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Trending in Education Podcast

First published in Trending in Education

In this Trending in Education podcast, Mike Palmer and Michelle Weise discuss “T-shaped” learners, and how they develop over a long career. Michelle talks about later-life learners, and how they can profitably upskill. Pursuing a four-year degree may not appeal to 55+ learners, and even the current MOOCs may not meet their needs.

Given all the challenges, Mike asks Michelle to provide some hope. She tells him how Clay Christensen helped her stay optimistic, and how the wide variety of innovators should stick to the shared agenda of creating a robust ecosystem: breaking down the walls that obtain between K-12, higher ed, and workforce learning. And how the pandemic has further exposed this need.

Mike and Michelle discuss the work of Suzanne Simard regarding the surprising subterranean ecosystem of trees and how it can serve as a model of the idealized education ecosystem. They then discuss the power of such metaphors. They also note David Epstein’s Range, and the importance of the generalist in the world of specialization. “Far transfer” is also on the table.

Finally, Michelle discusses “skills compasses”. Enterprises often do not know the skills their employees have, and let them go despite their potential usefulness. She notes a few innovative companies that help those laid off find the training they need to meet the skills demanded in their labor market.

Full transcript below

Mike Palmer: Welcome to Trending in Education. Mike Palmer here. I'm very pleased to be joined today by Michelle Weiss who's the author of a new book, called Long Life Learning. I would definitely recommend it to our listeners. It's a really interesting take on the future of work. She's also a senior advisor at Imaginable Futures. She has had a long and interesting career, which we're going to get into in a bit. Michelle, I'd just like to welcome you to Trending In Education.

Michelle Weise: Thanks, Mike.

Mike Palmer: The book is relatively recently off the presses. I believe it dropped in late 2020. There are some interesting references to how the pandemic may have influenced some of your thinking. But a lot of what you were writing about was more far-reaching, perhaps, in terms of the time span that you were thinking about. I'd like to begin by getting some perspective from you on how you got to this point in your professional life. We always like to hear our guest's origin story as we're kicking off the conversation.

Michelle Weise: Sure. I started off my career as an English professor at Skidmore college and slowly made my way into ed tech by working for an ed tech startup that was helping service members transition out of the military into civilian careers. And that's really where I first got exposed to every version of nonprofit online education.

From there, after that company had to pivot, I ended up working with Michael Horn at the Clayton Christensen Institute for Disruptive Innovation, which was Christensen's think tank for education. And I led the higher education practice there, and was lucky enough to work directly with Clay as well.

And then I started moving from theory into practice by building out research and development labs for both Southern New Hampshire University and then later for a Strada Education Network. I ended up building their Institute for the Future of Work, and now I'm with another funder called Imaginable Futures and doing some advisory work there.

Mike Palmer: That's a relatively long span of time where you've been looking at what's emerging, how innovation can happen. And then you established some labs to do research about the future of work.

Let's begin with the book Long Life Learning. It's an interesting turn of phrase. Folks frequently hear “lifelong learning”, but the title is Long Life Learning: Preparing for Jobs That Don't Even Exist Yet. I think there's a lot in that title, and it is a really interesting read.

Can you talk about what drove you to put the book together?

Michelle Weise: Yeah, it's obviously a play on “lifelong learning”. And the reason why I thought it was important to do that was for me, especially, I've been this space straddling higher education and the workforce, and I've heard often in the ether this concept of lifelong learning, people love to talk about it. But I wasn't necessarily seeing that translate into action. So, I thought a new mental model might be helpful.

And for me, one of the most useful prodding mechanisms to push us into action has been this thought that, "Oh my goodness, what are we going to do if we actually have to navigate a longer and more turbulent worklife?" Because a lot of the different forecasts and prognostications are pointing to what is not only a longer lifespan, but ultimately a longer worklife. And so, as we think about "long life learning", it suddenly makes clear all the different kinds of changes and moves we need to make in order to better connect the future of education and the future of work.

So that's really the motivation behind the book.

Mike Palmer: Makes sense. And it is interesting, it's something I've reflected on more over the years, that even if you have a fantastic undergraduate experience in that 18 to 24 age range, that becomes further and further in your distant past. And it becomes harder to remember the lessons that you learned then and apply them to the remainder of your professional life. I was really struck by how deeply you explore that idea. And as someone who looked at disruption with Clayton Christiansen, and thinking about how it relates to higher education, I'd love to get some of your thoughts on that.

Michelle Weise: What's probably pretty obvious when you read the book is there's a lot of featuring of different kinds of innovations. Not all of them necessarily disruptive innovations, but just things that we need to pay attention to and think about scaling or building out more of these. Or maybe building out better business cases for some of these different innovations. I feel like I've had this great privilege to evaluate and look at the landscape of hundreds of different kinds of education technologies that have been emerging over, especially, the last decade. And it's been from the standpoint of someone who wasn't one of the competitors in the marketplace but could look at them and assess and see what was going on.

When I was at Clayton Christensen's think tank, for instance, we would get hundreds of inbound requests. People wanted us to look at what they were building to ultimately maybe write about them as disruptive, but they also just wanted to see if we had seen anything like it on the market. I got to continue that landscape view when I was at Southern New Hampshire University, and so many different vendors and entrepreneurs were coming to us because they wanted us to use some of their different solutions.

It’s been fascinating to see just the span of innovations and the incredible burgeoning of solutions out there. At the same time, it's also been hard for me to look and see so much reinvention of the wheel or duplication of efforts, or just siloed activity, where you have all these amazing innovators who are building in parallel with one another, not realizing, how their solutions overlap with one another and might actually be better served if they brought their services together.

In the book, what I try to lay out is: this isn't about starting from scratch. It's about stitching together a lot of these new and existing solutions in service of what I call a better functioning learning ecosystem, where if we have to face a 60, 80, or a 100-year worklife, we are going to be able to do this in a more seamless way.

Mike Palmer: There are a lot of interesting ideas that you explore in some depth in there. I like both the conceptual depth and then the breadth of examples, because I think you do a good job of both those things. And then also there's a lot of case studies and interviews with typically a working professional who is trying to juggle their educational career, and in many cases, they're left to their own devices to do that.

The thing that I also appreciated was the way in which the stories that were told could humanize what can sometimes be a somewhat abstracted conversation. And then you did talk about the types of skills that folks should be thinking about to stay job-relevant in that long life and preparing for jobs that don't even exist yet.

Michelle Weise: Yeah, it's funny. As I was writing the book, I think some of my colleagues thought I was going to be out there with a crystal ball saying precisely the kinds of jobs that would exist in the future. And that's just impossible.

We've already seen just over the last decade, these jobs emerge that are the hot jobs of today that really just didn't exist before. The purpose of the book is not to identify the specific jobs that will be in demand in the future, but the kinds of skills and the kinds of problem solvers we need to become in order to meet that very uncertain world of work ahead. As we think about better preparing ourselves for this very turbulent future ahead, we have to realize first that yes, there are certain skills that humans can leverage better than robots. There are certain skills that we just have to relinquish to computers and machine learning, and AI, just because they're always going to do it better, faster, and without any mistakes.

There are other things that are like those more human skills or those quote-unquote softer skills or non-cognitive skills. Those workforce competencies around collaboration and teamwork, and exercising judgment, systems thinking, creativity, curiosity, all those kinds of things that we talk about that are going to be core skills needed in the future.

But it's also really important that we realize that alone is not enough. We can't just bank on our human skills. In order to be someone who is truly marketable in the future, we have to have also enough of that technical or domain expertise in order to also assess the work or intervene at the right times when we're seeing how that plays with artificial intelligence or these different kinds of rapid technological advancements.

I think that we're seeing right now with, for instance, the intense challenges you're seeing around social media, that there are all these different kinds of problems and volume impact repercussions that we didn't evaluate before letting loose the technology. And now what has happened is some of that technology has outstripped our ability to manage it.

And so that's what we need to avoid in the future. Making sure we're marrying our human skills and our judgment and our ethics and our values with enough of that technological understanding, that vertical expertise.

If you think about a T-shaped learner, we need enough of that to be prepared to face all the different kinds of uncertain circumstances that are coming our way.

Mike Palmer: The human-plus skills are what you referred to, that blending of the broader top-of-the-T, human skills. And then what I thought was interesting too, was maybe a handful of places where you are going deep technically. Rather than just a T with one vertical dimension, you have a few different areas.

And you could imagine over that long career life that you're talking about, that in many ways you're developing a different depth of knowledge with each of those stints. And then as you start to round that out, it does speak to some of the advantages of hiring someone who maybe is later in their career. I did think the perspective you had on the workforce that is 50 and older was a recurring theme

Michelle Weise: It's fascinating because even today people are staying in the workforce for decades longer than we had anticipated. They're moving well into their sixties and seventies before they think about retiring. Some of them can't even fathom retiring just because of the lack of ability to accumulate wealth.

And so, it was important for me to not only share some of the statistics around those, but also for the audience or the readers to be able to hear the voices of these learners. When you hear what they are saying, it suddenly becomes so immediate. I think we can all think of someone we know in our families or a friend who is in this similar situation where even if you're 55 it feels a long way off to think about retirement. How do you begin at 55 to think about a new career or a new set of job transitions that you're going to have to navigate?

And how do you as that more mature learner evaluate and surface the different kinds of skills and experiences you've accumulated along the way that have never been formally recognized. There's so much richness in what you've learned, and the labor market has no idea how to make sense of it.

This is a real amazing part of thinking about long life learning. And I think when people think about reskilling and upskilling they immediately think more along the lines of the technical skills that we need to remain relevant as we think about the advancement of machines and robots. But often it's actually that kind of broadening of that horizontal set of skills, those more human skills, that we really need to work on. And for someone who is in their 50s, 60s, or 70s, what does a learning experience look like for that older learner so that they can do that kind of broadening of skills?

We tend to always situate that kind of learning in a two or four-year degree. We associate it with a liberal arts experience. A 55-year-old learner is not really apt to go back and think about entertaining a four-year degree. They need something that is more short-burst, more affordable, more relevant to the skills that they want to build, but also enables that kind of broadening of human skills.

So, the question around 50-plus workers really also pushes us to think about how we truly engage with learners differently than ever before. Even if you look at some of the open-access mega online universities that cater to 30-plus learners, there's still not a real meeting of learners where they are. There's still rigidity within the system where it's still not quite flexible enough for people to integrate into their busy lives. If we're trying to push ourselves into action, we need to break down this monolithic category of adult learners and really identify as we think about the different stages and phases for adult learners and what truly are their needs.

Mike Palmer: I did find the language of the new learning ecosystem to resonate as well, where the benefits of design thinking and system thinking--that's a relatively new innovation. Similarly, on the product side, there's been a lot of innovation around product management and user-centered design learner-centered design Those don't seem to be reflected in the current educational system that we have.

And what I like about your book is that it's almost a challenge to be optimistic about the future, and a challenge to not just envision what it might be, but then begin to explore how we might get there. Can you talk about how you stay optimistic, because the tone did seem hopeful, and I'm trying to find lessons in hope these days.

Michelle Weise: I think for me being in this space of the future of work, it can feel daunting and paralyzing If you focus on the it, or the work, the jobs that are going to be automated by technology. When you get into that space, it can get very dark very quickly. But if you actually shift away from thinking about work to the future of workers, you start to see how you can adopt that more positive and hopeful vision for the future.

And I think a lot of it probably stems from the way that my worldview was so shaped by Clayton Christiansen. His theories of disruptive innovation have always offered me this very constructive lens through which I can look at a whole slew of innovations, even ones that we typically would feel this reflex to dismiss because some of them just seem of low quality to us as higher ed administrators, or whatever the case may be. Clay really taught me that it's really at those moments when you feel ready to dismiss that thing that you can't make sense of to actually take a closer look.

For me, all the hope comes from all the innovation that I see. There is just this vast kind of bubbling up of so many different kinds of solutions out there. It's not a lack of awareness or a lack of willingness to meet this future ahead. We can't just be in this mode of a flurry of activity; it has to be geared toward that common agenda. And that's where the language of an ecosystem is critical. We cannot continue to have a separate K-12 system which is siloed from our higher ed system, which is siloed from our workforce training system. We've done that for far too long.

And I think the pandemic has shown us is what happens when the cracks get just so vast in between those systems, that when you were forced to think about transitioning folks who are laid off or furloughed into wholly new industries we were just completely stuck. And it was just because we were in this mode of not relying on these different systems, and not forcing them to communicate well with one another. And I love the concept of an ecosystem because it underscores the value of interdependence. It's not about just getting rid of a system or starting over; it's about making sure these things are tied better together

Mike Palmer: And you get into the roots of the tree system, and a really interesting analogy. I got to say, you do have some interesting analogies in there, but that one in particular captured my imagination. Can you expand on that a little bit?

Michelle Weise: If you think about just a very simple ecosystem like a forest, I think we tend to look at the things that are above ground. We'll see a rock or a tree or a mountain and spend our time looking at those things. What we fail to notice are the incredible workings underneath, or below ground.

This is leveraging some of the research from Suzanne Simard who talks about these fungal networks that actually connect root systems of trees. I think what people didn't really quite understand is that trees actually have this amazing communication network even across different kinds of trees. So, like a fir tree can talk to a pine tree, and of course a mother tree can shield a seedling tree and send it more nutrients through this intense fungal network below ground. It's this near-intelligence that emerges.

And for me, it was so striking to think about that ecosystem as almost an ideal that we need to aspire to. Because when we think about the incredible data across all of those systems that we have--K-12, post-secondary, workforce--and we think about how difficult today it is to just understand the outcomes of learners because we have no ability to stitch together wage earnings and outcomes data--our system is so unintelligent.

And we need to figure out how do we pull together all these different strands and make all of this data make sense together, so that we can also figure out what to prioritize, and what to build, and how to allocate resources. To me, that kind of natural ecosystem ideal is important to understand, because again it underscores the work ahead that we need to accomplish.

Mike Palmer: And it does remind me that a lot of these metaphors are going lateral or reaching across dimensions that typically the information it's not shared in. And I did like your references to David Epstein's book, Range, which I'm a huge fan of as well: the importance of keeping some sense of generalist sensibility. And it's also important in that some of those technical dimensions, some of those aspects of expertise that you develop, are almost designed to be obsolete relatively soon after.

Michelle Weise: Just to touch on your comment about David Epstein's Range. I think you're absolutely right: this kind of lateral, or horizontal, or the ability to move across domains and think analogically and stitch together solutions from seemingly unrelated domains in order to solve problems in whatever industry you're focused on--I think that is really powerful.

As we think about long life learning, that kind of learning transfer--or what learning scientists called far transfer--takes a long time. That kind of deep learning is probably the most important learning that we do as humans, but it doesn't come across quickly, it doesn't come across well in the kinds of standardized exams we have today. And there's this great quote that he has in the book where he says something like, “Deep learning looks like failure, right? It doesn't look good the way we test it.”

So again, as we think about this kind of longer life span how are we going to assess that kind of learning and how are we going to provide opportunities for learners to develop that kind of ability of range and far transfer over a lifetime? But in ways that also move them ahead, and move them out of kind of this mode of survival into jobs where they can thrive. And that really then implicates the employer in some of that learning.

Mike Palmer: Can you talk about the way to think about skills? Some detail that you go into about your skill shape, and the way to understand how jobs needs may be very localized, I found that to be something I've talked to folks about, I've read about in the past, but I feel like there was a very compelling case to be made for some broader concepts. One is a skills-based orientation, another is problem-based learning or challenge-based learning. Can you go a little deeper on the way you think about skills?

Michelle Weise: You hear this concept that labor markets are hyper-local, that there maybe isn't a national skills gap, but it really depends on where you're looking in terms of regions. This is a study that we did at Strada Institute with Emsi, a labor market analytics group. We were able to actually isolate how different the same exact role looks when you're looking at, say you're in manufacturing and you have a more traditional production role within manufacturing.

Even just the differences between Los Angeles and Northern California are pretty stark. In LA, because the skill demand is shaped by the employers in that region which are often aerospace, Raytheon, Northwest Grumman, those kinds of organizations, it actually looks more like a traditional manufacturing role.

But when you move up the coast to Northern California, the skill shape is actually very much molded by those employers in the area which are Apple, Tesla, Intel. And so, it's much more difficult to have traditional manufacturing skills in Northern California. There are a lot of other skills you have to tack on when it comes to design, and also statistics and math--they're higher skills demanded in those areas.

And this happens for every single role that we hire for in the United States, where if you're looking for a digital marketing specialist, as an example, the kinds of skills that are in demand are going to look very different in Wichita versus Washington, D.C. And so what we're able to show in the book is what physically those skills shapes look like. Because they actually do make interesting kinds of shapes.

But you can not only do this for jobs that are in demand, or opportunities, but you can also do this with people. We each have our different skill shapes. My skill shape looks different from yours, Mike. And when you actually map those two things together, your own personal skill shape, or the skill shape of the talent in a specific region, and the skill shape of that role, you see where the talent may be showing too much of a certain kind of skill or showing too little of a skill in Apache suite or whatever the case may be.

You can do this in any kind of region. We did it at the metropolitan or a city level. And for me, the best illustration of why this is important to actually do is, I think, when we think about reskilling, we think about just automatically, especially for working-class Americans, “Oh, they just need to go get a degree.” Because we realize the earnings premium on a bachelor's degree.

But as I show in the book, with one of these real-life learners, Jaylin, it can be so daunting to think about four years of night school. That's not something in the cards that they were looking forward to, especially if they're trying to take care of a family and be a dedicated father at night. And what we're able to do with an actual skill shape is to show Jaylen, “You don't actually need to go get a four-year degree. What you need are these specific skills so that your skill shape actually matches the skill shape of this role that your employer wants you to fill.” So that's the exciting opportunity ahead.

Mike Palmer: And I think there is a mindset shift to discover around a skills-based way of understanding yourself, a way of understanding the marketplace. It is very different from the traditional two-year, four-year degree mindset. And it maps much more closely to what traditionally is called on-the-job training.

And there's a real skill to understand how to distill what you know how to do into the skills that are in desire. And that's a theme that I continue to come back to in the conversations I'm having; which is, “Can you get better at translating who you are and what you can do into something that resonates with the hiring manager on the other side and or the labor market data that is out there?”

You talk a lot about transparency as well and bias. I'd love to hear a little more from you on that. How do we get this new learning ecosystem to operate in a way that is not unfair and isn't perceived as rigged?

Michelle Weise: Part of the way forward out of what feels to be a rigged system is getting transparent about those skills. So exactly as you were saying, how do we help folks actually translate what they can do into the language of the labor market? This is something that stymies learners from graduation throughout their work lives

And part this phenomenon has been this concept of skills-based hiring, which is an attempt to get to that place and to move away from just thinking about pedigree or a degree from a prestigious institution, to get very granular about the kinds of skills that we're actually wanting in the workforce and what skills does this person actually have. So, there's an assumption that if we can get transparent there will be less friction in the labor market.

And it does make sense because right now we're flooded with the credential engine--over 730,000 different kinds of credentials out there. And it's impossible for a hiring manager to make sense and sift through those over 700,000 different kinds of credentials. So how do we actually figure out how to make this process more fair and actually get very clear about the skills we need. Because as most of us know who have ever written a job description, there's no standardized way to do this. And we are not clear about what we need. And we'll often hire people who don't have those skills that we write down on that job posting.

And then again, as we think about learners who have different experiences in the workforce, and may not have ever gotten that degree, how do we capture what they can actually do? A lot of the innovations that I point to in the book are showing different ways of getting at this better marriage of skills in demand and the skills that are there in the supply out there. That can come in the form of different kinds of skills compasses which I point to from places like SkyHive, and MD, and Future Fit. This can come in the form of different sorts of assessments that try to democratize the process and help us understand, “Does this person actually have those problem-solving skills that we need?” in a way that's fair and doesn't rely on that bachelor's degree. Are there ways to blind the hiring process and match people for hiring managers based on the skills and experiences they have and that job postings data?

There are all these different kinds of innovations that are emerging trying to reduce that friction between education and the workforce, and I think even though this concept of skills-based hiring is out there and trending, and a lot of employers say they are open to moving in this direction, we haven't actually seen the data bear that out yet. We haven't actually seen this tremendous shift towards skills-based hiring. We have quite a bit of work to do there.

Mike Palmer: And I think the related point about bias in some of the hiring processes as something to be careful about, and another place where those human-plus skills are needed where the AI filters frequently may inherit some bias. That then makes the hiring practice problematic.

The other aspect of equal access I think is around accessibility. Another metaphor that you talk about is the idea of the ramp and making it easier to get onto and off of your educational program over the course of your life. It did remind me about some of the conversations I've had about universal design for learning. But you were talking about it a little more in the metaphorical sense: How do we break the curve so that we can make it less bumpy when people are trying to get into or out of education?

Michelle Weise: What you're referring to Mike is the section that I talk about curb cut effects. Before the Americans with Disabilities Act was signed, there was this movement to build these sloping curves so that folks with disabilities could actually move around more easily. And the fascinating effect, or the ramification of these sloping curves, is that just as you suggest with universal design, it's not just about facilitating the mobility of folks in wheelchairs, but it also helps all of us. It helps us if we're pushing a stroller, or pushing a dolly with packages, or skateboarding, or cycling, or even just running. It's got this positive effect for all of us.

And so, if we design well for the people who are facing the most constraints and obstacles, there's a really beneficial future ahead for all of us. Because we have to recognize that in this longer, more turbulent worklife, it's not about people over there who need to be upskilled or reskilled, it's actually all of us. No matter if we have an incredible job today, or a great setup even right now, we are going to be in an uncomfortable state in the future where we're going to have to seek out new work.

And it's going to happen to all of us, even if we have a four-year degree, or a master's degree, or even a Ph.D. We're going to have to navigate more job changes than we ever dreamed of in order to make for that better future. We have to design for the people who are stuck today, because if we do it well it's going to benefit all of us in the future. That is why I am laser-focused on people who are currently not thriving in the labor market, so that we can design around the obstacles or the barriers that they are facing today.

Mike Palmer: Modularity and flexibility of online learning is something that certainly came to light on the positive side in response to the pandemic, even though I think there was a lot of concern about the quality of online education. But the reality is it can fit around the complex demands on adult slides in really interesting ways.

I always like to get my guests’ perspectives on any other trends or new things that are emerging in the world around you that are capturing your imagination. Anything new and exciting that we haven't talked about so far?

Michelle Weise: Yeah. I think the one piece that I mentioned before is around a skills compass. There are these different kinds of AI-powered platforms out there that are trying to do a better job of helping us surface our competencies, and also helping employers understand the skills of their existing workforce.

It's fascinating how poorly employers understand their own people. They often are struggling to figure out what to do with names and titles. They don't actually know the granular skills that they have, and they also don't know whether they should be skilling up some portion of their existing workforce for the jobs that they anticipate in the future.

And so, some of these different groups like SkyHive have shown me interesting examples where they've been actually able to identify people that a company has laid off to say, “You could have taken 10 of those 30 people you laid off to meet the demands of these strategic goals that you have for the future.” They've been able to show and illustrate how you can do that when you get clear on skills.

Another kind of company in the same sort of skills compass family is Future Fit. They've gotten into the business of what is known as out-skilling, helping learners navigate a layoff. A lot of employers are now realizing they know in the future that they're going to have to maybe lay off a thousand workers. The optics of that are terrible in general. And so how do they do better for their people?

Now what some of these companies are doing is they're bringing in a group like Future Fit to get clear on skills and help some of these learners map to a better job elsewhere. They're actually helping those workers understand their competencies and skills, and then help them identify different pathways forward, and also the kinds of education providers in their area that can help them fill their different skills gaps. These are the kinds of things that even if they're more nascent innovations today, they give me great hope for the future.

Mike Palmer: Fantastic stuff, really interesting book. I do recommend it, Long Life Learning: Preparing for Jobs That Don't Even Exist Yet. Michelle Weiss, senior advisor at Imaginable Futures, a really interesting person to get to know a little bit. If folks want to learn more about any of this, if they want to understand how to get the book or how to find out more about you, do you have any recommendations for them?

Michelle Weise: Sure, I am on both Twitter and LinkedIn with the handle RW Michelle, or they can just go to my website, which

Mike Palmer: I appreciate the time you've been able to spend with us today, Michelle

Michelle Weise: Thank you so much for having me on.

Mike Palmer: And for our listeners, if you like what you're hearing, tell a friend, subscribe. We love you coming back. Thanks for listening. This is Trending in Education.

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