The Short Definition of “Real Learning”


Real learning is the ability to adopt what you know and know-how to do and adapt it under an everchanging variety of circumstances.

 

No point beating around the pedagogical bush. I’ve been asked by a number of readers “How would you define real learning?” Real learning is the ability to adopt what you know and know-how to do and adapt it under an everchanging variety of circumstances. Learning is an ongoing process. That’s my definition and I’m sticking to it.

Real Learning Versus Rote Learning

Learning in a classroom, actual or online, involves the use of short-term memory. It is all about remembering – regurgitating – then forgetting. It is rote learning, the encumbered and inhibited kind we are mostly used to doing. You remember the lesson, and show that you remember through a variety of tests and then move on. Moving on is all about forgetting. With two interesting and notable exceptions. Art. Science. The reason is simple. Art and Science require an evolving degree of knowledge from basic to advanced. Think learning to play the tuba or building a car. It’s the kind of subject matter that was always learned by apprenticing or being tutored by a master. You need basic math to get to algebraic equations and then onto experimental astrophysics. If you don’t master the fingering you cannot play a decent scale let alone get to a Bach sonata.

By contrast, real learning is somewhat like sleeping. (Not the sleeping you do when a sage-on-the- stage drones on in that sonorous monotone and lulls you into dreamland.) You do not “fall” to sleep, you go through a process of sleeping, through stages. If you’re constantly interrupted you wake up the next morning feeling like you had a bad night sleep. Real learning requires stages as well, and you cannot skip over any of them.

Playing Golf: Spaghetti on the Putting Green

Even though I do not play the game, I use golf as an example to explain the process of real learning. Interestingly a recent number of neuroscience researchers have been doing the same. They talk about reaching a point during the adoption phase where you peak at the physical learning part of the game, and you move on to the strategic or mental part. Your body has practiced so much it has really learned what to do, and now it’s on to the rest of you to learn to find the spot where you want the ball to go. Feel the wind. Sense the way the green curves. Before you get to that stage you spend a lot of time looking all over the place. They followed the eye patterns of novice golfers on a green, lining up a putt, and when they illustrated their eye movements it looked like someone had thrown a plate of spaghetti on the green. Lines and loops going every which way. With the top golfers, the eye patterns were a only few lines, most of them moving directly towards the cup.

When you have learned to play well enough, the body part of the learning to play golf is done, and your mind is free to focus focus focus. You reached the point where you are in the zone. It’s like sleep where you managed to avoid being interrupted until you reach Phase 5 – dreaming.

Why Do We Continue to Fake It?

Rote learning is an incredible waste of time and money. So much of what we learn in school, and in companies that have copied the schoolplace model into the workplace, is forgotten. It does not build on itself from experience. Not just experience in the sense of doing but even experience of knowing more. Even though history, for example, should take you from the Year One up until Today, and then deeper into every era, most of what you learn about history you quickly test and rapidly excrete. That’s just the way the system measures and rewards the student. It has nothing to do with really learning about history. Or any other subject as well. And it certainly fails miserably at providing the 21st century skills we need for the emerging Knowledge Economy.

My Story About History and Herstory

Side note: An alternative example of real learning. I had the advantage of going to a school – at the time it was called “experimental” – where we spent two years moving through time. Going to school was like being in a time machine. For example, when we were learning about the period called the 16th century, we did not have just one short history lesson,  but learned everything 16th century. We were taught about their language, words, maps, arts, crafts, clothes, sciences, cultures, politics, music, poetry, literature, plays, travels, trade, religions, wars, weapons, you name it. We were immersed in the 16th century. It felt like we were in the 16th century. It’s just another model that while not perfect, teaches you more about history than the 3 weeks you get in most schools before jumping ahead from the 16th to the 17th century.

Back to real learning versus rote learning. There are two very surprising elements to real, uninhibited learning that the fake pale excuse of rote learning excludes, disables, and even prohibits.

The Critical Importance of Forgetting

The first is that real learning starts with forgetting – making room for the new. If you have a hard time forgetting the old you will have a difficult time starting to learn the new. If you had a hard time learning what you know, then you will also try and hold on to the old and not learn the new. And be honest we’ve all experienced it. That moment when they upgrade or change a process or procedure or tool you know how to use and you exclaim “Hey, I just learned how to use it, and they’re already changing it!” So, you need to be able to clear the mental cache to use a materialistic model of the brain.

It’s Not Failure if You Learn Something

The second big part – the really big part – of real learning is failure. Failure happens. When you are adapting what you learned from the last time you did it or thought it or spoke it or argued it or whatever, you will experience failure. Smart people who are real learners go “Oh I failed, okay what did I do wrong and how can I fix it so next time I do it right?” Einstein. Edison. Dyson. My Uncle Karl. Long list. So you need to accept and enable failure for the process of real learning to work. And if “failure is not an option” then you will fail and not learn anything.

If you are involved in any kind of learning, and forgetting and failure are not emphasized as part of the learning … leave. You will not really learn a thing. If forgetting what you know at the start (I love those movie scenes where the Sargent – Captain – Leader says “Okay you idiots for starters I want you to forget everything you ever learned!”) then real learning will not happen. By the way forgetting is a brain function as studied by neuroscience as remembering. Imagine what your life would be like if you could not forget what you learned the first (and last) time you learned it …

The “High Wire Training” Exception

Now there is what I call “High Wire Training” where failure leads to your or someone else’s death. Walking across the Grand Canyon. Going into battle in Afghanistan. Responding to a 911 emergency involving a mass shooting or horrible car accident. Let’s be honest. Most of what we learn is not in the High Wire Training category. If it was, this would be a very different blog with a focus on practice, practice and more practice. Repetition. Simulation. VR headsets and more … hmmm … maybe next time.

To sum it up. Learning is a natural brain process that occurs in stages. Real learning enables all the stages. Rote learning disables the stages and focuses on a small part of the process. I’m not sure what value rote learning has in today’s world. Then again, I’m not sure it ever had any real value. What’s the point of spending time and energy learning something only to forget it almost immediately after the test?

To review: Real learning is the ability to adopt what you know and know-how to do and adapt it under an everchanging variety of circumstances. It is one of the reasons my new book Minds at Work harps on the need for continuous learning in the Knowledge Economy where every day – using what you know and know-how to do – is more than ever under the pressure of constantly changing circumstances.

Minds at Work will be published this December 2017 by ATD Press and is available for preorder on Amazon.

 

CFO to CEO: “What happens if we invest all this money into managing minds and they leave us?” CEO to CFO: “What happens if we don’t and they stay?”


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This is the first in a three-part series. The goal is simple: Completely change the way we manage and learn. Forever. Looking forward to your reactions.

Companies worldwide today seem to be suffering from the same challenges: finding and hiring talent, time-to-performance, employee engagement and retention, innovation, decision-making, and more. They all seem to be fighting off the symptoms of the common disease that has infected them. This series of posts is part of a carefully-researched and groundbreaking book “Minds at Work”, co-authored by Stephen J. Gill and I, that traces the astonishing root cause behind the malaise that’s gripping corporations, and how it can be cured.

Part One: The Wisdom in the Maps from the Age of Exploration

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Old maps are brilliant. The great mapmakers of the 16th and 17th centuries not only captured the places that were known, but gathered information from as all available sources in their efforts to map uncharted territory. The 1502 Hunt-Lenox Globe is a great example: when its creators reached a place in unknown maritime waters, they would add the warning “here there be dragons” and illustrate that area with pictures of sea monsters.

Corporate managers are now sailing into uncharted territory. It seems as if we all went to bed one night, and when we awakened the next day, everything had changed. Yet many of us are still operating as if it were yesterday. Most of the management practices and principles we use today were devel­oped in the 19th and 20th centuries, when managers were managing hands and workers were learning at a different pace. Digital technology, automation, and globalization have changed everything forever, and we all know that it’s not changing back.  In our 21st-centu­ry knowledge economy, employees produce knowledge and know-how. In order to remain competitive, corporations have a critical need to ensure that employees are continuously learning, and need to find ways to ensure that can — and will — happen.

Stuck in a Timewarp

In actuality, most manag­ers are continuing to rely on management principles and practices that were developed to meet the needs of prior centuries. Yet we have discovered that there has been a trend — growing almost undetected — among corporations located around the globe, to manage employees in radically new ways that are a better way to meet the needs and challenges of today’s knowledge economy.

The greatest mapmakers of old were not the ones who made better maps of places that were already known, but the ones who were able to imagine where the places that were still unexplored and uncharted might be, and how they might be reached.

A similar approach is needed now. Managers are in uncharted territory. They are struggling to imagine what to do and how to manage employees hired for what their minds can create, rather than what their hands can produce. Managers suddenly find themselves in a company that desperately needs employees to be responsible for their own learning, since no L&D or HR department can keep up with the rapid pace of change.

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The forces driving the new world economic order has placed companies at an inflec­tion point in the history of managing people and the way they learn.  Managers continue to sit at the exact center of the curve, which has been shaped by three sweeping and dramatic shifts in the economy in the last 200 years, each of which created its own management approach and educa­tional system to teach people how to do their jobs.

The Agricultural Economy: Managing Backs

The first great economic era focused on land: land for wealth, land for status, land for food. Private property was legally defined for the first time. Learning was hands on, and on the farm. Education was limited to a few, and was delivered in the home by tutors or in small private schools and colleges to the chil­dren of wealthy landowners and landed gentry.

Bodies were originally the major tool of production; over time, people learned to harness the power of oxen and horses. At the most extreme, people were enslaved or indentured to do the most backbreaking work. In the early 1800s, picking cotton was one of the most important jobs in the U.S. economy. We managed backs, and even in 1900, almost 90 percent of the population in the America and Europe worked on farms and in the fields.

The 20th-Century Industrial Economy: Managing Hands

The second great economic era focused in things. Harnessing steam power, discovering the uses for electricity and making their power universally available to mass-pro­duce cars, clothing, food, and more on our assembly lines and in factories. Machines revolutionized farm work and the number of farm­ers dropped to below six percent by the end of the century. Backbreaking household chores were utterly transformed. Clothes no longer had to be washed by hand — a machine could do it.  Huge blocks of ice no longer had to be hauled up stairs once the refrigerator was invented. Electric vacuum cleaners saved hours previously spent hanging up rugs and hitting them with rug-beaters.

Education was also transformed by the industrial economy. Public education was meant for everyone, and through a series of legislative decisions, it reached the rich and poor, urban and rural, male and female. Classroom instruction emphasized preparation for careers that were more about using hands than backs. Schools emphasized efficiency over individualization, and focused on educating the masses by using a curriculum designed centrally by experts that was consistent year after year. Companies taught managers and employees to do their jobs by exactly copying the classroom setting in which, as children, everyone had learned to learn.

The work­place became the school-place. The goal was mass training for mass production so that employees would be able to perform the same task or set of tasks the same way for as long as those skills remained useful.

Change was slow, and learning a skill or how to complete a task often took months, even years. People reported to the same site—office or factory—day after day. The predominant method of learning was classroom-based and instructor-led. It was high­ly structured training that followed a model developed during WW1. Corporations or training companies developed programs or courses and pushed them out to people the corporation had determined needed them.

Frederick Winslow Taylor, one of the first management consultants, wrote The Principles of Scientific Management in 1911. He developed what he called the “scientific management theory,” by studying the way hands were used at work, and using a stopwatch to time these movements to the hundredth of a minute.  The goal of these time-motion studies was to scientifically determine the most efficient way to reduce the time needed for a worker to complete each task. If that could be done, Taylor believed he would have come up with the best way to optimize and manage work on the assembly line.

For the rest of the 20th century — some 90 years — Taylor’s research would be the standard used for managing hands. It formed the basis for a branch of study called “manage­ment science” and other influential theories, including Max Weber’s Bureaucratic Management, Abraham Maslow’s Hierarchy of Needs (1943), and Douglas McGregor’s Theory X & Theory Y (1960). Today’s MBA degree traces its lineage directly back to Taylor and his studies and work that are inextricably linked addressing the needs of industrial age businesses and managers, who hired employees to work with their hands.

The 21st-Century Knowledge Economy: Managing Minds

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In this third and most recent economic era, the focus is on something intangible: data. Data that help create information that is turned into knowledge. In 1982, nearly 40 years ago, Peter Drucker was prescient enough to realize that a major change in the needs of corporations and labor force. name He realized that increasingly, people were working to produce information rather than things or goods and he referred to them as “knowledge workers.”   These were employees who mass produced know-how and ideas. They spent their days fluidly moving between thinking and talking, meet­ing and deciding, researching and writing. They transformed data into information and then into knowledge. Increasingly, large corporations and our economy was quickly evolving, depending less on the work of backs or hands and more on brainpower. And yet the way corporations were managing, training and educating people did not evolve in parallel. Essentially, it remained static.

The way corporations train employees today is fundamentally the same as it was 100 years ago. Training is still pushed out as an event and primarily presented by a “sage-on-the-stage” even if that stage is online. The same schoolplace-in-the-workplace approach used during the previous industrial economy is widely used today. Employee management practices are still based on strategies that were created to manage hands – “command-and-control”, “knowledge is power,” siloed organizational structures with minimal collaboration across the enterprise, lack of transparency, “cube farms,” a limited use of communication technology. That’s the rule and not the exception.

We Crossed the Rubicon of Work, and it Was Deep … Really Deep

The agricultural economy and the industrial economy were more evolutionary than revolutionary.

For thousands of years people used their hands to produce everything from food to tractors, and work was labor-intensive. During this period, especially through both industrial revolutions, the way people structured organizations, and approached management and learning, was a result of the need to manage hands to maximize performance and expenditures.

The changes that have come in less than 50 years are a true revolution.

For the first time in history, we have an economy in which the majority of employees use their minds to produce the work they are paid for.  These profound changes mean corporations now have no choice but to restructure our organizations, management models, and learning practices to align with and support this historic change. The new mind-intensive knowledge economy means corporations must embrace new ideas and models to ensure they are getting the smartest, most agile, innovative, creative, and collaborative workers.

Part Two: The Impact of the Great Divide and the Inflection Point

 

The Internet of Smart Things – humanizing the IOT


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David Grebow, CEO of KnowledgeStar and former co-director of the IBM Institute for Advanced Learning, believes that the Internet of Smart Things (IosT) is the most significant opportunity that has come out of the IoT world, especially for manpower-intensive heavy industries. He spoke with Industrial Internet Now about IosT’s potential to humanize the IoT and realize companies’ returns.

What is the Internet of Smart Things and how does it differ from IoT in its implications on work as we know it?

The IoT was originally designed as an interconnected system of computing devices that could transfer data over a network. The original focus was to enable machine-to-machine transfer and display of data. The primary output was the data that informed a few people about how the interconnected devices were functioning. The emphasis was on managing that data, driving new business value from the investment of the infrastructure supporting the IoT, and finding more effective and efficient ways of doing business made possible by the IoT. It was not focused on how people could more safely and effectively use the machines, since there was no human-to-machine interface.

The Internet of Smart Things™ (IosT) incorporates that human-to-machine interface and uses the interconnected computing devices to alert and inform people about what they need to know and do to safely and effectively do their jobs. Imagine if the equipment you use in the workplace could show you what you need to know about how they operate, tell you how to use them correctly and efficiently in your native language, help you be safer working with or around them, offer you details to complete and submit regulatory forms and checklists. What if they could also show you how to fix them if they are broken, provide you with the schematics and diagrams you need, help you contact a mentor or emergency assistance, and more?

“Imagine if the equipment you use in the workplace could show you what you need to know about how they operate, tell you how to use them correctly and efficiently in your native language. What if they could also show you how to fix them if they are broken, provide you with the schematics and diagrams you need, help you contact a mentor or emergency assistance, and more?”

What if all this information was delivered automatically whenever you were within a short distance of the machine? Imagine if it was instantly and securely viewable from any nearby internet-connected device. Think of the enormous impact that could have: increasing safety, eliminating errors, boosting employee productivity, proving timely compliance, among others. It could dramatically reduce injuries and associated worker’s compensation and insurance costs – all of which would have an immediate and positive effect on the bottom line.

We’ve all heard and read about how the Internet of Things in the home will transform the ways in which we live. We’ve heard for years how your refrigerator is going to send a shopping list to your grocery store, your car will make an appointment for an oil change, and the blinds on your windows will automatically close as dusk falls.

What about the Internet of Things in the workplace? It seems to me that far more people have an immediate need for the machines they work with every day on the job to supply them with specific information.

While I can appreciate that having an expensive lathe machine tell me that there is a problem with the calibration of one of the lathes, having that same piece of machinery provide me with safety warnings, a way to access operational information I may have forgotten, a name of a person to call to solve an immediate problem, or a checklist of compliance issues that need to be completed before I operate it would be far more useful. That’s the Internet of Smart Things.

In the shift to a learning economy, what role will managers play, particularly in companies in more manpower-intensive heavy industries like ports and container handling, mining, automotive and general manufacturing? Also, with relation to industrial jobs, in what ways is IosT an opportunity?

Managers who are currently responsible for providing on-the-spot reminders and remedial training would be free to perform more important managerial jobs. Learning becomes the responsibility of the workers who can find out what they need to know and do using their smart devices – phones, tablets, or Google Glass EE – connected to the machines. Managers’ role will be to enable workers to use the IosT.

Managers will also be able to look at the analytics the IosT returns and see where training is hitting or missing the mark, find out who is acting as a go-to expert for operations or repairs, check to make sure regulatory guidelines and maintenance are being met on time, and more. Managers responsible for training will be able to see what parts of the training are working and which areas need to be revisited and revised.

In your writings, you’ve said that the IosT humanizes the IoT? In what way?

It adds people back into the equation. It takes machines that can essentially talk to one another and gives them the capability to literally talk to the workers operating and maintaining them.

You’ve also mentioned that the return on investment is easier to see with the IosT. How so?

According to the 2016 Training Industry Report, the manufacturing sector alone spent more than $25 million on training that year. Current research informs us that we forget as much as 50% of that training in a matter of days or weeks. That means that every dollar spent returns only 50 cents in value. The IosT is an antidote to forgetting since it provides not only just-in-time information; it can be designed to provide just-for-me initialized training as well.

Safety direct and indirect costs from injuries and accidents in the workplace have been estimated by the Occupational Safety and Health Administration, or OSHA – an agency of the United States Department of Labor – to amount to almost $1 billion per week. This ranges from medical payments to repairs of damaged equipment. Smart machines, driven by the IosT, would dramatically cut down these costs by reinforcing safety training and providing safety alerts and instructions. By ensuring that machinery was properly operated and maintained the indirect costs would also be reduced.

What, in your opinion, do responsible developers of technology need to consider in developing IoT systems to make the IosT a reality?

“The value of having a smart machine talking to other smart machines has already proven to be valuable. Incorporating the people who work on those smart machines into the equation makes the IosT even more important.”

The human-machine interface. There is an entire ecosystem that needs to be accounted for. Machine-to-machine data sharing is one element of the ecosystem. Human-to-machine interaction and connection is the other. The value of having a smart machine talking to other smart machines has already proven to be valuable. Incorporating the people who work on those smart machines into the equation makes the IosT even more important. It’s a viewpoint that asks a simple question: How can this technology be used to make life better for the people who work with these interconnected machines every day?

David Grebow heads KnowledgeStar, a US-based consulting firm that provides Fortune 500 corporations, start-ups, NGOs and analyst agencies with insight about the intersection of digital technology and education. His latest book, co-authored with Stephen J. Gill,  “Minds at Work” will be published in December, 2018 by ATD Press.The Internet of Smart Things™ is trademarked by KnowledgeStar, Inc.

Originally published in Konecranes The Industrial Internet Now September 29, 2017

 

The Birthplace of Stars


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This turbulent cosmic pinnacle lies within a tempestuous stellar nursery called the Carina Nebula, located 7500 light-years away in the southern constellation of Carina.

 

Scorching radiation and fast winds (streams of charged particles) from super-hot newborn stars in the nebula are shaping and compressing the pillar, causing new stars to form within it. Streamers of hot ionised gas can be seen flowing off the ridges of the structure, and wispy veils of gas and dust, illuminated by starlight, float around its towering peaks. The denser parts of the pillar are resisting being eroded by radiation.

 

Nestled inside this dense mountain are fledgling stars. Long streamers of gas jets can be seen shooting in opposite directions from the pedestal at the top of the image. These jets are signposts for new star birth and are launched by swirling gas and dust discs around the young stars, which allow material to slowly accrete onto the stellar surfaces.

I love this image. It is all about new beginnings …

I’ve been writing this blog for such a long time that I became too familiar with the old look and feel. Until now. When I started these posts, I was just starting to scratch the surface of how technology could change the ways we learn. As one of my friends like to say, “Since Moby Dick was a minnow.” I have always been fascinated by how technology could enable, empower, and enhance the ways we learn.

I started before most of the educational technology we now have even existed. CD-ROMs had been replaced by Internet-driven online learning that was still new and being explored and evaluated. Formal learning in a classroom with a sage-on-the-stage was still the default and smile sheets continued to be the way the learning was assessed. LMS were just starting to be used to manage classrooms and the new online programs, and informal learning was still just a really bad idea. Some things changed, yet it feels like most of what we were doing then is what we are doing today.

So, what have I learned in all this time? Two important lessons.

First, learning as a natural human process has not changed in thousands of years. I’m still in awe of how we take in, remember, forget, and use what we learn. And no one, despite all the theories and ideas about education, has any real understanding of the process. Some theories and assumptions, many good guesses, lots of practical observations. And learning is still a wondrous mystery.

At the Inflection Point

Second, we are at the most dramatic inflection point in the history of work, and the implications for management and learning are profound.

We have come through two very different economic eras, each with a specific and different way of managing people and helping them learn to do their jobs. We are entering the third, and it is unlike anything that has ever come before. The first two periods – Agricultural driven by land, and Industrial driven by things – were labor-intensive. We produced work with our hands and learned to manage hands. Jobs were not as complex or constantly changing and we had time to learn, to take what we knew from school, or one job to another, and learn as we worked.

As we enter this newest period of history, driven by Knowledge, most of us produce work with our minds. The need to be an adept continual learner has become part of every job description. Technology has become an indispensable tool for the ways we learn.

If I could see all of you reading this post at this moment I would ask you the following question: How many of you produce work with your hands? I know that not many of your hands would go up. If I asked this question fifty years ago, when many people still worked with their hands, I would see 30-40% of the hands raised. In any audience one hundred years, ago almost every hand would be raised since just about everyone worked with their hands.

We measure our lives in tens of years not hundreds, so it feels like one hundred years is far away and long ago. In the span of history, it’s a barely a blink. And in this blink, most of the people on this planet have switched from producing work with their hands to producing work with their minds. That is new and has never happened before. Until recently it was a gradual evolution not a revolution. So, we have reached a profound inflection point in the history of work. The future is now all about learning how to manage minds.

Driving Forward Looking in the Rearview Mirror

The problem is that too many organizations are still acting as if we have not reached the inflection point. Their organizational structures and approaches to management and learning are disabling instead of enabling people to be successful. They are mired in a way of doing business that was developed when we were managing hands. What they are experiencing are a myriad of symptoms that are a result of trying to meet 21st century challenges with 20th century solutions. It does not work and there are enough companies in this century that have ended up in the Business Boneyard to prove the point. Either you evolve or you perish.

The implications are staggering and far-reaching, and that is what I will be writing about in this blog, and what I covered in my new book. If your future has anything to do with managing people, or enabling and empowering then to learn and grow professionally and personally, I recommend that you read what I discovered about how organizations are successfully learning to manage minds and thrive in the future. The research introduced me to some of the most forward-looking, smart and successful companies around the world who are quickly becoming models for what needs to be done.

The book Minds at Work, co-authored with Stephen J. Gill, can be pre-ordered on Amazon and at other web bookstores.

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And thank you to all of the many loyal readers who have been with me for any part of this learning journey.  It will only get more interesting from here onward.

The Future of Learning is Not Training


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The Future of Learning is Not Training

By Stephen J. Gill and David Grebow

Instead of jumping off the January 1st starting line, we decided to wait and see what other people are predicting for corporate training and learning in 2017.  Here’s a partial list from our 2017 Crystal Ball Scorecard:

  • The New Year will bring a wider adoption of mLearning
  • All companies will be dong more microlearning
  • There will be much wider use of xAPI and Learning Records Stores (LRS)
  • Learning apps will become ubiquitous
  • Gamification will be for everything and everywhere!
  • Video learning will be on a smart device near you
  • Social learning the idea whose time has come
  • Things are looking up for cloud-based delivery
  • Responsive Web Design (RWD) will be the buzzword for 2017
  • 2017 is the year of adaptive more personalized learning
  • Content curation for learning will lead to better learning
  • Look out Virtual Reality (VR) and Augmented Reality (AR) coming up fast
  • Finally training will focus on performance and not on smiles

Any of these predictions about technology and trends may come true. We won’t know until we reach the end of the New Year. We just believe the prognosticators are doing what they always do — looking at the future through the wrong end of the telescope.

Before we tried to see into the future, we studied the past. For over 100 years, during the first Industrial Economy, work meant using your hands to produce things. Training and learning were predicated on the need to manage all those hands. Business schools focused their management practices and principles on managing hands. Today, despite the desire some of us have to pile into The Wayback Time Machine, most of us produce work with our minds. We have been transported into the Knowledge Economy so rapidly that many of us are still not sure what happened. Even in the workplaces where hands are still making things, minds are hard at work using the digital technologies that are being employed.

All this means we need to make an abrupt turn and change our whole approach to the way we manage people, training, and learning. We know from experience that change is hard. We tend to grab onto the past and use it to design the future. It’s is a profound failure of imagination. That’s why so many predictions on this year’s list feel so disappointingly similar to last years. They are based on a managing hands model that is well beyond its’ shelf life. It’s just pouring “new wine into an old wine skin”.

The future is no longer about looking for continuity with the past and choosing shinier versions of existing technologies and trends. Sometimes there needs to be a disruptive idea that lights up the crystal ball and makes us look at the future in a new way. We believe that future starts with a simple prediction: We will transition training and learning from a managing hands world to one in which we are managing minds. And managers will be at the center.

Managers will think very differently. Training and learning are no longer the primary responsibility of someone else like the L&D Department. The primary role managers will have will be helping people continuously learn, equipping them with the tools and technology they need, empowering them to work together, constantly collaborate, openly communicate and figure out what they need to know, and know how to do quickly and effectively. Managing minds is now their responsibility and they will need to rethink and relearn what to do. Managers will need to look for people whose EQ is as high as their IQ. They will need to post on their walls and carry in their wallets what Arie de Geus said when he headed the Royal Dutch Shell’s Strategic Planning Group. “The ability to learn faster than your competitors may be the only sustainable competitive advantage.”[1]

Employees have their own work cut out in this new economy. They will need to learn to “pull” the information they need from a variety of resources rather than wait around for the information to be “pushed” to them. The artificial and archaic way we separated learning from work will be replaced by the idea that work is learning. If employees are not continuously learning, finding what they need when and where it’s needed, they aren’t improving, creating, innovating, competing, or keeping up with change. In this new managing minds world, they need to be able to rapidly curate the information coming at them from all sides, take risks applying that information to their work, and quickly decide what is useful. They will need to be able to communicate in every way, reflect on actions and decisions, and learn from everyone’s experience.

The only certainty about the future from here on out is that it won’t resemble the past. For example, we no longer have the luxury of time to define, design, develop, deliver, manage, and measure formal courses. Survival will require people who can navigate a rapidly-changing maze of policies and procedures, products and services at high speed. They need to find their own curriculum and courses, figure out an appropriate way to learn, and get on with it. It’s cliché to say it but employees will have to learn how to learn in this new environment. And management will need to support self-learning, not direct it.[2] We discovered it is already happening in companies around the world, an unknown yet powerful trend.

So our prediction for 2017: The future of learning is managing minds.

For a more in-depth look at what this all means to managers and employees look for our forthcoming book from ATD titled “Managing Minds”.

[1] http://thinkexist.com/quotation/the_ability_to_learn_faster_than_your_competitors/222409.html

[2]  http://jarche.com/2009/10/the-future-of-the-training-department-2/#comment-192218

Corporate Training’s $70+ Billion Dirty Secret


According to analyst Josh Bersin, US companies spent well over $70 Billion for employee training in 2013. Analysts predict that amount is will be significantly greater in 2015.

These are the kinds of statistics one might expect C-suite executives to pay attention to. So it’s odd that they seem not to be paying much attention to the ROI for corporate training.

It’s abysmal.

PHOTO elephant in room

Leading experts have studied the subject at length; the statistics they provide differ. Some say there are too many variables to allow for “one-size-fits-all” statements about how much training is retained, and how quickly it is forgotten. They note the  variety of training goals and audiences receiving the training, as well as differences in training delivery methods.

Having said this, there is general agreement among experts in the field that that corporate training’s success rate is, shall I say, “poor.”

One of these experts is Dr. Art Kohn, who has done a great deal of work on “the forgetting curve” and its effect on training retention. He’s also the recipient of not one but two Fulbright Fellowships for work in Cognitive Psychology and Educational Technology. In a recent article in Learning Solutions, he wrote the following:

It is the dirty secret of corporate training: no matter how much you invest into training and development, nearly everything you teach to your employees will be forgotten…this investment is like pumping gas into a car that has a hole in the tank. All of your hard work simply drains away.

The fact is that this “dirty secret” is really not secret at all.

The research and resulting articles about this have been out there for years. Yet there’s not much evidence that corporae executives are acting upon it, despite its its obvious and critical importance to the bottom line.

Bersin’s research also shows an explosive growth in technology-driven training, including self-authored video, online communication channels, virtual learning, and MOOCs. Worldwide, formal classroom education, now accounts for less than half the total training “hours.”

According to Bersin, mobile devices are now used to deliver as much as 18% of all training among what he calls “highly advanced companies.”

Does this mean that employees are using their iPads to access Udemy courses? If so, is there a significant difference in retention rate for employees who have information presented by a live trainer while sitting in a room with 20 fellow workers… versus those who receive it on mobile phone the subway on the way home at night… compared to someone being trained via  iPad while sitting in the living room after the kids have been put to bed?

We won’t have statistics to provide answers to those questions for some time.

But corporations should be watching closely to see if new methods of delivering training result in a dramatic increase in retention among employees once they’re on the job — because if Kohn is right, even achieving a whopping 400% increase in retention will mean that after just one week, the average employee will still be retaining only about half of what is needed on-the-job.

That’s hardly a stunning success rate.

Research has made it abundantly clear that the basic premise that drives corporate training is fatally flawed.

It’s abundantly clear that the training corporations are currently providing to their employees  is not succeeding in providing them with the information they need to do their jobs properly the first time. So why does corporate America keep throwing good money after bad, trying to find a “patch” or download an “updated version”?

It’s as if a purple elephant with pink toenails is standing next to the coffee table and corporations are only willing to acknowledge that there’s an “unusual scent in the air.”

My next blog will give more compelling facts to show why a major change in corporate training is needed.

High Innovation Hiring?


dilbert on training

I have been researching the difference in approaches to learning between Boomers and Millennials. I recently started reading and hearing about a new approach to hiring and learning called a “high innovation system”.

We know there has been a sea-change in the old hiring for life contract between employer and employee. And the union agreements are disappearing faster than you can say “retiring boomers”. There is also a newer change in the way companies view employees learning.

We originally had a “high commitment system,” which valued long-term employment and on-the-job training. The new approach is called “high-innovation”. Here’s the idea in a quote from  Andrew S. Ross writing in SFGATE

Engineers are typically hired because their skills and knowledge are required for a specific technology or product being developed. This system is seen as cost-effective, since the company can hire required skills and does not have to retrain experienced workers, who usually command higher wages than new graduates. Of course, this puts engineers, who are no longer retrained by their companies, at a disadvantage as they age.

I had an epiphany about why older workers over 40 are becoming an endangered species, not only in the high-tech industry, but in companies worldwide.

I come from a generation of continuing education – workers tagged to go from event to event to learn new skills and improve or update old ones. I wondered why we consider so many older (read post-40) workers as part of the ‘long-term unemployed’. The answer is that “knowing” has replaced “learning”. According to the SFGATE article, if a company can find a worker with a specific skill to fill a job that requires that skill, then there is no need to spend the time and money training someone to learn it.

In today’s flat and hypercompetitive world, it’s the equivalent to trying to teach a square peg ‘roundness’ when simply finding a round peg will do.

It is the difference between the “high-commitment system” in which employees expect to be taught and learn and improve skills while they are working in order to improve their performance, and the “high innovation system” in which people only become employees when they can already perform the skills that are required. How they learned them is not important. Being able to prove they can do them is all that counts.

In the industrial economy, where change happened more slowly, there was time and money to train someone in a new skill. In today’s Digital economy, where there is more talent out there than time or money for training, the trend among some companies is that learning and development is irrelevant. The digital revolution happened so fast that an entire segment of the workforce now has an ‘use by’ date stamped on their foreheads.  It appears that what a Digital Native has already knows will always be in higher demand than what a Digital Immigrant can learn.

To quote Mark Zuckerberg: “I want to stress the importance of being young and technical,” Facebook’s CEO told a Y Combinator Startup event at Stanford University. “Young people are just smarter. Why are most chess masters under 30? I don’t know. Young people just have simpler lives. We may not own a car. We may not have family. Simplicity in life allows you to focus on what’s important.”

The problem with this approach to hiring and learning is that it may work for hard skills, but with regard to softskills – for example people management – learning never stops. You may temporarily find the round peg for the round job, but wait a few months and the shape of things will change. The Digital Immigrants and Digital Natives must both be continuous learners of softskills. And the experience of the older workers – especially in the area of soft skills – will always be an important part of the younger workers learning. Mentors are not born, but only made by adopting and adapting to success, failure, more success over lots of time.

Training for hard skills will soon become as obsolete as the chalk board. My prediction is that it will soon be replaced by performance support utilizing the Internet of Things (IoT) to help people who simply want operational or procedural information on the job.

Training for hard skills will soon become as obsolete as the chalk board. My prediction is that it will soon be replaced by performance support utilizing the Internet of Things (IoT) to help people who simply want operational or procedural information on the job.  Using embedded chips or beacons, machines or equipment will be able to ‘talk’ to you. They will tell you what to do to make them work, how to troubleshoot a problem, instruct you about fixing a broken part, walk you through completing a safety inspection checklist or finishing a regulatory compliance report form. That finally solves the problem of your mind falling off the forgetting curve and takes hard skills training – and the many millions of dollars and uncountable hours of development time – off the high-commitment table.

It’s the people-to-people skills that are still and always will be hard to learn, especially for people who prefer to spend time focused on things or ideas. You cannot put a performance support beacon on a worker and have it instruct you about what to say if their performance is not meeting the company’s expectations. Or they need time off for an operation. Or they are depressed over someone’s death. Or … or … or ….

So that still leaves us with the need to learn soft skills. An area from what I understand Mark Zuckerberg and Facebook could go to school on.

How would you define your company, as high-innovation or high-commitment? And as time marches on is this just a temporal blip on the hiring radar of the Millennial generation? Is high-innovation a symptom of an outmoded approach to training that no longer really works? Will a culture of learning evolve and replace what we once called the high-commitment company? You tell me ….

Discursive or recursive? The fractal nature of education


We’d like to share the article below by Steve Wheeler, Assoc. Professor of Learning Technology in the Plymouth Institute of Education at Plymouth University in England.  We admire Steve not only for his thinking about the future of learning and education–but also for the clarity and beauty of his writing. His blog can be found at:  Steve Wheeler

I presented a keynote at the Curriculum Enhancement Day for Portsmouth Business School recently, and chose this bright coloured image as one of my opening slides. It is as beautiful as it is intriguing, and it’s known as the Mandelbrot Set. I didn’t choose it solely for its visual impact, although as you can see, it certainly is quite a stunning image, and there are many variations. I chose it because I wanted to use it to make a point about what education is, and what education can become. You see, the image represents a mathematical formula that is recursive. In other words, as you zoom in to the image, which represents data points of a mathematical calculation, it continually reproduces itself towards infinity. Mathematicians will understand the explanation below from Wikipedia, but the rest of us might struggle:

The Mandelbrot set is the set of complex numbers ‘c’ for which the sequence (c, c² + c, (c²+c)² + c, ((c²+c)²+c)² + c, (((c²+c)²+c)²+c)² + c, …) does not approach infinity. The set is closely related to Julia sets (which include similarly complex shapes) and is named after the mathematician Benoit Mandelbrot, who studied and popularized it. Mandelbrot set images are made by sampling complex numbers and determining for each whether the result tends towards infinity when a particular mathematical operation is iterated on it. Treating the real and imaginary parts of each number as image coordinates, pixels are coloured according to how rapidly the sequence diverges, if at all.

Follow that? Me neither. The rest of us simply admire its visual appeal or marvel at its fractal properties and how it is never ending. The point I wanted to make at the conference was that much of our education systems are fractal in nature. Education is delivered recursively, where students are required to reproduce knowledge that is already known.

It’s a safe approach to education, and learning can be easily measured. Those that become teachers continue this tradition, teaching their own students the same knowledge, in more or less the same style they were themselves taught. Assessment of learning also has fractal features. Standardised testing is based on reproducing knowledge. Final examination success is premised on the student’s ability to reiterate what has already been taught in lessons. There is no room for exploration or creativity in summative assessment.

My point was that when education is conducted in fractal mode, it does not obtain its full potential and students are disadvantaged. I asked my audience to consider the difference between recursive and discursive education approaches. In recursive education, we see reproduction of knowledge, and we see students learning content towards a product – memorising facts and then reproducing them for the examiner. In discursive education, students are allowed to digress from the pathway, investigate new and untravelled pathways, and discover for themselves. Instruction is minimised, learning takes centre stage in the process. This kind of learning can be found in project work, problem based learning and personal research and many other progressive approaches.

My question for my audience was this: How can we as educators provide discursive opportunities for our students?

What would it take for us to leave the safe and mundane world of product based, recursive education behind and adopt new pedagogies that promote self discovery, digression from prescribed pathways and learning by a process of serendipity?

It would be a major risk for many institutions, and there would be some personal cost. But if we don’t try, how will we make any progress? This is an initial foray into this area for me and I would interested in your views on these ideas. As ever, I am open to discussion and revision, because I’m wholly committed to discursive enquiry.

Steve can be reached at S.Wheeler [@] plymouth[.ac.uk]

Coming Soon to Your Workplace: The Internet of Smart Things


Imagine if the equipment you use in the workplace could:

  • show you what you need to know about how they operate
  • tell you how to use them correctly and efficiently
  • help you be safer working with or around them
  • offer you details to complete and submit regulatory forms and checklists
  • show you how to fix them if they are broken
  • provide you with the schematics and diagrams you need
  • help you contact a mentor or emergency assistance
  • and more, lots more.

What if all of this information was delivered automatically whenever you were within a short distance of the machine?  Imagine if it was instantly and securely viewable from any nearby internet-connected device.

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Think of the enormous impact that could have: eliminating errors, boosting employee productivity.  It could dramatically reduce errors and injuries and associated workman’s comp and insurance costs —  all of which would obviously have a positive effect on the bottom line.

We’ve all heard and read about how The Internet of Things in the home will utterly transform the ways in which we live. We’ve heard for years how your refrigerator is going to send a shopping list to your grocery store, your car will make an appointment for an oil change, and the blinds on your windows will automatically close as dusk falls.

What about the Internet of Things in the workplace? It seems to me that far more people need the machines they work with on the job to supply them with specific information.

While I can appreciate that having a dishwasher that will automatically turn itself on when its full might be nice, having a piece of machinery that can provide me with safety warnings or with a checklist before I operate it could prevent me from being seriously injured.

That’s a whole new category that I call “The Internet of Smart Things.”


I recently saw a demo of an app that can make everything I’ve just described above a reality. The app won the Best in Show Award at mLearnCon 2014 DemoFest in San Diego, and it went up against some big names in the Edtech industry.

The app is driven by iBeacon technology connected to any cross-platform internet connected device that can pull information from the cloud. The beacon goes on any machine or piece of equipment and sends out a specific signal when you get close. The app ‘hears’ the signal and calls the cloud for the information on that machine or piece of equipment. You get a tailored menu of information choices that could include safety checklists, operating instructions, functional specs, diagrams, and safety warnings. Whatever you need. Whenever that information is really needed.

You have now crossed over into the Internet of Smart Things.

The opportunities are vast and diverse, across industries ranging from mining, logging oil exploration and refining, to manufacturing, pharmaceutical and medical, construction and engineering, food production and agriculture.

According to a recent Gartner study the size of the market for the Internet of Things (IoT) by 2020 is estimated to be $1.9 Trillion.

And here’s a breakdown by Industry according to another Gartner study:

IoT Market Share 2020

Here’s the link to the 9-minute demo of the app I saw. It’s technical and explains how it works:

https://www.dropbox.com/s/0jq8ohnaykfa09w/DemoFestArchiveBestOfmLearningDemoFest2014Baty.mp4?dl=0

I invite you to take a look and tell me what you think. Looking forward to hearing your thoughts.