get a recommendation engine to work, on data that is less than reliable.  You may come to the decision that personal learning plans are actually best constructed using simpler software techniques from spreadsheet levels of data.

Conclusion
The danger is that people get so enamoured with data collection and learning analytics that they forget what they’re actually there to do. Large tech companies use big data, but this is BIG data, not the trivial data sets that learning produces, often on single courses or within single institutions.  In fact, Facebook is far more likely to use A/B testing than any fancy recommendations when deciding what content works best, where a series of quick adaptions could be tested with real users, but few have the bandwidth and skills to make this happen.

Why AI in healthcare is working but IBM failed. What we can learn from this in learning?

First up, this is not a hype piece that says ‘Look at this wonderful stuff in medicines where Doctors will soon be replaced by software… the same will happen to teachers’. The lessons to learn from AI in healthcare are that AI is useful but not in the way many thought.

The dream, pushed primarily by IBM, who saw Watson winning the game show Jeopardy as a marketing platform for what IBM CEO Virginia Rometty called their ‘Moonshot’, a suite of healthcare applications, was that AI would change healthcare forever. This all started in 2011, followed by billions in investment and acquisitions. That was the plan but, in practice, things turned out differently.

The mistake was to think that mining big data would produce insights and that this would be the wellspring for progress in everything from research to diagnosis. That didn’t happen and commercial products are few and far between. The data proved messy and difficult for NLP to use effectively. Trials in hospitals were disappointing. Diagnosis proved tricky. The Superdoctor dream of an all-round physician with access to way more knowledge and way more data than any human, that would trounce professionals in the field, has had to be rethought.

Roger Schank has been a relentless critic of IBMs misuse of the term AI. He is especially critical of their use of terms such as ‘cogntitive computing’, an area he pioneered. Roger knows just how difficult these problems are to crack and sees IBM as marketing lies. He has a point. Another is Robert Wachter, chair of the department of medicine at the University of California in his book The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age. 

So what went wrong? IBMs Oncology Expert Advisor product used NLP (Natural Language Processing) to summarise patient cases then search patient databases to recommend optimal treatments. The problem was that patient data is very messy. It is not, as one would imagine, precise readings from investigative tests, in practice, it is a ton of non-standard notes, written in jargon and shorthand. Strangely enough, Doctors through their messy processes and communication, proved to be remarkably resilient and useful. They could pick up on clues that IBM products failed to spot.

The Oncology Expert project was also criticised for not really being an AI project. In truth, many AI projects are actually a huge and often cobbled together mix of often quite traditional software and tricks, to get things to work. IBM has proved to be less of a leading edge AI company than Google, Microsoft or Amazon. They have a sell first, deliver later mentality. Rather than focus on some useful bullets aimed at clear and close targets, they hyped their moonshot. Unfortunately, it has yet to take off. 

However…

However, and this is an important however, is that successes with AI in healthcare have come in more specific healthcare domains and tasks. There’s a lot to cover in healthcare:

Image analysis

Pathology

Genetic analysis

Patient monitoring

Healthcare administration

Mental health

Surgery

Clinical decision making

In the interpretation of imagery, where data sets are visual, and biopsied confirmation is available, success is starting to flow - mammary scans, retina scans, pathology slides, X-rays and so on. This is good, classifiable data.

Beyond scanned images, pathology slides are largely examined by eye, but image recognition can do this much faster and will, in time, do this with more accuracy. 

One of IBMs rare successes has been their FDA-approved app, launched in 2018 (SUGAR IQ). This delivers personalized patient support for diabetes by monitoring glucose levels and giving recommendations on diet, lifestyle and medication. It is this narrow domain, clear input and defined, personalized outputs for patients, that marks it out as a success. It is here that the real leverage of AI can be applied – in personalized patient delivery.

Another success has been in genome analysis, which is becoming more common, a precise domain with exact data means that the input is clean. Watson for Genomics lists a patient’s genetic mutations and recommends treatments. This is another case of limited-domain input with sensible and measured outputs that can really help oncologists treat patients.

Another good domain is healthcare administration, often antiquated, inefficient and expensive. There are specific tasks within this area that can be tackled using AI, optimising schedules, robots delivering blood and medicines within hospitals, selecting drugs in pharmacies and so on.

In mental health, rather than depending on NLP techniques, such as sentiment analysis, to scan huge amounts of messaging or text data, simple chatbots, like Woebot, which delivers daily dose of personalised CBT therapy, are proving more promising.

Robot surgery has got a lot of hype but in practice it really only exists at the level of laser-eye surgery and hair transplants. These are narrowly defined processes, with not a lot of variation in their execution. 

Where AI has not yet been unsuccessful is in the complex area of Doctor’s diagnosis and clinical decision making. This has proved much more difficult to crack, as AIs need for clean data clashes with the real words of messy delivery.

So most of the low hanging fruit lies in support functions, helping Doctors and patients, not replacing Doctors.

So what can we learn from this story about AI for learning?

Lessons learnt

There are several lessons here:

avoid the allure of big data solutions

avoid data that is messy

look for very specific problems

look for well-defined domains

AI is rarely enough on its own

focus on learners not teachers

I have been critical of the emphasis, in learning (see my piece 'On average humans have one testicle'), on learning analytics, the idea that problems will be solved through access to big data and machine learning to give us insights that will lead to diagnosis of students at risk of dropout. This is largely baloney. The data is messy and the promises often ridiculous. Above all the data is small, so stick to a spreadsheet.

Way forward

Let’s not set off like a cartoon character running off the cliff, finding ourselves looking around in mid-air, then plummeting to earth. Let’s be careful with the hype and big data promises. Let us be honest about how messy our data is and how difficult it is to manage that data. Let us instead, look for clear problems with clear solutions that use areas of AI that we know work – text to speech, speech to text, image recognition, entity analysis, semantic analysis and NLP for dialogue. We’ve been using all of these in WildFire to deliver learning experiences that are firmly based on good cognitive science, while being automated by AI. High-retention learning experiences that are created in minutes not months.
Conclusion
I have no doubts about AI improving the delivery of healthcare. I also have no doubts about its ability to deliver in education and training. What is necessary is a realistic definition of problems and solutions. Let's not be distracted by the blue-sky moonshots and and focus on the grounded problems.

Can you name the three winners winner of the Nobel Prize for their work in climate change? How online learning is tackling climate change

Climate change is a challenge. Above all it is an educational challenge. It is not easy to get to grips of the complexities of the issue. I asked some teenagers last week if any could name the three winners winner of the Nobel Prize for their work in climate change. None could name any and no one even knew that it had been awarded. (Answers at end.)

I then asked about the Paris targets – again no actual knowledge. How about something practical, like the inner workings of a wind turbine or the power equation for wind energy? Nothing. To be honest, I had the sketchiest of answers myself. So it was a joy to be doing some online learning for a major European, renewable energy company.

We used WildFire to teach:

1. Wind turbines
2. European policy 

1. Wind turbinesWe see those mighty, white towers and big blades all the time but how do they work? Inside the ‘nacelle’ (no I didn’t know that was its name either) the casing at the top of the tower is a fascinating box of tricks – shafts, a gearbox, generators and controls for ‘yaw’ and ‘pitch’ (know what those are?). Then there’s the wind power equation. Once you understand this, you’ll realise why  the biggest variable in the mix is wind speed, as generated  power equals air density X blade area X wind speed cubed. That word ‘cubed’ really matters – it means low-wind, almost no energy; high-wind, tons of energy.

2.  European policyPolicy is action and it is good to know what Europe is doing and by when. There’s the decarbonisation policy, the Paris targets, electrification, targets in transport, construction and industry. You’d be surprised at the differences between nations across Europe and the scale of the problem is immense. Emissions, in particular, are a real challenge. There’s the near terms 2030 targets and the 2050 targets. On policies, you need to know about incentives, taxes, subsidies and all sorts of market dynamics.

How did we do this?
WildFire simply took the documents, sent to us as attachments by email, we fed them into WildFire and produced two kinds of course:

1.    Detailed knowledge

2.    Free text input

The first literally identifies the key components in a wind turbine, key concepts like yaw and pitch, the variables in the wind formula and so on. You read the content, in sensible chunks, then have to type in your answers, either specific or free text, which is then semantically analysed and feedback given.

In addition, for most concepts, the system automatically provides links out to further explanations. For example, if you don’t know what ‘torque’ is, a link pops up automatically and you get supplementary explanation (including an animation). This is all generated automatically.

Fast
We have literally had no face-to-face meetings on this project, as the client is in Europe. The content was super-quick to produce, at low cost. Above all, as the learner has to actually retrieve and write what they think they have learnt, as opposed to simply clicking on stuff, they have to get it into long term memory. This is high retrieval and retention learning, not clickthough content.

CurationThere is also the option to add curated content to the end of every small piece of learning using the curation tool. This allows individual teachers and trainers to customise things to their own ends.

ConclusionIt is great to deliver a project where a social good is the aim. Climate change has its challenges, one of which is understanding the available renewable technology, another the policies and targets. Many countries now see education as a key pillar in their climate change initiatives. This is spot on. But it takes effort. It is one thing to skip school to protest but this must be more than matched with good informed knowledge and debate around what it actually takes to change things. The climate is changing and this must be matched with cognitive change - that, in the end, is all we have to prevent catastrophe.

PS
2007 Nobel Prize to between the IPCC and Al Gore. The 2018 Nobel Prize went to William Nordhuas, for his work on the economic modelling of climate change.