Do you just want to get that back to that first slide? Oh, come in right. Applause.
Hi, everyone. Welcome you.
Welcome. Welcome. We're so happy. You're here. You
Thanks Charles for picking up our intro there as folks are coming in, if you can just use the directions on the screen to introduce yourself so we know who's here you
So real quick, one one twist on this introduction, given that today is All about skills validation, we do want your name, your organization and your role, but don't forget that last little bit, one surprising skill you have, what is a hidden talent or a superpower that you have, maybe it's not been validated by anyone other than you, and that's fine, but
oh yes. Improv comedy. Matthew Aranda, I endorse. What else. Kristen Stryker is a super organizer and shepherd. Can sing, Sue Meg, yar, apologies if I'm mispronouncing your last name. Learned to whitewater kayak later in life. I love that I'm seeking to develop some new later in life talents myself.
Cards, fire, baton twirling might need to be that. Oh, Heather, I didn't know you rode horses. Learn all kinds of surprise skills here.
Limbo. Marty Reed, general handyman, Marty, what was it? We always used to introduce you as when we were working on the integration stuff, we had a funny friendly neighborhood. Data janitor, your friendly neighborhood data janitor, good to see everyone. Good to see you. Been a while. It's been a minute.
Awesome. Well, please keep those introductions coming as those of you who are just joining us, so we can kind of see who's here as we go, and we're going to go ahead and get started with our main event here today. So welcome. Thanks for coming. You are at the innovations in skills validation virtual event, so hopefully you are in the right place with us, and even if you're not, we hope you stay. My name is Meghan Raftery, and I'm with education Design Lab, and I'm going to be a bit of your host for the day, but we do have some additional folks here and some presenters that you'll be hearing from as we go along. And before we begin, we do want to make sure that we just say a big thank you to our grant sponsor, to Walmart. I think we may have even a few people here with us today who really supported this work and invested in skills validation generally as well. We're really proud to be partnered with them and to have this work to showcase because of them today. So our other presenters here today, I don't think I saw Naomi. She is in Tampa, so you can imagine that she is, if she joins us, maybe not as present as she would like to be. But we did want to give a chance to shout out to her investment in this work and all that she's done to support the skills validation network. You'll also be hearing today from Tara Laughlin, who oversees the the project of X credit. As you know, I know a lot of you know her, and she's going to be telling us kind of the framing of this event, and also sharing one of our prototypes. And we also have Nishita, another education designer, on the team, and my partner, who is going to be talking about our one of another one of our prototypes today. So you'll get to hear from a few of us here on the team as we go. As you may know from when you registered, we have a couple of objectives that we're hoping to accomplish today. We're hoping that you will actually leave this event with a couple of really important key things. We want to help you to understand where you and everybody else who's here is on the kind of skills validation continuum. Where are we, individually and collectively in our work with skills validation, we're going to show you a couple of new ways to validate skills that we have prototyped, because we want you to be able to kind of hear our learnings from those methods and develop a next step for your own self. So we'll be showcasing our methods, but the real purpose of today is to show you new ways to validate skills that you might be able to take away from to put into your own work. So that's our key mission today, as we go, and you'll let us know if we get there as we go. So we'll be doing that by doing some framing, talking about skills validation, how we define that, and what we mean by that in the labs work. We'll be doing a survey together just to get some information from you before we move on to our three prototypes. I'll be talking about skill story, which is a self assertion prototype. Nishida will be talking about skills quest, which is a skills demonstration prototype, and then Tara will share the gig translator, which is a, oh, I just forgot my word experience translation prototype, and then we will get to have a chance to check in with you to see what your next step is that you've identified by the time we're finished. So before we do that, we wanted to just take a quick poll to see where you are in the skills validation continuum. So I'm going to launch a little short poll, and you can see on the screen where those four choices come from, as you read those descriptors which best describes your current position on the skills validation continuum, just so we can get an idea of your level of understanding what's a pretty skilled group From the results I'm seeing so far.
Also, I just want to point out the meta nature of this activity, because you are in essence validating your own knowledge of skills validation, or you are self asserting your own knowledge of skills validation. We like to get, we like to get meta.
Panda, fantastic, okay, responses slowing down a little.
All right, let's take a legacy. So much interest. At least of the group identified interested as their category.
Let's see if we can take a look at where we all fall here. Can you all see that? There we go. So we've got about 50% in the interested category, about 17% in early implementation and about 33% in advanced implementation among the group here. Awesome, fantastic, super helpful. All right, I'm gonna go ahead and kick it to you, Tara, to do some framing for us.
All right, fantastic. Hi everyone. Tara Laughlin, Director of skills development and validation for the lab, and before we get into our insights and our prototypes, which we're very excited to share with you. I do want to give just a little bit of framing for why this work matters and also how we at education Design Lab got to this point. So what you're seeing on the screen right now is kind of a high level problem statement, as I'm sure many of you know, there is a growing movement to elevate skills alongside degrees as a means of communicating what any person knows and is able to do. However, we do believe that an abundance of unsubstantiated skills assertions does pose a risk to stakeholder confidence, adoption and sustainability of all skills based efforts, right? And so through our skills validation work, the lab is seeking to tackle this problem. Go ahead. Megan, so with funding from Walmart, the lab has established the Center for skills validation, which is aiming to enable skills validation at scale. And our vision for the center is, as you can see here, to provide an end to end suite of all kinds of stuff, tools, resources, research, insights, technical assistance, to build the capacity of the field at large, multiple stakeholder groups to be able to capture the value of all skills gained through work and life experience. We're doing that through the five work streams that you can see to the right. So as I mentioned previously, we do serve multiple ecosystem stakeholders, including opportunity seekers, anyone seeking an opportunity right that might be a learner, a worker, or anyone seeking that economic mobility in their lives, employers, educators and workforce, intermediaries and so these stakeholders, the center offers Megan, if you'll go ahead and click and you're going to see the purpose of each bucket transform as we go So first R and D, which offers continued research and experimentation to learn what works and what stakeholders need, tools for delivery and scale of skills validation solutions, an expanding ecosystem for integration, automation and access to skills validation offerings, a menu of services for capacity building and support in skills validation initiatives, and underneath, stretching across the bottom, down there, you'll see ongoing thought leadership to increase awareness, understanding and adoption of all of the work going on in the center. But today, we're going to zoom into the labs, R and D efforts. So everything you see today lives within this bucket of work and one effort in particular, in particularly, actual, actually in particular, actually, is the work of our skills validation network. Go ahead, Megan, so quick intros. There are actually SVN members on the call today who have heard this 100 times before. But for those of you who are new to this, this initiative, the skills validation network, is our collaborative that's important, collaborative r, d engine to dream, build and test innovations in skills validation, such as those we'll be sharing with you today. You can see the mission of the network here on the right, we are seeking to expand the methods, tools and opportunities available to validate skills gained through work and life experience, specifically for those individuals who are skilled through alternate routes, or stars, a term coined by opportunity at work. And we want to do this by using education, design labs, frameworks and tools and the collective resources and expertise of our fantastic network members. And you might be sitting there thinking, I want to get involved. How do I become a part of the network or stay tuned into what the network is doing. Don't worry that information is coming. But here you can see all of the phenomenal partner organizations who jumped on board almost two years ago when this network launched, along with a five person team who have been guiding this network forward. So getting into the work a little bit, you can see here the beliefs that are driving the network's innovations forward. We believe that because individuals are diverse and their experiences are diverse, the methods through which they can demonstrate and validate their skills must also be diverse. And so what do we mean by that? Well as a system, we have an over reliance on assessment as a method of validation. We also have an over reliance, which Megan will talk about in a little while, on self assertion as a method of validation. Both of those have a role to play, and we want to build on top of that so that we can take advantage of the myriad of other data sources and information available which can shed light on what people know and can do. So through an iterative process, actually, back it up just a little bit. Yeah, let's stay here, through an iterative process, over the course of several months, the network brainstormed, synthesized, expanded, narrowed, refined our ideas, which ultimately led to our first breakthrough, which you can see here on the screen. This is our draft formula for defining skills, validation methods. And I do want to say on its face, on the surface, this may not seem particularly shocking what we're suggesting here, but I'm going to tell you how we're thinking about this that makes it useful for innovation. An effective way to validate someone's skills must have two key components, and that is a what and a how. So the what is the source of evidence that you're looking at to validate the skills and the how is the method through which you are actually analyzing that evidence to make sense of it? So as a common example, take yourself back to high school, hopefully a non painful, particular experience, and imagine that your teacher assigns you an essay. When you turn your paper in, the teacher now has her what the artifact, right? That's the source of evidence of your skills. But the existence of that essay on its own does not actually validate anything. It's a key piece, but the teacher then needs to apply a strategic method of analysis and evaluation to that essay to determine the presence or absence or level of particular skills. So that analysis process represents the how. So again, obvious example, common context, but what we did with the network was we used this formula as a foundation to begin to innovate both of these components. What can be substituted in for that, what and what methods can be leveraged for that? How that are a little bit different, that are things that maybe haven't been commonly attempted before. And so yes, thank you, Megan, so the network has worked to off of this formula prototype, three such methods, and within each method a particular prototype, which we'll be showing you today. These are based on the three methods of validation that we call self assertion. You can see that on the left experience translation and skills demonstration.
There's a lot going on in this slide we can make. We will be sending out a recording as well as some follow up materials so you can dig into this deeper, but suffice it to say that a great deal of intentionality went into these prototypes. We followed education, design labs, human centered design process. You can see those phases in green, purple and blue, no blue, purple and yellow across the top. And with the time and effort voluntarily given by our SVN members. You can see all of the touch points represented by the cute little handshakes and the volume of tools and assets represented by the the it's like a hammer and a wrench, pretty small on my screen, but all of the tools and assets produced throughout the process. And importantly, as human centered designers, humans, stakeholders, need to be at the center of that process. And so what you can see on the screen here is the way we brought that value to life. We did engage deeply with three central stakeholder groups, employers, stars. We have an other here, because there were a variety of folks, including career counselors, that we met with, talked with and got feedback on our solutions along the way. But this is something in particular that I wanted to highlight, that we're very proud of. So we can't wait to share these prototypes with you, but before we do, we are asking for your help. So we and Megan in particular, are deep in the process of reimagining what the next stage of the skills validation network is going to be right? We've got these great prototypes. That sort of phase of work has come to a close, but we're not done right? We are continuing to go and to build upon what we've done so far. So we are seeking your input to help inform the direction of the skills validation network by sharing your needs and preferences, to be super clear, filling out this survey, you're not signing up for anything, right? You're not raising your hand to join the network, but more so just informing the direction of our roadmap of where we might go next. So we're going to take about a three minute pause, and we're going to ask that everybody on the call today and all of those watching the recording later, scan the QR code. I believe one of my colleagues is yes, Nishita has dropped the link into the chat. There's only three questions on the survey, so we'll take just a moment and about three minutes to fill those out. Thank you all in advance. So much for your input.
You Yes, I will say again for everyone in the back and Javier, I know you're joking, but I'm going to say it again. You're just helping us out. This is just data to inform our roadmap. There will be an opportunity to express interest in joining the network, maybe not a multi level marketing scheme. I don't know about that, but that will come later. But this is not that. Oh no, Holly, okay, Holly, let me make a note. Maybe we can reach out to you via email, just with the free questions, and try to gather your feedback that way. If that works, Kim, sorry to hear that
we still view skills validation as a pretty emergent space field. There are a lot of directions we can go, but the most important thing is that the direction we're going actually aligns to the things that currently feel difficult, or problems that need to be solved, or needs that need to be met. And so that that's what we're trying to do here, is just Be sure we are proceeding In a useful direction.
You Oh, thank you so much. We've got our first submission. My inbox is about to hopefully blow up with all these responses that we've gotten. Our first response
from Alex, by the way, Alex, it's Good to have you here.
And we'll take this about one more minute.
Yeah, one more minute. Megan, I'm two minutes ahead of schedule. Are you proud of me? I
I tried to do a slow fade out of the music to be like a real DJ, but obviously I don't have that hidden skill. It was a problem Megan. We
need to award Holly, her, her problem solving validation here. Well done, yay. Thank you. All right, those of you still still going, please finish up. But as you do that, Megan is going to move us forward into the prototypes. Awesome.
Well, this is the part that's the most fun to talk about. So we're really excited to share these with you. We are going to be doing a pretty high level overview of the three prototypes, and but we do want to let you know there's lots of information about the work that went into these, the actual examples of how they work. And so if you have any interest in that, of course, we'll give contact information at the end, and you're welcome to reach out for more detail about any one of the prototypes if you'd like to know more. So basically, what we're going to be showing you is three different examples. Today I'll be sharing about skill story, which is our self assertion prototype. Then I'll be turning it over to Nishita to share skills quest, and then Tara will end us with our gig translator. So you'll get to hear about all three types as we go today and as we go through in the spirit of human centered design, our each of our working groups that develop these prototypes created their own persona, and sometimes personas in order to imagine who might be using these tools. But for today's purposes, we also have three personas that we're going to be using to guide us through these prototypes. So we'll be looking at Jasmine's example in skill story, and then we'll move into Tori for skills quest, and then finally, Sandeep for the gig translator. So you can see here on the screen, and we'll bring them back up as we get to each prototype. These are folks that represent the kind of things that are star personas, the people who are using this prototype, presumably what they're doing, what they're wanting to do, and what they need, and that was very much at the center of both the design of the prototypes and also the insights and learnings that we'll share today. You may have stars as one of your target audiences, but even if you don't, we hope that you'll find the learnings that came from using a persona useful in the context of your work as well. So first, we're going to talk about skills story, which is a self assertion prototype. We've said self assertion a few times, but just so we are clear on what we mean by self assertion. This is the method by which somebody says, I have this skill. It's by far our group has found the most common method of skills exposure. I guess you could say, out in the world, we have lots and lots of methods that we use. The traditional methods we typically think of resumes, job interviews are loaded with self assertions. And what we do know about self assertions is that while people say that they don't necessarily trust a self assertion to be a valid example of a person's skill. They also rely really heavily right now on self assertions, so they don't necessarily trust this method much, except they make a lot of high stakes decisions based on self assertions, both employers and people at other parts of the skills validation continuum. So one of the things that we wanted to do with the self assertion method as a group was to help stars to understand what their skills actually are, or what skills they have that might be needed by employers. So the problem we're trying to address is that stars don't necessarily know how to tell their skills story and Jasmine, we're presuming, is an example of this. Jasmine is a store manager, and she's looking to grow into a corporate role, so she needs to know which of the skills that she's developed over time are useful in that next goal, that next job. So what we know from employers and the work that we've done with them is that job candidates need help articulating their skills. And also in this prototype, we talked to career counselors who told us that they need ways to decrease the on ramp time that they need to invest in a person to understand what jobs they want and what pathways would make them successful. So we also checked in with them to see how they might use a prototype like skill story. So as a brief overview of what this prototype actually does, it's an interactive chat bot that helps stars extract their skills and to validate those skills by telling about their lives. So what we're trying to do is help the star to tell a story, and then from that story, find out what skills we can pull out of that story. The Chatbot is designed to ask a series of simple, targeted questions to learn about the star. The tool is trained to understand input from stars, to specifically identify durable skills that might be evident in that story, and then also to structure an output for the star, so that they can validate their skills and strengthen those self assertions that they've made with evidence. So we want them to be able to know here are the skills that are present in your story, and if you want to make that self assertion even stronger, here are ways that you can actually attach evidence to that self assertion. So through the experience, Jasmine would enter into our existing X credit ecosystem and go through our normal registration process, and we envision this prototype sitting at the beginning of a skills validation experience. So when the person enters into our ecosystem, where there are multiple things that they can do, we wanted them to be able to have this opportunity to tell their stories so we can signal to them what might be a good way for them to validate their skills. So basically, Jasmine would create her account, and then she'd be prompted with, would you like to tell a story to help you decide what skills you want to validate? And through that process of questioning, which would be responsive to what the star is actually inputting into the system, we would then be able to analyze, on the back end, the skills that are present using a framework. There's our how, and then we would be able to say, here are some things that skill story recommends you do next, some skills that you could validate. So first, we would name the skills for them. They could end there. Now I know that's a skill I might have, and I can actually use that when I'm applying for a job, when I'm speaking in a resume, when I'm being interviewed, and we're encouraging them to strengthen that self assertion through a validation method. So we can say it sounds like from your story, that you have initiative based on that. Why don't you take an assessment to validate that skill, or use one of the other methods of validation that we're prototyping. From this long process of developing this particular prototype, we've learned a couple of really important things about self assertion as a method. We've learned that, like we said, that self assertion is currently the dominant skills validation method, but people still have difficulty identifying their own skills. Stars in particular might have this challenge being able to describe the skill in a way that is desirable for employers. We have learned that discovering skills first before a validation experience actually eases the process of identifying and inserting asserting those skills so a discovery process can get the right kind of thinking going that makes it easier to say, Ah, yes, this is a skill I have. We also learned that additional validation is generally needed beyond self assertion. We need another piece of evidence to go on top of the self assertion, to really build trust for people to believe that that self assertion is a true picture of the skill the person has. Stars have told us that they want a user friendly, inclusive and flexible prototype, and that means a lot of different things within this particular tool, that there is a need for this kind of customization that goes along with the needs of the star. And then we also know that to develop this prototype further to take it to a next step, we would really need to increase accuracy of the tool, security of the tool, usefulness of the tool. It doesn't just stop at tell your story. Here's a validation there's quite a bit of training that would need to go into a self assertion prototype like the one that we designed. So what we want to know from you here is, what role does self assertion play in your current positioning on the skills validation continuum. So first, just thinking for a moment, you know we know that self assertion is a dominant method. What role does self assertion play in your current processes? And then as a follow up question, how might you attach evidence to that self assertion? So the first is a thinker, where is self assertion in your world? And second is, how might you attach evidence to that and what's one thing you could do to strengthen those self assertions? We have a couple of ideas up here on the screen, but we'd also love to hear from you in the chat, are there any ideas you can think of of what you could do to strengthen self assertions in your processes?
I also would love to mention that I actually think I saw in the participants our entire working group here today. So a lot of the folks that worked on this prototype, that are super proud of it have a lot of information to share. So if anybody has any follow up questions that they want to ask, there's quite a few people who can answer them as we go, you
I'm just taking a look at some of our responses here, lots of good questions here, one thing we are discovering as we start to socialize the skill story prototype, there are a lot of What's parallel innovations happening out there in the skills validation world, which has been really interesting for us as a team, to kind of say, wow, it looks like we were thinking about this very similarly to other people, how to use AI to enable this type of storytelling. So a few mentions here in the chat about other things you've seen. It is not the same as those in that we didn't work with those teams to develop this, but it sounds like pockets of people were doing very similar things out there in our field while we were developing this prototype. Awesome. Okay, well, I am going to turn it over now to my colleague Nishida, who is going to talk to us about the second prototype skills demonstration. But please do feel free to continue asking questions in the chat or sharing anything that you are thinking about in terms of self assertion in your own work as we continue.
Thanks. Megan, Hi everyone. I'm going to take you all through skills quest and skills demonstration prototype that was created with our working group members and my wonderful co lead, Fabi kaini, who is in the audience right now, and I'll be, yeah, speaking on behalf of all of them and her. So skills quest is a skills demonstration prototype. And what is skills demonstration, right? So it's a method of skill validation that's focused on an individual's actual demonstration of their skills. It's performed for providing evidence or showcasing proficiency. So it can be something like a live performance or a hiring task. So like Megan, started with the problem at the lab, all of our work starts with defining the problem that we're trying to address. And for our work, the problem that we're trying to solve for is that many stars don't have documentation or evidence of their skills, and need a way to showcase their skills in a manner which is recognized and valued by employers. So in this case, let's take Tori's example, who's a young star. She's held several service industry jobs, but is now looking for a stable role in hospitality, but translating her skills just taking all of her varied life experience and translating that into tangible assets for things like hood, as you may or in interviews, is challenging because there's a lack of documentation or evidence. So that's the problem that our group tried to address with this prototype. So we're called the skills quest prototype. It's an app, a gamified app, that's designed to help stars demonstrate their abilities through various interactive workplace challenges. So what happens is that the app engages users by placing them in a group based scenario that mimics real world tasks, allowing them to showcase their skills in a collaborative setting, and I'll take you through a little bit more about it in detail. I wish this was like more zoomed in for you all to actually see it, but stay with me. So the way that this works is users start by creating an account and going through an onboarding process based on the experiences and interests they will receive recommended skills to validate now these skills will be paired alongside several workplace scenarios within which they can demonstrate the skill. For example, Tori might choose to demonstrate resilience, but she will choose to demonstrate resilience in the context of a restaurant, and it can be multiple contexts. It can be a manufacturing unit, it can be like a call center. So the idea is to mimic real world scenarios and tasks. So she will then be matched with other players who've chosen the same scenario to demonstrate their skills, and then as a group, they will engage in multiple 10 to 15 minute challenges where they're given several opportunities to demonstrate that specific skill. And a quick note is that this is a collaborative demonstration and not a competitive one, so while everyone is working together and is in the same room trying to work on the same problem, they each have unique objectives that don't clash with each other. It's more so that they have to collaborate to actually like solve the problem. Once the demonstrations are done, the evaluations will happen to begin with, evaluations will be conducted by trained evaluators and fellow players, but the vision is later that AI can be trained to conduct these evaluations to enable skill and then successful completions will end up with players earning badges, which are embedded with these experiences and demonstrations that they did so that they're able to showcase that to employers with that proof that was not available to them in the first place. So the whole idea is now that they have evidence that they have this skill, and they're able to demonstrate that. We also collaborated with Seth Corrigan, who is an SME in instructional design, and AI to design a sample task. I'm not going to go into the details of it, but Fabi worked very, very closely with him, and is a pro at this. So if you have any questions related to like, what does this look like? We'll be happy to chat about that later on as well. So we learned a lot of things through this entire process of prototyping, and some of these are that one context matters, employers are looking for demonstrations that are within the context of their job industries or job responsibilities, and on the other side, stars are also looking to demonstrate their skills in the context within which they performed already or within which they want to go to. So for example, Tori would like to demonstrate her skill in hospitality, because that's the industry that she wants to go to. So the context within which a skill is demonstrated matters like we are hearing from even the skill assertion group is stars are seeking engaging solutions that offer flexibility and inclusivity. These are really important factors for the experience to be meaningful for them. The third one is, there's a critical need to balance effort with engagement. So there is, there does this need for us to balance like, you know, how much effort are we asking stars to put in and the time investment and versus what they're getting on the end? So just balancing that is important. And the last one is employers favor demonstration and trusted settings, some of which were hackathons or like academic context, because they want context within which performance is naturally validated and credible. So trust is a big thing. The last thing that we want you to do is, of course, I think about how we can design skills demonstrations that enables us to confidently showcase the abilities while providing credible evidence employers trust. So when you think about this problem, how do you think of addressing it? And we've laid down some ideas of you know what we've learned from this experience, but we'd love to hear from you on, yeah, how you're thinking about it, how some of what we have learned from our experience can be translated into some of your ideas? Yeah, do we? I don't know. I don't know how if I'm on time or if I've gone over time. You
have some time if you wanted to share them. We have about three minutes left in this one. Okay,
perfect.
Thank you.
Yeah. So some of the things that came up with us, and some ideas are like we want, we want one ideas, we can identify where individuals are already demonstrating their skills, instead of asking them to demonstrate their skill again. I think that puts all for, like the time poverty issue, or just like putting in more asking starts to put in more effort. So maybe identifying where these are naturally occurring would be great. And the other thing that's really important is also, how can we meet the needs of both stars and employers? Because swearing to either one risks us into creating a solution that's not as effective or adoptable. And the last thing we learned, because we did like a gamified prototype, is we do think, like, when done right, play and gamification can actually be great motivators. It's just in like, how do we strike that balance with you. Know, this needs to be like a serious game as well, so, but yeah, we'd love to hear from you all if, yeah, any of this rings a bell, or If you'll have any creative ideas on how to do this,
we're crowdsourcing your ideas in the chat. How does, how do you see skills demonstration in hiring processes, advancement and otherwise? What types of evidence? What types of processes are you using or familiar with?
Yeah, you can, you can continue in the chat, but I'm going to pass it to Tara again. Thank you. Everyone.
Hi, everybody. Okay, so switching methods again, right? I wrote this in the chat, but what you're seeing here are three prototypes reflecting three different methods of skills validation the network skills validation network came up with a list of eight methods which are in the publication that we are releasing today. It's a companion report with this event, and so a full list of all eight methods are in that document, and by working with our network members and just asking, What are you interested in exploring of these eight methods, that's how we got down to the three and prototyped in those three areas. So the method that we're switching over to now is one that we're calling experience translation, and this is all about validating skills based on documentation acquired through lived experiences and identities outside of the traditional classroom or workplace. So some examples of that here. This was kind of a starting point for our prototype group. We were thinking about parents, about military aligned individuals, gig workers, influencers, caretakers. Another one that came up afterwards was international learners and workers or refugees, and thinking about based on these identities and these lived experiences, what kinds of documentation already exists related to this work? You know, it may not be professional all the time, but it is a form of work. What? What might they already have that can serve as meaningful evidence of those skills. So here we're going to be looking at this through the lens of Sundeep, because the group, the working group in this area, zeroed in on gig workers as our population of focus. You know, again, I identified all of those possible groups who who also need solutions, right? And there's work going on in the military space, in terms of translating through a joint services transcript skills gained in the military to the civilian workplace. So this is kind of trying to provide a similar concept, but in the sphere of gig work. So here we have Sundeep. He's a gig worker using the Upwork platform, and he's seeking salaried employment. He wants to shift out of the gig economy into a stable, full time kind of salaried role. So like many stars, including those that we talked to during our focus groups and design studios, he has gig economy experience, which means he's accumulated valuable performance data from his clients. In essence, he has crowdsourced information about his performance and his skills from all these clients that he served on the platform. However, he struggles to actually take advantage of all of this data about his skills in his career advancement process, and showcasing those skills to potential employers. So that's actually serving as a barrier toward his desired next step. Up in the top right, you'll see the sort of prevalence of this issue. 36% more than a third of Americans do engage in some kind of contract, freelance, aka gig work. And you can see here that workers under 30 who are Hispanic and have lower incomes are over represented in the gig economy, and most of them are relying on this income to meet their basic needs. So finding a way to take advantage of this experience is really, really important. So we set out to design this prototype, which we're calling gig translator. And the idea behind this tool is that it can access and analyze an individual's gig performance data to identify and validate their durable skills. Okay, at the lab, we are all about durable skills. We have our durable skills competency framework. So that's where we focused our efforts here. But here you'll see, embedded in this the what and the how, right? The gig data, that's the what, the source of evidence and the analysis of that gig data represents the how. And we'll talk a little bit more about that based on that work. Sorry, Megan, if you'll go back just for one more moment. They can earn the labs digital micro credentials, which also was the case for the prototype that Nishita just shared to showcase their skills to potential employers. Sample data points that we found that were fairly common across gig platforms, as we did our research were gig, number of gigs, star ratings, reliability ratings, qualitative, client client reviews and other in app achievements. Okay, so what does the experience of going through gig translator look like? Keep in mind, these are prototypes, right? These are things we're tinkering with. These are not fully baked and ready for release into the world. But here is the flow, the user flow. So first Sundeep would log into gig translator and be presented with options for different gig platforms and asked, Which of these do you engage with? Because he's working with Upwork. You can see that highlighted in the purple box, he selects that Upwork is He is then brought to this page, which is an Upwork profile data scraper, and prompted to paste in The URL to his gig profile into this field after doing so and again, apologies that these images are so small, the the scraping process begins and the data from his gig profile is extracted from the platform. At that point, Sundeep is presented with a summary of his data before any actual skills validation happens, just so that he can see that what was pulled out of the platform mirrors what he knows to be true about his profile, right? This is kind of a human checkpoint. At that point, he can click a button to then have that data mapped against the lab's durable skills framework. And so the tool then begins analyzing, mapping those data points to the framework, with the end result being the communication of his results, right, which durable skills? Was there a substantial enough body of evidence to validate and that information can be communicated back with Sundeep. So things you might be wondering, the two biggest challenges that our group had to overcome was, can we actually access this gig data? And if so, can we actually map that gig data to the lab's durable skills framework, Megan? If you click, you'll see that we thankfully found that the answer to both questions was Yes, and what these screenshots here actually show is the testing that we successfully completed on this process. So on the left, we were actually able to build a custom web scraper to pull sample data from the Upwork platform and extract it into, as you can see here, a couple of spreadsheets. Once we had that, we then worked with an NLP. We had help with this. I'm not the one who did it, so pardon my, my I'll do my best to describe this process to using an NLP AI skills mapping tool to map those data points to the labs competency framework. And the bottom right hand screenshot, you can see was the result of that we were able to, in an initial test, get some yeses and nos in terms of which skills were suggested. Now, a lot of work would need to be done to further substantiate and set a threshold level for whether these skills are accurate and truly deserve to be validated. But this, this was a testing process that told us that at the very least at a baseline level, this can be done, which I found very, very exciting. So here are some of the insights that we learned from this process that I want to share with you. First of all, experience, translation, 10 and gig work in particular, tends to be undervalued. This may not surprise you. Job, the stars that we talked with told us that they often don't include anything about their gig work in their application materials for jobs. They don't talk about it. They don't think employers value it, so they sweep it under the rug. On the flip side of that, employers told us that they would value that and that they want to know
about the gig work the individual has done, the nature of it, but also what the gig translator is providing is going beyond just what they did, what it means for them, what was the value of that work in relation to the role that they're applying for so it's currently undervalued. And then all about the data, most of the conversations we had with our stakeholders centered on the data component of this in some way, one being stars and employers, both were enthusiastic about the ability to identify and leverage existing data from various work and life experiences for skills validation, as Nishita mentioned earlier, stars often those skilled through alternate routes, and some of the other identities that they hold tend to be time poor. And so the idea of having to take a course, complete an assessment, or engage in any other kind of new means of providing evidence is often a deal breaker for them. So they really liked the idea of this data set about me exists. How can I just take advantage of that to get my skills validated? Another thing with the data was across gig platforms, but then even moving out of gig work into other identities, military experience, parenting experience and otherwise, that data varies so widely that comparing apples to apples, in other words, normalizing that data is going to be difficult, right this That's a challenge in this space. And then lastly, context matters. So credentials that are based on skills gained through experience, they need to have data or metadata within those credentials that helps to tell that story right where that skill was gained. This kind of circles back to Megan's prototype about the need to be able to tell their story in addition to having that credential. So real quick, a couple thoughts. I know this whole idea of web scrapers and AI mapping might seem far removed from your day to day work, but thinking about the connection points here, here are some ideas. Thinking about where you sit. How might you tap into skills gained through lived experience of who whomever it is that you serve. Think about those lived experiences they bring into any environment with them. How might you tap into that? So some ideas are First, figure out what those lived experiences are, who is represented in the group of folks that you're working with? Is it some of these identities we've named here, or otherwise second based on that, what kind of evidence or other data get creative might exist from those experiences that could potentially signal their skills, durable skills, technical skills, etc. And then lastly, and this was one of our challenges, identify how you can get access to that data and analyze that data to better understand their skills and set them on that trajectory to economic mobility in whatever setting it is you are within. And my hint here at the bottom is, when in doubt, go straight to the source. Just ask them, if you're not sure about what those lived experiences are, what evidence they might have, how you can get access to it. When we talked with the stars, they were pretty forthcoming, right? So, yeah, that's that's how we hope you can take something away from this particular prototype, Megan.
All right, so hopefully, as you were hearing those different examples, you're thinking about how this directly applies to your work, and we've seen some good chatter over here in the chat of some different questions that we'll take a closer look at and get some answers to folks, or even just keep engaging in the dialog, but hopefully you're getting a taste too, for the types of things that the skills validation network is constantly grappling with and talking about. So we don't necessarily come to you with sure answers, but we do come closer to understanding the problem, and that's really what our group has been working with. So hopefully there's something that's piqued your interest. At the beginning of this call, we asked you to identify where you were on the skills validation continuum. And right now, what we'd like to know from you is what's one step that you think you could take to advance your skills validation methods, or, excuse me, your placement in skills validation now, it might not be that you're going to an entirely new level. But even within that level, each of us, as we shared our prototypes, shared some examples of what you might do. So if you have a big insight that you're willing to share with us, we'd love to hear, Oh, this is one thing that I'm thinking of. This is something that I might try. Please do. Feel free to share that with us as we kind of close off here. So there we were. Do you think we have time for a chat waterfall? You think Tara or just let people share as they want to? We have about one minute left.
Well, you're muted. Go ahead and put the next slide up so that it's there, and then let's end with the chat waterfall.
Okay, so what we'll ask you to do is just, if you are willing, type your idea into the chat, and then just wait to hit send. And what we'll do is we'll hit Send all at one time, and then we'll be able to read those responses in the last 30 seconds or so. So type your response, but don't hit enter yet, and then we'll all get to do that at once. And for those of you who are quick or don't have an idea to share, you also see up on the screen some information about the skills validation network. So year one of this work, and year one plus it's a little bit extended from that because of the foundational work is coming to a close, but we are just about to launch year two of the skills validation network. It's the perfect time to join the conversation. So if we piqued your interest today, if you have more to say, if you want to interact with folks who are doing the same kind of thinking, we encourage you to scan the QR code and join the skills validation network. Let us know about your interest. Set up a call with us, and we'd be happy to talk through your position on the skills validation continuum. Your the information you know in this work. So now, if you're ready, send you
awesome ideas, and in the meantime, as folks are hopping, if you would like to reach out to us, of course, you have our contact information from registering for this, but you can also direct any questions about the skills validation network towards me. My email is up on the screen, and we are just so glad that you joined us today and actively participated. We've learned a lot from you already, even in this hour. So thank you so much for participating today. You
great ideas in the chat too. Thank
thanks everybody,
thank you. Definitely reach out if you want to chat. We're we want to learn from you.
