Thaler & Sunstein - Nudge theory

As is sometimes the case, the catalyst for a theory is a popular book. Nudge theory hit both the popular and professional consciousness when Richard Thaler and Cass Sunstein published Nudge: Improving decisions about health, wealth, and happiness (2008). Thaler is a Nobel Prize-winning behavioral economist. Note that education or training is not in the title but they touch upon education and the principle or technique can and has been applied in learning. They call their approach libertarian paternalism, where people are gently nudged into doing what is best for them, through small nudges and steps which can lead to significant changes in behaviour. They have had enormous influence as behavioural economics and psychology influenced government policies.

Nudge theory 

Nudge theory reverses the idea that we tell people what to do or legislate that they should do something. Behavioural psychology and economics starts first with people; where they are, what they are doing and what they need to do to improve their health, finances, happiness and learning. They recommend thinking about problems in terms of ‘choice-architecture’ that changes attitudes, knowledge, skills and behaviour, with small nudges in the right direction, rather than large-scale programmes and training. 

Thaler & Sunstein start with the now famous example of an etched image of a fly in the urinals at Schiphol Airport, in Amsterdam, where spillage was reduced by 80%, playing to men’s playfulness in trying to hit the fly. Smaller plates were found to lead to less food waste. Turn rubbish bins into characters with big mouths.

Designing nudges

You must use choice-architecture to avoid the natural inertia that exists in behaviour. Drawing on the work of Kahneman and Tversky on anchoring, availability and representativeness, they give not only many examples but some guidance on how to design nudges. 

Some of their design recommendations are useful. Think in terms of defaults - assume that people will default and take the path of least resistance and not read the manual! Expect Error - to err is human, so always assume that users will make errors and mistakes. Map your digital architecture or choices onto useful outcomes for consumers. Simplify and structure complex choices. Also provide incentives such as showing the costs of electricity used, as you use it. Give feedback - tell learners they’re doing well or making mistakes and why.

Social and peer pressure

They also call on research by Asch (1995), where people answered questions correctly on their own but, defying the evidence of their own senses, got things hopelessly wrong they saw others give wildly wrong answers. People are therefore heavily influenced by others in decision making, through groupthink. This conformity rate seems to happen 20% - 40% of the time. Peer pressure, explored in brilliant detail by Judith Harris appears to be a problem. Sherif (1937) found that this group effect was stronger in small groups, especially when they had to make their results public. This has led to nudges both avoiding the peer pressure of inertia but also the efficient design of nudges that use social pressure, for example, in the UK Government’s EAST methodology. This is important in learning where social or peer pressure can be used to push individuals forward.

Learning nudges

We make poor decisions about our health, wealth and happiness. We also make poor decisions about teaching and learning. In education and training the course; hours, days, weeks, even years long, has been the dominant paradigm. You have to go somewhere at a certain time to be taught something. Yet subtle nudges that introduce learning into your routine, may help you sustain a learning experience, like learning a new language.

They point towards a high degree of ‘unrealistic optimism’ about learners’ self-perceived performance, something they also found in self-judgements among faculty. This unrealistic optimism is common in high stakes decision making, such as the success of marriage. People need to be nudged out of these misconceptions. Creative nudges have been used in advertising, marketing and public contexts by governments and organisations, nudges are now commonly used in online learning. Notifications in adaptive learning systems to propel learners forward. They can be used to deliver timely reminders, even content, based on personal and aggregated data to motivate learners without delivering courses and content. Clark (2020, 2021) and others recommend using techniques such as micro-videos to nudge managers towards using better management techniques or making people aware of security problems like phishing.

Critique

Some see Nudge theory as a new form of paternalism, even worse a manipulative technique that goes against the freedom of choice and agency that we should have in life and work. Rather than change laws and policies or demand that processes and procedures in organisations are changed, the theory is used as an ‘under the radar’ marketing tool to effect change. In the context of social media it has been criticised as being the driver towards polarisation, as people are nudged through algorithmic and marketing techniques, such as ‘likes’ to drift towards more extreme positions. The concept has also been seen as vague and ill-defined, as anything that is a stimulus to action and that what is new is not the idea but its role in popular behavioural psychology. They are openly libertarian and so on school choice they think that nudging parents to make good choices is the solution and the rest is a given. This ignores the many other possible ways of making schooling better and fairer. 

Influence

Nudge theory has been taken up by governments and organisations as part of change management programmes. The UK government has what is called a Nudge unit, which has developed its own EAST method. In learning it has been given life by the rise in LXP (Learning Experience Platform) technology, where notifications and nudges are pushed to learners, rather than courses.

'Nudge' by Thaler and Sunstein is the book that took a theory from behavioural psychology into a mainstream policy technique for many governments. It also found a place in learning, as a method of increasing the efficacy of learning.

Bibliography

Thaler, R.H. and Sunstein, C.R., 2008. Nudge: Improving decisions about health, wealth, and happiness.

Asch, S.E., 1995. Foundations of Conformity and Obedience. Psychological Dimensions of Organizational Behavior, p.267.

Sherif, M., 1937. An experimental approach to the study of attitudes. Sociometry, 1(1/2), pp.90-98.

Clark, D., 2020. Artificial Intelligence for Learning: How to Use AI to Support Employee Development. Kogan Page Publishers. 

Clark, D., 2021. Learning Experience Design: How to Create Effective Learning that Works. Kogan Page Publishers.

AI for Learning. So what is the book about?

This is, to my knowledge the first general book about how AI can be used for learning and by that I mean the whole gamut of education and training. It is not a technical book on AI. It is designed for the many people who teach, lecture, instruct or train, also those involved in the administration, delivery, even policy  around online learning, even the merely curious. It is essentially a practical book about using AI for learning, with real examples of real teaching and learning in real organizations with real learners.

AI changes everything. It changes how we work, shop, travel, entertain ourselves, socialize, deal with finance and healthcare. When online, AI mediates almost everything – Google, Google Scholar, YouTube, Facebook, Twitter, Instagram, TikTok, Amazon, Netflix. It would be bizarre to imagine that AI will have no role to play in learning – it already has. 

Both informally and formally, AI is now embedded in many of the tools real learners use for online learning – we search for knowledge using AI (Google, Google Scholar), we search for practical knowledge using AI (YouTube), Duolingo for languages, and CPD is becoming common on social media, almost all mediated by AI. It is everywhere, just largely invisible. This book is partly about the role of AI in informal learning but it is largely about its existing and potential role in formal learning – in schools, Universities and the workplace. AI changes the world, so it changes why we learn, what we learn and how we learn.

It looks at how smart AI can be, and is, used for both teaching and learning. For teachers it can reduce workload and complement what they do, helping them teach more effectively. For learners it can accelerate learning right across the learning journey from learning engagement, support, feedback, creation of content, curation, adaption, personalization and assessment, AI provides smart solutions to make people smarter. 

AI is an IDIOT SAVANT

So how did we get here? Well AI didn’t spring from nowhere. It has a 2500 year pedigree. What matters is where we are today - somewhere quite remarkable. AI is ‘the’ technology of the age. The most valuable tech companies in the world have AI as their core, strategic technology. As it lies behind much of what see online, it literally supports the global web, driving use through personalization. Surprisingly, AI does this as an IDIOT SAVANT, profoundly stupid compared to humans, nowhere near the capabilities of a real teacher, but profoundly smart on specific tasks. Curiously, it can provide wonderfully effective techniques , such as adaptive feedback, on a scale impossible by humans, but doesn’t ‘know’ anything. It is ‘competence without comprehension’ but competence gets us a long way!

AI and teachers

In the book we first look at AI from the teacher or trainer’s perspective, showing that it is not a replacement, but valuable aid, to teaching. Robot teachers are beside the point, a bit like having robot drivers in self-driving cars. The dialectic between AI and teaching shows that there will be a synthesis and increased efficacy in teaching when its benefits are realized. Similarly for learners. AI is not a threat, it is a powerful teaching and learning tool.

AI is the new UI

AI underlies most interfaces online by mediating what you actually see on the screen. More recently it has provided voice interfaces, both text to speech and speech to text. This is important in learning, as most teaching is, in practice, delivered by voice. Then there is the wonderful world of chatbots, the return of the Socratic method, with real success in engagement, support and learning. There’s lots of real examples of how these new interfaces and, in particular, dialogue will expand online learning.

AI creates content

A surprising development has been the use of AI to create of online content. Tools like WildFire have been creating online content in minutes not months with high-retention learning – using AI to semantically interpret answers and get away from the traditional MCQs. AI can also enhance video, which suffers from being a transitory medium in terms of memory like a shooting star leaving a trail of forgetting behind it, towards powerful, high-retention learning experiences. New adaptive learning platforms are proving to be powerful, personalizing learning on scale , delivering entire degrees. AI pushes organisations towards being serious learning organisations by producing and using data to improve performance, not only of the AI systems themselves but also teachers and learners. Models such as GTP-3 are producing content that is indistinguishable, when tested, from human output. This shows that there is far more to AI than at first meets the AI!

AI and learning analytics

Learning is not an event, it is a process. Data describes, analyses, predicts and can prescribe process. Data types, the need for cleaning data, the practical issues around its use in learning and its use in learning analytics along with personalized and adaptive learning shows how AI can educate and train everyone uniquely. Data-driven approaches can also deliver push techniques, such as nudge learning and spaced-practice, embodying potent pedagogic practice. New ecosystems of learning such as Learning eXperience Platforms and Learning Record Stores move us towards more dynamic forms of teaching and learning. Sentiment analysis, using AI to interpret subjective emotions in learning is also covered. AI in this sense, is the rocket with data as its fuel. We explore how you can move towards a more data-driven approach to learning in the book.

AI in assessment

Then there’s assessment, which is being made easier and enhanced by AI. From student identification to the delivery of assessments and forms of assessment, AI promises to free assessment from the costs and restraints of the traditional exam hall. Plagiarism checking is also discussed, as is the semantic analysis of open input in assessment and essay marking.

What next for AI in learning?

Well, there will be a significant shift in the skills needed to use AI in learning away from the traditional ‘media production’ mode and these new skills are explained in detail. More seriously, you can’t have a book on AI for learning without tacking ‘ethics’ and so bias, transparency, race, gender and dehumanisation are all examined. The good news is that AI is not as good as many ethicists think it is and not as bad as you fear. On employment, we look at something few have looked at; the effect of AI on the employment of learning professionals.

AI: the Final Frontier

Finally there a cheeky look at the final frontier. What next? There technology on how AI may accelerate learning through non-immersive and immersive, brain-based technology, as well as speculation on how this may all pan out in the future. It is literally mind-blowing.

Finally…

In these times of pandemic, we have all had to adapt to online learning; teachers, learners and parents. Necessity has become the mother of invention and this book offers a look at the future, where AI technology will provide the sophistication we need to make online learning smart, responsive and up to the the future challenge of a changing world. AI is here, its use is irreversible and its role in learning inevitable. I hope the book answers any questions you may have on AI in learning, more importantly, I hope it inspires you to think about how you may use it in your organization.

Year of learning dangerously – my 15 highs and lows of 2018

So 2018 is behind us. I look back and think… what really happened, what changed? I did a ton of talks over the year in many countries to different types of audiences, teachers, trainers, academics, investors and CEOs. I wrote 65 blogs and a huge number of Tweets and Facebook posts. Also ran an AI business, WildFire, delivering online learning content and we ended the year nicely by winning a major Award. 

So this is not a year end summary nor a forecast for 2019. It’s just a recap on some of the weirder things that happened to me in the world of ‘learning’…

1. Agile, AI-driven, free text learning

As good a term as I can come up with for what I spent most of my year doing and writing about, mostly on the back of AI, and real projects delivered to real clients of AI-generated award winning content, superfast production times and a new tool in WildFire that gets learners to use free-text, where we use AI (semantic analysis) as part of the learning experience. Our initial work shows that this gives huge increases in retention. That is the thing I’m most proud of this year.

2. Video is not enough

Another breakthrough was a WildFire tool that takes any learning video and turns it into a deeper learning experience by taking the transcript and applying AI, not only to create strong online learning but also use the techniques developed above to massively increase retention. Video is rarely enough on its own. It's great at attitudinal learning, processes, procedures and for things that require context and movement. But is it poor at detail and semantic knowledge and has relatively poor retention. This led to working with a video learning company to do just that, as 2+2 = 5.

3. Research matters

I have never been more aware of the lack of awareness on research on learning and online learning than I was this year. At several conferences across the year I saw keynote speakers literally show and state falsehoods that a moments searching on Google would have corrected. These were a mixture of futurists, purveyors of ‘c’ words like creativity and critical thinking and the usual snakeoil merchants. What I did enjoy was giving a talk at the E-learning network on this very topic, where I put forward the idea that interactive design skills will have to change in the face of new AI tech. Until we realise that a body of solid research around effortful learning, illusory learning (learners don’t actually know how they learn or how they should learn), interleaving, desirable difficulties, spaced practice, chunking and so on… we’ll be forever stuck in click-through online learning, where we simply skate across the surface. It led me to realise that almost everything we've done in online learning may now be dated and wrong.

4. Hyperbolic discounting and nudge learning

Learning is hard and suffers from its consequences lying to far in the future for learners to care. Hyperbolic discounting explains why learning is so inefficient but also kicks us into realising that we need to counter it with some neat techniques, such as nudge learning. I saw a great presentation on this in Scotland, where I spoke at the excellent Talent Gathering.

5. Blocked by Tom Peters

The year started all so innocently. I tweeted a link to an article I wrote many moons ago about Leadership and got the usual blowback from those making money from, you guessed it, Leadership workshops.. one of whom praised In Search of Excellence. So I wrote another piece showing that this and another book Good to great, turned out to be false prophets, as much of what they said turned out to be wrong and the many of the companies they heralded as exemplars went bust. More than this I thought that the whole ‘Leadership’ industry in HR had le, eventually to the madness of Our Great Leader, and my namesake, Donald Trump. In any case Tom Peters of all people came back at me and after a little rational tussle – he blocked me. This was one of my favourite achievements of the year.

6. Chatting about chatbots

Did a lot of talks on chatbots this year, after being involved with Otto at Learning Pool (great to see them winning Company of the Year at the Learning technologies Awards), building one of my own in WildFire and playing around with many others, like Woebot. They’re coming of age and have many uses in learning. And bots like Google’s Duplex, are glimpses into an interesting future based on more dialogue than didactic learning. My tack was that they are a natural and frictionless form of learning. We’re still coming to terms with their possibilities.

7. Why I fell out of love with Blockchain

I wrote about blockchain, I got re-married on Blockchain, I gave talks on Blockchain, I read a lot about Blockchain… then I spoke at an event of business CEOs where I saw a whole series of presentations by Blockchain companies and realised that it was largely vapourware, especially in education. Basically, I fell out of love with Blockchain. What no one was explaining were the downsides, that Blockchain had become a bit of a ball and chain.

8. And badges…

It’s OK to change your mind on things and in its wake I also had second thoughts on the whole ‘badges’ thing. This was a good idea that failed to stick, and the movement had run its course. I outlined the reasons for its failure here.

9. Unconscious bias my ass

The most disappointing episode of the year was the faddish rush towards this nonsense. What on earth gave HR the right to think that they could probe my unconscious with courses on ‘unconscious bias’. Of course, they can’t and the tools they’re using are a disgrace. This is all part of the rush towards HR defending organisations AGAINST their own employees. Oh, and by the way, those ‘wellness’ programmes at work – they also turned out to be waste of time and money.

10. Automated my home

It all started with Alexa. Over the months I’ve used it as a hub for timers (meals in oven, Skype calls, deadline), then for music (Amazon music), then the lights, and finally the TV. In the kitchen we have a neat little robot that emerges on a regular basis to clean the ground floor of our house. It does its thing and goes back to plug itself in and have a good sleep. We also have a 3D printer which we’re using to make a 3D drone… that brings me to another techy topic – drones.

11. Drones

I love a bit of niche tech and got really interested in this topic (big thanks to Rebecca, Rosa and Veronique) who allowed me to attend the brilliant E-learning Africa and see Zipline and another drone company in Rwanda (where I was bitch-slapped by a Gorilla but that, as they say, is another story). On my return I spoke about Drones for Good at the wonderful Battle of Ideas in London (listen here). My argument, outlined here, was that drones are not really about delivering pizzas and flying taxis, as that will be regulated out in the developed world. However, they will fly in the developing world. Then along came the Gatwick incident….

12. Graduation

So I donned the Professorial Gown, soft Luther-like hat and was delighted to attend the graduation of hundreds of online students at the University of Derby, with my friends Julie Stone and Paul Bacsich. At the same time I helped get Bryan Caplan across from the US to speak at Online Educa, where he explained why HE is in some trouble (mostly signalling and credential inflation) and that online was part of the answer. 

13. Learning is not a circus and teachers are not clowns

The year ended with a rather odd debate at Online Educa in Berlin, around the motion that “All learning should be fun”. Now I’m as up for a laugh as the next person. And to be fair, Elliot Masie’s defence of the proposition was laughable. Learning can be fun but that’s not really the point. Learning needs effort. Just making things ‘fun’ has led to the sad sight of clickthrough online learning. It was the prefect example of experts who knew the research, versus, deluded sellers of mirth.

14. AI

I spent a lot of time on this in 2018 and plan to spend even more time in 2019. Why? Beneath all the superficial talk about Learning Experiences and whatever fads come through… beneath it allies technology that is smart and has already changed the world forever. AI has and will change the very nature of work. It will, therefore change why we learn, what we learn and how we learn. I ended my year by winning a Learning technologies award with TUI (thanks Henri and Nic) and and WildFire. We did something ground breaking – produced useful learning experiences, in record time, using AI, for a company that showed real impact.

15. Book deal

Oh and got a nice book deal on AI – so head down in 2019.

AI for Learning. So what's this book about?

 So what is the book about?

This is, to my knowledge the first general book about how AI can be used for learning and by that I mean the whole gamut of education and training. It is not a technical book on AI. It is designed for the many people who teach, lecture, instruct or train, also those involved in the administration, delivery, even policy  around online learning, even the merely curious. It is essentially a practical book about using AI for learning, with real examples of real teaching and learning in real organizations with real learners.

AI changes everything. It changes how we work, shop, travel, entertain ourselves, socialize, deal with finance and healthcare. When online, AI mediates almost everything – Google, Google Scholar, YouTube, Facebook, Twitter, Instagram, TikTok, Amazon, Netflix. It would be bizarre to imagine that AI will have no role to play in learning – it already has. 

Both informally and formally, AI is now embedded in many of the tools real learners use for online learning – we search for knowledge using AI (Google, Google Scholar), we search for practical knowledge using AI (YouTube), Duolingo for languages, and CPD is becoming common on social media, almost all mediated by AI. It is everywhere, just largely invisible. This book is partly about the role of AI in informal learning but it is largely about its existing and potential role in formal learning – in schools, Universities and the workplace. AI changes the world, so it changes why we learn, what we learn and how we learn.

It looks at how smart AI can be, and is, used for both teaching and learning. For teachers it can reduce workload and complement what they do, helping them teach more effectively. For learners it can accelerate learning right across the learning journey from learning engagement, support, feedback, creation of content, curation, adaption, personalization and assessment, AI provides smart solutions to make people smarter. 

AI is an IDIOT SAVANT

So how did we get here? Well AI didn’t spring from nowhere. It has a 2500 year pedigree. What matters is where we are today - somewhere quite remarkable. AI is ‘the’ technology of the age. The most valuable tech companies in the world have AI as their core, strategic technology. As it lies behind much of what see online, it literally supports the global web, driving use through personalization. Surprisingly, AI does this as an IDIOT SAVANT, profoundly stupid compared to humans, nowhere near the capabilities of a real teacher, but profoundly smart on specific tasks. Curiously, it can provide wonderfully effective techniques , such as adaptive feedback, on a scale impossible by humans, but doesn’t ‘know’ anything. It is ‘competence without comprehension’ but competence gets us a long way!

AI and teachers

In the book we first look at AI from the teacher or trainer’s perspective, showing that it is not a replacement, but valuable aid, to teaching. Robot teachers are beside the point, a bit like having robot drivers in self-driving cars. The dialectic between AI and teaching shows that there will be a synthesis and increased efficacy in teaching when its benefits are realized. Similarly for learners. AI is not a threat, it is a powerful teaching and learning tool.

AI is the new UI

AI underlies most interfaces online by mediating what you actually see on the screen. More recently it has provided voice interfaces, both text to speech and speech to text. This is important in learning, as most teaching is, in practice, delivered by voice. Then there is the wonderful world of chatbots, the return of the Socratic method, with real success in engagement, support and learning. There’s lots of real examples of how these new interfaces and, in particular, dialogue will expand online learning.

AI creates content

A surprising development has been the use of AI to create of online content. Tools like WildFire have been creating online content in minutes not months with high-retention learning – using AI to semantically interpret answers and get away from the traditional MCQs. AI can also enhance video, which suffers from being a transitory medium in terms of memory like a shooting star leaving a trail of forgetting behind it, towards powerful, high-retention learning experiences. New adaptive learning platforms are proving to be powerful, personalizing learning on scale , delivering entire degrees. AI pushes organisations towards being serious learning organisations by producing and using data to improve performance, not only of the AI systems themselves but also teachers and learners. Models such as GTP-3 are producing content that is indistinguishable, when tested, from human output. This shows that there is far more to AI than at first meets the AI!

AI and learning analytics

Learning is not an event, it is a process. Data describes, analyses, predicts and can prescribe process. Data types, the need for cleaning data, the practical issues around its use in learning and its use in learning analytics along with personalized and adaptive learning shows how AI can educate and train everyone uniquely. Data-driven approaches can also deliver push techniques, such as nudge learning and spaced-practice, embodying potent pedagogic practice. New ecosystems of learning such as Learning eXperience Platforms and Learning Record Stores move us towards more dynamic forms of teaching and learning. Sentiment analysis, using AI to interpret subjective emotions in learning is also covered. AI in this sense, is the rocket with data as its fuel. We explore how you can move towards a more data-driven approach to learning in the book.

AI in assessment

Then there’s assessment, which is being made easier and enhanced by AI. From student identification to the delivery of assessments and forms of assessment, AI promises to free assessment from the costs and restraints of the traditional exam hall. Plagiarism checking is also discussed, as is the semantic analysis of open input in assessment and essay marking.

What next for AI in learning?

Well, there will be a significant shift in the skills needed to use AI in learning away from the traditional ‘media production’ mode and these new skills are explained in detail. More seriously, you can’t have a book on AI for learning without tacking ‘ethics’ and so bias, transparency, race, gender and dehumanisation are all examined. The good news is that AI is not as good as many ethicists think it is and not as bad as you fear. On employment, we look at something few have looked at; the effect of AI on the employment of learning professionals.

AI: the Final Frontier

Finally there a cheeky look at the final frontier. What next? There technology on how AI may accelerate learning through non-immersive and immersive, brain-based technology, as well as speculation on how this may all pan out in the future. It is literally mind-blowing.

Finally…

In these times of pandemic, we have all had to adapt to online learning; teachers, learners and parents. Necessity has become the mother of invention and this book offers a look at the future, where AI technology will provide the sophistication we need to make online learning smart, responsive and up to the the future challenge of a changing world. AI is here, its use is irreversible and its role in learning inevitable. I hope the book answers any questions you may have on AI in learning, more importantly, I hope it inspires you to think about how you may use it in your organization.

Use code AHR20 here to get 20% discount and free delivery in UK and US.

 

Nudge learning

Things move fast in organisations and when Standard Life merged with Aberdeen Asset Management, an agile learning approach to changing behaviour in the new organisation was implemented, a training intervention that is itself agile and resulted in actual behavioural change. A huge traditional course, whether face-to-face or online, based on a diet of knowledge would have been counterproductive in this fast moving, post-merger commercial environment and be seen as a bit old-school and non-agile. Whereas a series of short, sharp interventions that nudge people into applying agile in their own context and work environment was likely to work better. At least, that's what Peter Yarrow, Head of Learning, thought – and I think he's right. It was his brainchild.

His successful project used the 'nudge' technique. Nudge theory recommends small interventions to push people into changing behaviour. Famous examples include the image of a fly in men’s urinals, to improve aim and reduce cleaning costs! Opting out, rather than into organ donation is another. The psychological theory is laid out in the book 'Nudge: Improving Decisions About Health, Wealth and Happiness’ by Thaler and Sunstein. They could well have added ‘Learning’ to the title.

Nudge solution

In learning, Standard Life Aberdeen sent small, professionally shot videos (on average 1min 30secs long), mainly talking heads from leaders and experts in the organisation, out via email. In addition to the video, there was a ‘challenge’ to apply the lesson in their own working environment. I like this approach, and it is a truly fresh and agile 'nudge'intervention.In their case it was general management techniques but I feel that agile techniques could be applied in response to all sorts of needs. Each starts with a proposition, or problem, followed by a suggested solution and finally, and crucially, a call to action. This is based on techniques also used in web and online design.

Example 1

Video on importance of comms

It’s hard to be a high performing team if colleagues don’t know each other well. Without trust, mutual respect and goodwill, performance will most likely remain middle of the road. Exceptional performance is fuelled by positive working relationships. Take this week’s challenge to get to know your colleagues better.

Example 2

Video on mentoring

Being mentored is a great way to develop and progress. But how do you get started? Begin by identifying someone you trust who has taken a career path you aspire to. Take this week’s challenge to learn more about making mentoring relation ships work.

These videos and challenges were sent out by email and usage tracked. The take-up across the organisation surprised the training department and the feedback was very positive. People felt that it was integrated into their natural workflow (they were not too long and intrusive) and that it was made more relevant by virtue of nudging people towards action by them as individuals in their specific job.

Suggestions for nudge learning

Great start but rather than batch emails, I'd use an algorithm to decide personal needs and, take data from usage and get more precise in timing and targeting. This means harvesting more data, which one can do, even with internal email systems. People get habituated out of responding if they get too many emails.

On the challenges I’d use more of a pure marketing approach, a strong command verb at the start, really concise, with reason and emotional pull. Give your audience a reason why they should take the desired action, maybe a bit of FOMO (Fear Of Missing Out), maybe some compelling numbers. Writing calls to action is both a science and an art and it’s worth being a little creative.

I'd also do slightly more than just state the challenge. I'd get the user to do something there and then to make sure they got the main points in the video (we've done this by grabbing the transcript and getting the user to check they've understood the main points) using AI generated, open-input experiences with WildFire.

None of this is a criticism of Peter’s pioneering project, merely suggestions to make it more potent.

Conclusion

I really liked Peter’s fresh thinking around the ‘nudge’ thing. It has legs and could go in all sorts of directions. It is the combination of proven marketing techniques with learning that make this approach fly. Few in marketing want to slab out hours and hours of content – they think first audience, second channels and third action. Their whole way of thinking is around ‘less is more’. This also happens to be exactly what the psychology of learning tells us about learning experiences. The limits of working memory, cognitive overload, forgetting and the need for transfer mean doing less but doing it better. 

Nudges and learning

Nudge, nudge

'Nudge' by Thaler and Sunstein is the book that in every policy maker’s, combination-lock briefcase this summer. It’s another ‘concept’ book, which is basically an innocuous word masquerading as a serious idea.

But there are several problems with the book:

1. The basic concept is too vague and covers too many cases to be taken entirely seriously. TV ads, slogans, pictures, policy tweaks – you name it, it can be called a nudge. It’s a jack of all trades term.

2. It is hopelessly US-centric. They literally talk about the American Dream (which has just turned into a nightmare) as if it were the premise behind all human behavior. They really do distrust government and have unbridled trust in business (hope they’re watching TV this week). Their whole treatise is framed in a Democrats v Republican frame (say no more). It’s libertarian capitalism at its worst.

3. They are really lawyers masquerading as psychologists. They drag out a couple of old Asch studies but largely ignore the bulk of 20^th century social psychology, depending on anecdote and examples.

4. By recommending ‘nudges’ as a panacea, they simply put policy making into the marketing sphere. The bad news is that the private sector will market you out of existence. Take smoking. The only way to stop those crooks from killing our children is to make the laws tougher.

Nudges are actually interesting

To be fair, nudges is a nice little word, and some of their examples are quite catching.

Example 1: place the image of a fly in airport urinals to reduce spillage (I can confirm that this works as the cleanest urinals in Brighton are in Zilli’s restaurant)

Example 2: cash feedback loops on utility and petrol consumption

Where the book scores is in giving a complex set of techniques a simple name. It forces you into thinking about how to change behaviour without automatically defaulting into compulsion.

Nudges and learning

What are useful are the lessons to be learnt about the marketing of learning and e-learning to learners. The book does have some useful ideas that could be taken across into the learning world. Here’s my top ten starter list:

1. Language nudges

Learning professionals should use appropriate language and scrap training, learning styles, competences, objectives, homework and so on.

2. Feedback nudges

Focus on regular formative and not end-point feedback. Learning is about correcting errors, see Beyond the Black Box.

3. Email nudges

Email nudges like no other form of communication, yet little actual learning is delivered or prompted by this means.

4. YouTube nudges

Use YouTube nudges to virally spread learning. For example, this brilliant PowerPoint tutorial – hilarious and succinct.

5. Book nudges

Encourage the purchase of books, give everyone an Amazon account and budget, and get one into your bag for the train or plane.

6. Note nudges

Branson has a notebook on him at all times. Memory is fallible and note taking dramatically increases learning. Take notes every day.

7. Audio nudges

Podcasts, audio books, recording lectures. A still, vastly underused form of nudge learning.

8. Doing nudges

Buy Getting Things Done by Allen. It’s full of nudges around getting things done, on the premise that you leave nothing hanging in the air. Brilliant book.

9. Feed nudges

Get a personalized home page with feeds from your favourite learning sources and start using RSS.

10. Blog nudges

Get blogging. You’ll learn loads by habitually writing things down.

Why learning professionals – managers, project managers, interactive designers, learning experience designers, whatever, should not ignore research

Why do learning professionals in L and D – managers, project managers, interactive designers, learning experience designers and so on, ignore research? It doesn’t matter if you are implementing opportunities for learning such as nudges, social opportunities, workflow learning, performance support or designing pieces of content or full courses, you will be faced with deciding on whether one learning strategy, tactic or approach is better than another. This can’t be just about taking a horse to water - you must also make sure it drinks. Imagine a health system where all we do is design hospitals and opportunities for people to do healthy things or get advice on how to cure themselves, by people who do not know what the clinical research shows. 

Whatever the learning experience, you need to know about learning.

Lawyers know the law, engineers know physics but learning professionals often know little about learning theory. The consequences of this are, I think, severe. We’re sometimes seen as faddish, adopting tactics that are neither researched nor anything more than a la mode. It leads to products that do not deliver learning or learning opportunities – social systems that lie fallow and unused, polished looking rich media that actually hinders rather than helps one learn. It makes the process of learning longer, more expensive and less efficacious. Worse still, much delivery may actually hinder, rather than help learning, resulting in wasted effort or cognitive overload. It also makes us look unprofessional, not taken seriously by senior management (and learners).

We have seen the effect of flat-earth theory such a learning styles and whole word teaching of literacy, and the devastating effect it can have, wasting time in corporate learning and producing kids with poor reading skills. In online learning the rush to produce media rich learning experiences often actually harms the learning process by producing non-effortful viewing, click-through online learning and cognitive overload. Leader boards are launched but have to be abandoned. The refusal to accept evidence that most learning needs deliberate practice, whether through desirable difficulty, retrieval or spaced practice, is still a giant vacuum in the learning game.

So there are several reasons why research can usefully inform our professional lives.

1. Research debunks myths

One of things research can achieve, is to implore us to discard theories and practices, which are shown to be wrong-headed, like VAK learning styles or whole word teaching. These were both very popular theories, still held by large percentages of learning professionals. Yet research has shown them, not only to be suspect as theories, but also as having no efficacy. There’s a long list of current practice, such as Myers-Briggs, NLP, emotional intelligence, Gardener’s multiple intelligences, Maslow’s hierarchy of needs, Dales cone for learning and so on, that research has debunked. Yet these practices carry on long after the debunking – like those cartoon figures who run off cliffs and are seen still hanging there, looking down…

2. Research informs practice

Whether its general hypotheses like Does this massive spending on diversity training actually work? Or, at the next level Does this nudge learning delivery strategy based on the idea of hyperbolic discounting actually work better than single point delivery?  Research can help. There’s specific learning strategies by learners Does this retrieval or spaced or desirable difficulty practice increase retention? Even at the very specific level of cognitive science, lots of small hypotheses can be tested – like interleaving. In online learning What is the optimum number of options in a multiple choice question? Is media rich mind rich? As some of this research is truly counterintuitive, it also prevents us from being flat-earthers, or believing something, like the sun goes round the earth, just because it feels right. 

3. Research informs product

As technology increasingly helps deliver solutions, it is useful to design technology on the basis if researched findings. If, for example, an AI adaptive system was to be designed on the basis of Learning Styles, as opposed to the diagnosis of identified cognitive errors, that would be a mistake. Indeed technology, especially smart technology, often embodies pedagogic approaches, baking in theory, so that the practice can be enabled. I have built technology that is based wholly on several principles from cognitive science. I have also seen much technology that does not conform to good evidence based theory.

4. Research helps us negotiate with stakeholders

Learning is something we all do. We’ve all gone through years of school and so it is something on which we all have opinions. This means that discussions with stakeholders and budget holders can be difficult. There is often an over-emphasis on how things ‘look’ and much superficial discussion about graphics, with little discussion about the actual desired outcome – the acquisition of knowledge and skills and eventual performance. Research gives you the ability to navigate through these questions from stakeholders on the basis of avoiding anecdote, relying on objective research.

5. Research helps us motivate learners

Research has shown that learners are strangely delusional about optimal learning strategies and what they think they have learnt. This really does matter, as what they want is not always what they actually need. Analogously, you as teacher or learning designer, are like a doctor advising a patient, who is unlikely to know exactly what they have to do to solve their problem. An evidence-based approach moves us beyond the simplicities of learning styles and too much focus on making things ‘look’ or ‘feel’ good. Explaining to a learner that this approach will get them to their goal quicker, pass that exam and perform better can benefit from making the research explicit to the learner.

6. Research helps you select tools
One of the biggest problems in the delivery of online learning, is the way the tools shape what the learner sees, experiences and does. Far too many of these tools focus on look and feel, at the expense of cognitive effort, so we get lots of beautiful sliding effects and lots of bits ion media. It is, in effect, souped-up Powerpoint. Even worse are the childish games templates that produce mazes and other nonsense that is a million miles away from proper gaming. We have a chance to escape this with smarter software and tools that allow the learner to do what they need to do to learn - open input, write, do things. This requires Natural Language Processing and lots of other new tech.

7. Research helps us professionalise within organisations

In navigating organisational politics, structures and budgeting, also making your internal service appeal to senior management, research can be used to validate your proposals and approaches. HR and L and D have long complained about not being taken seriously enough by the business. Finance has the advantage of a body of established practice, massively influenced by technology and data. This is becoming true of marketing, production, even management, where data on the efficacy of different channels is now the norm. So it should be with learning. Alignment and impact matter. Personalised 'experiences' really do matter in the midst of complex learning.

Conclusion
If all of the above don’t convince you, then I’d appeal to the simple idea of doing the right thing. It’s not that all research is definitive, as science is always on the move, open to future falsification. But, as with research in medicine, physics in material science and engineering, chemistry in organic and inorganic production, maths in AI, we work with the best that is available. WE are duty bound to do our best on the best available evidence or we are not really a professional ‘profession’.