Throughout her career, Sae Schatz has been deeply immersed in the application and advancement of learntech. She currently serves as director of the Advanced Distributed Learning (ADL) Initiative, a government program for science, technology, and policy related to distributed learning. She’s also an editor of and contributor to the e-book Modernizing Learning: Building the Future Learning Ecosystem.
Prior to joining ADL, Sae worked as an assistant professor and an applied human-systems scientist, with an emphasis on human cognition and learning, instructional technologies, adaptive systems, human performance assessment, and modeling and simulation.
In this third episode in our seven-part series on the frontiers of learning technology, Jeff talks with Sae about ADL’s “building the future learning ecosystem” initiative and the key ideas behind it. They also discuss the role of Total Learning Architecture (TLA), data, the future of learntech, and suggestions for learning businesses to capitalize on these new technologies.
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[00:29] – Intro and background info about Sae Schatz
[01:31] – About ADL, the work it does, and Sae’s role
Sae shares that the Advanced Distributed Learning (ADL) Initiative was created in the late 1990s to encourage the use of online learning, across the government and society and, in particular, defense.
Originally, a lot of the work focused on what we now see as “traditional” e-learning—learning management systems and SCORM (Shareable Content Object Reference Model), for example.
They also do a lot of work with digital learning interoperability, data, emerging platforms, and the learning science for different modalities.
The Evolution of xAPI and cmi5
[02:58] – How are xAPI or cmi5 evolving? What other specifications are you working on?
Besides xAPI (the Experience Application Programming Interface) and cmi5, Sae says the ADL is working on other interoperability specifications, dealing with learning content metadata, learner records, and competencies. These are all ways to be able to put building blocks together.
She notes we have been talking about these emerging concepts for years: lifelong learning (see Chris Dede’s discussion on the 60-Year Curriculum), ubiquitous learning across all different kinds of platforms, augmented reality, virtual reality, extended reality, data-driven learning, AI-driven personalization, learning analytics, and competency-based learning.
The question really becomes how do we take all these amazing puzzle pieces and put them together? And that’s really where all of these different interoperability specifications come into play. Think of that sort of like Lego blocks. The reason that Legos work is because you have the exact same little connections in the same sizes in the same places. But the blocks themselves can be all different shapes and formats, and you can build your own little castle, however you want it to be. The art is in the way that they’re interoperable.Sae Schatz
Frontiers of Learning Technology: Building the Future Learning Ecosystem
[05:45] – When you think about the phrase “frontiers of learning technology,” what comes to mind?
Sae shares the ADL is very focused right now on building “the future learning ecosystem”—the K-to-gray, technology-enabled, data-driven, heterogeneous system of systems. Imagine a technology-enabled lifelong continuum of learning that’s driven by data, that’s personalized, and that incorporates good-quality learning science.
The ADL recently published the (free and downloadable) e-book Modernizing Learning: Building the Future Learning Ecosystem, which talks about the rationale and then defines a roadmap across technology, data, and data issues like privacy and security, learning science, and organizational dynamics.
[07:33] – How do we make progress towards building this future of learning ecosystem?
Sae explains that won’t get there all at once; it has to be a journey. But we don’t have to get all the way to the end to begin to realize some benefits.
She shares three steps to making progress:
- Data interoperability. Currently we have learning islands that are pretty siloed. One particular course doesn’t know who you are or what courses that you took beforehand.
We don’t have a way to have that relay race, where we’re passing the baton of data, of information about the learner from place to place to place. Instead of having these learning islands, we want to connect them up. We want to get to that modular open systems architecture, that interoperability.Sae Schatz
- Semantic interoperability. This helps us get a common currency across the different islands, and it’s where competency-based learning comes into play, so can all work off a common, machine-readable list of the different knowledge, skills, abilities, and job requirements.
- Upskill learning professionals. We’re not living on our own little island anymore; instead we have this whole world we need to navigate. We need to have learning engineers—people who understand technology, learning data, learning analytics, learning science for learning at scale, lifelong learning, and all of these other capabilities to use our toolset to the best effect.
Total Learning Architecture (TLA)
[11:25] – Would you tell us about the Total Learning Architecture (TLA) concept?
TLA is about data interoperability and the enabling infrastructure to make this big vision work. Specifically, TLA is a set of IT and data specifications and standards for learning technologies. TLA is open and standards-based, so anyone can implement it in their systems. It’s the blueprint detailing where those little circles on the Lego blocks go.
The Total Learning Architecture incorporates four data standards.
- Getting good-quality, granular runtime data out of your learning management system, simulator, electronic grade book, etc. (using xAPI)
- Knowing and defining what the learner is doing (the course, video, simulation, etc.)
- Defining a learner’s education, experience, and employment history (the three Es of their life) to know their capabilities
- Identifying competencies (as a common currency)
Sae acknowledges this sounds simple, but, when you think about how to connect hundreds of systems, ensure security and privacy are maintained, abstract information, etc., it becomes complicated quickly. That’s what TLA is meant to help with. TLA is a blueprint of IT and data specifications for learning technologies to build out the ecosystem. It’s also the reference implementation and prototype to make sure everything works correctly and that the business rules are correct.
[15:40] – How does an organization start moving towards Total Learning Architecture?
ADL has produced a system administrator guide that lays out specific steps to implement.
Sae recommends the following to start:
- Get good runtime data out of your training, education, and other learning and development systems. We need to do a good job of measuring what individuals are doing, what they’re learning, and what their performance looks like. Using xAPI to do this puts things into an interoperable format.
- Start connecting different training and education opportunities together. For example, if you have a learning management system, a microlearning app for smartphones, and videos, connect those so you’re getting rich data across your own learning islands. Also, implement metadata that describes what those different experiences are. ADL is currently working with the IEEE Learning Technology Standards Committee on the P2881 standard for learning metadata.
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The Near Future of Learntech
[19:47] – Which trends in learntech have the most potential for significant positive impact in the next three years or so?
Sae says it’s not about the technology; it’s about how we use the technology. We need to figure out how to creatively—using good-quality, evidence-based learning science—make the best use of what we have.
She’s a big fan of Ruben Puentedura’s Substitution, Augmentation, Modification, and Redefinition Model (SAMR) Model. The first stage is simply replacing the old way with new technology.
The More Distant Future of Learntech
[22:29] – What might learntech look like in the more distant future?
Sae sees so much possibility with learntech and doesn’t think it’s going to happen in a too-distant future—it will certainly happen in our lifetimes.
Going back to the idea of learning ecosystems, she explains there is a childhood-through-lifelong-learning continuum that is technology-enabled with many different platforms. All the platforms collect data about your performance and what you’re experiencing so they’re able to then create individual profiles and truly personalize learning and development.
This would integrate education and employment, as well as other experiences that shape you as a person (being a parent, climbing a mountain) and may get to some soft skills and other important competencies that are hard to measure. And competency-based learning comes into play, so the organizations and the learners themselves are able to see what the learners can do.
We’ll have humans and AI working together helping to complete tasks. This will ensure we have truly effective learning, not that you’ve spent an hour in a course or four years getting a degree to show you are qualified. Rather, this will be based on your performance and where you need to go in your nonlinear journey.
Sae also imagines this is tied into the workforce, so that it’s an integrated system, and we’re able to connect a person’s capabilities with certain jobs. The better matchmaking will help us solve unemployment and underemployment and fill gaps.
Ideally, we would have a meritocracy, helping to elevate people and give them a clear pathway forward. She imagines the systems all driven by data where individuals, organizations, and society can leverage high-quality data about individuals to better inform learning, development, employment, and lifelong planning.
Getting Learntech Right
[27:02] – If we get learning technology right as society, what’s the good that we might see? And what are some of the things we need to do to make sure that we get learntech right?
Today every single individual is being asked to do a broader range of things and, typically, at higher levels of sophistication.
The pace of change is just constant. The technology you are using today, the landscape, the world today will probably be completely different in five years. So we need to be constantly learning, unlearning, relearning. And the only way we can cope with this increased breadth, depth, and pace is to have this flexible learning ecosystem around us to help us get to this constant learning and development that we’re going to need.Sae Schatz
She says we need to make sure that we’re doing a better job of capitalizing on our talent. Right now we do a pretty bad job of measuring our human capital and personnel readiness. We can do a much better job if we can identify individuals’ capabilities, where our gaps are, and what our jobs or tasks are and then make sure we put the right person in the right place at the right time, giving that person the right development experiences when needed. We can start to move towards more of a meritocracy for job placement instead of relying on propinquity or social advantage. We can start to do a better job with matchmaking for the people with the jobs.
Sae notes this isn’t just her vision. The Chamber of Commerce has an initiative to build a nationwide talent pipeline. They’re bringing together businesses and government organizations and working on the foundational data standards—those Lego block blueprints—so we can make this happen.
Learn more about the Chamber of Commerce’s initiative in this video.
Getting Learntech Wrong
[31:15] – If we get it wrong, what are the dangers that learning technology might bring?
Sae stresses this is a really important thing for us to be asking because there are some major pitfalls, and, if we’re not careful, we could cause damage.
Below are some ideas to consider to avoid getting learntech wrong:
- Information overload, disconnected experiences (lack of semantic coherence) lead to cognitive biases.
- Incomplete, degraded, or otherwise untrustworthy information is problematic.
- Equity is important. We need to make sure that we have high-quality learning experiences driven by high-quality learning data available to everyone.
Advice for Learning Businesses
[34:33] – What advice do you have for a learning business looking to effectively use learning technology and trying to decide what to focus on and invest in in the near term?
- Start with your data, and make sure you are capitalizing on it. Data is the new gold. Find the common standards that are being used across industry, academia, and government. You can use the standards they’re working on at ADL or standards from other organizations such as the Chamber of Commerce and IEEE organizations.
- Shift to a competency-based learning approach. This allows for a meritocracy, and it’s more motivating for people. You’re better able to align the training and education with the actual jobs or the outcomes you need, and it provides a common currency across different data systems. Just as with the data standards, you don’t have to invent this on your own because so much of this already exists. For example, the Department of Labor’s O*NET (Occupational Information Network) has competency frameworks.
- Upskill your learning facilitators. Facilitators means your designers, developers, and deliverers—the people who work across your learning franchise. Help them learn how to embrace learning data, learning analytics, improved assessment, and learning technologies to get the most out of them. This will help you get the most from individual learning, learning at scale, and eventually from the lifelong learning ecosystem.
[37:59] – Wrap-up
Sae Schatz is the director of the Advanced Distributed Learning Initiative and editor of the e-book Modernizing Learning: Building the Future Learning Ecosystem. We encourage you to visit the ADL Initiative’s Web site, as they make a wealth of resources available to help you as you think about your organization’s learning technology needs.
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[39:56] – Sign-off
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