Making Learntech’s Promise Real

As we close out our seven-part series on the frontiers of learntech, we want to help you make sense of what the wide-ranging topics that emerged mean for the future of your learning business.

Through the interviews with Ashish Rangnekar, Sam Sannandeji, Sae Schatz, and Donald Clark and our own reflective episodes, we’ve had the chance to touch on many different aspects of learntech. We covered everything from specific types of learntech, to the growing importance of data, the increasing need for organizations to have an integrated learning ecosystem in place, the essential role of standards in making such an ecosystem work, and, at long last, truly delivering on the promise of personalized learning.

We also explored related philosophical and ethical concerns such as the accelerating pace of change, equity in access to learning technology, and the potential bias in the data and design of technologies.

In this final episode in the series, we offer five suggested actions for how to make the promise of the frontiers of learntech a reality for your learning business: develop a data strategy, grow a learning ecosystem, conduct a feasibility study for extended reality, build a governance structure, and regularly reassess which learntech holds the most promise.

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[00:32] – An overview of the first six episodes in our series on the frontiers of learntech:

• Learntech: The Next Generation
• AI, Data, and Optimism with Donald Clark
• The Future Learning Ecosystem with Sae Schatz
• Bias and Equity in Learntech
• Finding XR’s Sweet Spot with Sam Sannandeji
• Digital Transformation with Ashish Rangnekar

The Challenge and Overview of Five Suggested Actions

[02:42] – There are incredible technologies being developed, and their promise for the future is huge—more effective learning, more efficient learning, learning that’s more broadly and equitably distributed, learning that responds to a specific learner’s needs in a particular situation at a particular time. Making sure those promises pan out is the challenge. What can a learning business do now to make those promises comes true? That’s what we want to address in this episode.

A lot has to happen to get from here to the frontiers of learntech, where those promises of more equitable, accessible, effective, and personalized learning exist, where they are well-executed realities rather than just possibilities. To help with the journey, we’ll offer five suggested actions.

The suggested actions are numbered, but the order isn’t important—these aren’t sequential—and you might not pursue all five actions or all five at once. These are the kinds of actions that might make it into your roadmap for how to make the promise of the frontiers of learntech a reality for your learning business.

1. Develop a Data Strategy

[04:20] – When we asked interviewees for their advice for learning businesses trying to figure out what to do with learntech, what to invest in, what to focus limited time and other resources on, data came up again and again.

When Donald and Sae answered that question, they mentioned data, as did both Celeste Martinell and Joe Miller, VP of customer success and VP of learning design and strategy at BenchPrep, respectively.

Celeste recommends focusing on the richness of the data that you’ll get from your learntech partner or partners. Joe stresses the importance of having a vision and how data as part of an overall approach helps you move from vision to roadmap. Both Celeste and Joe get at some of the areas and key questions you’ll want your data strategy to address, including what data you have and what insights that data can yield.

[08:00] – A good data strategy will:

• Account for all four of the levels of data use that Donald Clark describes in Artificial Intelligence for Learning: describe, analyze, predict, prescribe. The strategy should describe what data you have from what sources, but you have to analyze that data, and then be clear about what you’re trying to use the data to predict and prescribe. The strategy part of a data strategy is about using data to help your learning business meet its goals.
• Address questions you’re trying to answer, based on what’s strategically important for your learning business. Part of using the data to meet your goals will be about formulating the questions you want your data to help you answer.
• Think throughout your organization, not just your learntech data as you document your data sources. For a fuller picture of your learners, you’ll want to marry data from your learning management system with data from your association management system or customer relationship database and other sources.
• Think outside your organization. There are likely data sources that you don’t own but that can give you insight into learners and prospective learners. For example, the Bureau of Labor Statistics and the Bureau of the Census. There are also thinktanks and nonprofits that conduct original research and make it freely available—Georgetown University’s Center on Education and the Workforce and the Pew Research Center, for example. There are also likely data sources specific to the industry, profession, or field you serve. Take time to do the legwork to figure out what’s available and how you might be able to layer that broader data with your internal data for an even deeper understanding of the market you serve.
• Use marketing as a bellwether for learning. Marketing is strong in the area of mining various data sources and using that to drive conversion.
• Include standards in your data strategy. Which standards are you following now? Which standards should you use? The more consistent and uniform your data, the easier it will be to analyze and interpret. The importance of data standards was a key takeaway from our conversation with Sae. Competencies and frameworks can also be part of thinking about standards. If you have data you can tag and track against a competency framework, that’s likely to also help you track and interpret data about what your learners need, what they’re accessing, and where they’re struggling.

Sponsor: BenchPrep

[13:02] – If you’re looking for a learntech partner to play a meaningful role in your data strategy, check out our sponsor for this series.

BenchPrep is a pioneer in the modern learning space, digitally transforming professional learning for corporations, credentialing bodies, associations, and training companies for over a decade. With an award-winning, learner-centric, cloud-based platform, BenchPrep enables learning organizations to deliver the best digital experience to drive learning outcomes and increase revenue.

The platform’s omni channel delivery incorporates personalized learning pathways, robust instructional design principles, gamification, and near real-time analytics that allow organizations across all industries to achieve their goals. More than 6 million learners have used BenchPrep’s platform to attain academic and professional success. BenchPrep publishes regular content sharing the latest in e-learning trends.

To download BenchPrep’s latest e-books, case studies, white papers, and more go to www.benchprep.com/resources.

2. Grow a Learning Ecosystem

[14:18] – Learning ecosystems came up in our conversation with Sae, as well as standards and competencies. Sae co-edited ADL’s e-book Modernizing Learning: Building the Future Learning Ecosystem, and the first chapter offers a definition of learning ecosystem:

We use the phrase ‘future learning ecosystem’ to describe this new tapestry of learning. At the highest level, the future learning ecosystem reflects a transformation—away from disconnected, episodic experiences and towards a curated continuum of lifelong learning, tailored to individuals, and delivered across diverse locations, media, and periods of time. Improved measures and analyses help optimize this system-of-systems and drive continuous adaptation and optimization across it. Its technological foundation is an ‘internet for learning’ that not only allows ubiquitous access to learning, it also provides pathways for optimizing individual and workforce development at an unprecedented pace.

excerpt from Modernizing Learning, co-edited by Sae Schatz and J.J. Walcutt

A learning ecosystem is aligned with the fact that learning is a process, not an event. Learning is, to use the terms from the ADL e-book, not “disconnected, episodic experiences” but “a curated continuum of lifelong learning, tailored to individuals, and delivered across diverse locations, media, and periods of time.”

We like to think that it’s not just a move away from disconnection but a move towards connection. In nature, an ecosystem is a community—living beings interacting with each other and their environment. When healthy, an ecosystem is balanced. No single part of the system is more important than another, and changes in one part of the ecosystem may impact other parts of the ecosystem.

Learning is fundamentally about interactions among human beings and between human beings and their environment. Learning businesses have the ability to shape and influence a learning ecosystem through decisions about the people involved (the learners, the facilitators, the designers), the content offered (the courses, the publications, the community discussions), and the technologies used to support and connect the people and the content.

In short, a learning ecosystem is comprised of five parts: people, content, technology, and the processes and strategies that unite them. But the whole of a learning ecosystem is greater than the sum of those five parts. Learning culture emerges from a learning ecosystem while simultaneously influencing and impacting the ecosystem. Just as culture is dynamic and evolving, the ecosystem too is dynamic and evolving.

The two concepts are inseparable, two side of the same coin, or, to borrow from William Butler Yeats, as hard to distinguish as the dancer and the dance. Arbitrary or overly simple lines of distinction may do more harm than good. Too much emphasis on any one factor—a delivery method or approach to learning or a particular piece of learntech, for example—may negatively impact the whole.

This reminds us of something Ashish said when we asked him his advice for learning businesses when it comes to choosing learntech. He offered three considerations:

1. What’s your goal?
2. What are the changes/triggers driving the goal?
3. What is the solution? What is the learntech that will help you with your goal?

He’s making sure the tools and technologies don’t get overemphasized and recognizes that learntech are just one part of a learning ecosystem.

3. Conduct a Feasibility Study for XR

[19:16] – This is a little narrower in focus than the first two actions we’ve mentioned. Keep in mind that all five suggested actions aren’t necessarily for every organization or for every organization to pursue now. We feel that given the potential for extended reality—whether augmented or virtual or mixed—to make a training or learning environment more closely match the real-world contexts in which learners need to apply knowledge and skills, it’s worth learning businesses getting clearer on what would be involved.

We’re not suggesting you go build XR, not even a prototype. This is a pre-prototype phase, where you look at what would be involved, do a cost-benefit analysis, and try to understand the potential return on investment, the features needed or desired, which type of XR might fit best, and what tools and technology investments would be needed.

A feasibility study approach is in keeping with Sam’s advice on AR and VR. He made a point of saying a side-project, skunk-works prototype often doesn’t give an organization a good entrée into XR. Often it’s more helpful to go in, eyes open, with a goal in mind for the use of XR and a feasibility study backing up trying out XR.

Or a feasibility study that shows that XR doesn’t make sense, at least at this point. Even if the outcome of a feasibility study is to not pursue XR now, the work done to put together the assumptions about costs, value, development time, and uses can likely be updated and revisited, plugging in new numbers, so you can periodically reassess the validity of XR, VR, or AR. The feasibility study route will also help organizations avoid getting sidetracked by the flashy, latest shiny thing, which Sam also warned against.

It would also be helpful if organizations keep the SAMR Model—a framework created by Dr. Ruben Puentedura that categorizes four different degrees of technology integration in learning experiences—in mind (as referenced in our conversation with Sae).

It seems likely, though not necessarily always true, that to justify the time and cost of an XR solution for a training problem or a learning situation, you’re going to want to get into the transformation phases of SAMR. You’re likely going to want to consider modification or redefinition in your XR feasibility study, so that it’s not a cut and paste from how you’re doing things currently, but an exploration of what you might be able to help learners with that you can’t currently.

4. Build a Governance Structure

[24:03] – Your learning business needs some people to be thinking about learntech, not only from a technical standpoint, but also with the goal of understanding the implications of the technologies.

Those implications will run the gamut from budgetary considerations—what will building and maintaining a learntech stack cost?—to ethical and philosophical decisions around learners’ privacy and rights when it comes to what personal data is collected and how it’s used. It might also touch on issues of potential bias in the use of artificial intelligence and other automated technologies.

A governance structure could be charged with determining when interpretability and when explainability is needed around your learning business’s use of AI, machine learning, and other tech automation. We give credit to Christopher Penn of Marketing Over Coffee for the explainability versus interpretability tradeoff, which we talked about in episode 268, but, as a reminder, interpretability is the decompilation of a technology into its source code.

To use an analogy, interpretability is looking over the recipe and verifying the ingredients. Explainability is like tasting the cake rather than looking over the cake recipe. You’ll know how it tastes, and you can probably figure out most of the ingredients. Tasting is a faster, easier, less expensive way to verify the results—but it’s not as rigorous or complete as interpretability. When stakes are low, explainability (just tasting the cake) is often adequate. When stakes are higher, interpretability (reviewing the recipe) may be needed. The time and expense may be justified in that case.

If you have a governance structure in place, you have a group who can debate the tradeoffs and ultimately make decisions about things like when interpretability is needed versus just explainability. Think in advance about the implications of your learntech so you’re better equipped to deal with any issues that arise.

A governance structure could also be involved in considering which standards for data and technology a learning business should adopt and use and what the implications for that might be.

5. Regularly Reassess Which Learntech Holds the Most Promise

[27:11] – We return to a question we asked in the first episode of this series.

Out of what’s on the frontiers of learntech, what holds the most promise for significant positive impact on your learners and your learning business in the near future? Ask that question now. And ask it again next month. And three months out, and six months, and a year from now. Regularly check in on the frontiers of learntech so you can use learning technology effectively to grow the reach, revenue, and impact of your learning business.

Celisa Steele

When we spoke with Ashish, he said we’re in the second inning of a nine-inning ballgame. So there’s lots more to come—plenty of time for more developments and innovations. We need to regularly check in on our understanding of what’s coming.

When we talked with Joe, he recommended looking at how other industries are using technology to see the potential for learntech. Keep an eye on fintech, retail tech, and martech. Look for inspiration outside the learning space.

As you look at what others are doing with tech and as you engage and re-engage with the question of what holds the most promise for your learners and your learning business, your answer will likely suggest additional actions for you to take, beyond the five suggested here. And, if you do anything of the things we suggest, you will, over time, establish a good foundation to refer to when checking back in on this question—the data will help, feasibility studies will help, having governance in place will help, as will having a learning culture and growing a learning ecosystem.

[30:16] – Wrap-up

This is the last episode in the seven-part series on the frontiers of learntech. We hope you’ve enjoyed the series, and we’d love to hear your feedback and suggestions for the future. You can leave a comment below or e-mail us at leadinglearning@tagoras.com.

We’ll resume releasing episodes of the Leading Learning Podcast with a new series starting in July 2021.

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We encourage you to learn more about the sponsor for this series by visiting benchprep.com/resources.

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[33:06] – Sign-off

Other Episodes in This Series:

• Learntech: The Next Generation
• AI, Data, and Optimism with Donald Clark
• The Future Learning Ecosystem with Sae Schatz
• Bias and Equity in Learntech
• Finding XR’s Sweet Spot with Sam Sannandeji
• Digital Transformation with Ashish Rangnekar

Episodes on Related Topics:

• 7 Ways to Build Capacity for Your Learning Business
• Making the Business Case for Investing in Your Learning Business
• The 4 Cs of the Learning Business Landscape

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