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January 2018
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TD Magazine

Personalizing Adaptive Learning

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Personalizing Adaptive Learning

Four theories power the personalization behind adaptive learning.

In recent years, adaptive learning platforms have been heralded as the future of corporate training, and the reason is simple. Today's fast-paced and often uncertain work environment means it's more difficult than ever for talent development professionals to upskill an existing workforce or increase proficiency. They face the constant challenge of training a multigenerational team with diverse skills, abilities, and backgrounds, often spread apart by geographic dispersion.

Advanced artificial intelligence technology rises to the challenge and enables adaptive platforms to find incredible success where other methods fail. By personalizing instructional content, these platforms dramatically cut wasted time, giving talent development professionals a learning tool so adaptive and intelligent that it's like having a one-on-one instructor for every single learner.

To understand how such personalization works in adaptive learning, it's important to first understand that there are specific philosophies embedded in the adaptive technology itself—theories that guide the course's reflexes and content pathways. These theories determine how the platform will respond to individual learners.

To power adaptive learning with personalization adept enough to engage a near infinite range of learners, I believe there is a specific recipe for success—a combination of the metacognitive theory, the theory of deliberate practice, the theory of fun for game design, and the Ebbinghaus forgetting curve.

Metacognitive theory

"Know thyself." That Socratic axiom sits at the center of the metacognitive theory, the foundational cornerstone of adaptive learning platforms. The theory holds that learners learn best when they gain awareness about themselves, and more specifically about the full range of their own knowledge.

We call this self-awareness metacognition. When learners know what they know—and what they don't—they begin to think differently and unlock their potential in numerous ways. By giving learners insight into their own strengths and weaknesses, metacognition illuminates the path to erasing knowledge gaps.

It works like this: As learners progress through an adaptive course, the platform captures data on accuracy, confidence, and time. The platform will then automatically use these data to adjust content to further improve awareness about knowledge and confidence, so learners walk away "knowing what they know."

The application of the metacognitive theory offers talent development professionals an effective one-two punch of efficiency and confidence, which are extremely valuable outcomes in a corporate learning environment.

A core tenet of adaptive learning is the idea that mastery—not seat time—should be the metric for success. Of course, leaders in the corporate world agree. Who wants employees rehashing coursework they could breeze through, especially when they could be maximizing their potential elsewhere? By helping learners know what they know, adaptive platforms can steer them away from wasting time on material that they've already mastered.

So, on the most practical level, the implementation of the metacognitive theory saves organizations valuable time, energy, and resources, making time spent on education as efficient as possible.

Furthermore, by focusing on developing metacognitive insights, adaptive platforms give learners new levels of confidence. And in the corporate learning world, the value of confidence cannot be understated.

For example, imagine a pharmaceutical sales company implements a training program to educate its sales staff on consultative sales techniques. The desired outcome is a team with the ability to implement nuanced and thoughtful sales methods, skills such as establishing trust and developing real human connections with clients.

In that case, merely demonstrating mastery of new techniques could be insignificant if employees lack self-awareness. If, for example, an employee passes training assessments and yet lacks awareness of his own knowledge, he may fall short of the desired outcome because this kind of sales takes more than mastery, it takes confidence. Becoming more cognizant of her own knowledge can boost the employee's confidence, enabling her to implement the skills she's acquired in a training setting.

In addition, when armed with this self-awareness, the employee might even look beyond her own work to mentor others who are weaker where she has strengths. The benefits of confidence ripple out from one team member to her peers, her managers, and the entire staff.

Theory of deliberate practice

The theory of deliberate practice suggests that understanding our weaknesses helps us refine and focus practice techniques. Hammering away at the same problems or repeatedly practicing the same set of skills proves less successful than focusing energy on tackling unknown challenges and sharpening skills outside the learner's acquired set.

Guided by this theory, an adaptive learning platform continuously introduces learners to new content based on an individual's weaknesses, saving time and tailoring the coursework for maximum efficiency. Instead of allowing learners to remain on a treadmill of already mastered exercises or objectives, the platform automatically guides learners toward new ground and fresh material and pushes them to excel beyond the content they've already mastered.

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The application of this theory creates results that apply to every field, often in quantifiable, monetary ways.

Take, for example, an accounting firm that needs to ensure employees develop complete mastery in certain areas before they can embark on an audit in a client's office and bill by the hour. For this firm, time spent on training and development directly affects the bottom line. Every hour spent training employees is an hour that could be spent generating revenue. Therefore, optimizing training time by remediating employee weaknesses—instead of mindlessly reviewing strengths—greatly benefits both the employee and the firm.

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Adaptive platforms use this theory to push learners outside their comfort zone and into more challenging terrain. That kind of stretching builds confidence and can even show learners (and their advisers) strengths they didn't know they had. By pushing the limits, deliberate practice primes employees to excel beyond their own expectations.

Theory of fun for game design

Deliberate practice is a crucial component of adaptive learning technology, and it ensures learners are properly challenged. At the same time, this theory must be balanced by the theory of fun for game design, which suggests that learners are engaged at maximum levels when they feel challenged, but not too challenged.

Adaptive platforms incorporate this strategy directly into their algorithms. For example, if too many questions are answered wrong in a row, an adaptive platform automatically will introduce a question that falls within the learner's demonstrated knowledge base. By seasoning assessments with occasional questions the learner is sure to answer correctly, the platform works to build confidence and increase engagement.

Of course, that is a meaningful concept for all learners. Who doesn't get overwhelmed when challenges are too much to bear? In the corporate world, there are major ramifications for using this theory to increase engagement.

So often in corporate training, companies need to upskill an existing workforce, and in many cases that involves training an older generation of workers on new software or technological upgrades.

Imagine, for example, an industrial engineering firm that wants to transition to a new design software for all projects. There might be an employee who has been with the firm for decades who now must adopt a brand-new technology along with the rest of the employees. This employee might feel self-conscious about his tech knowledge. He might feel isolated, insecure, or even disadvantaged as he struggles to adopt new technology alongside a generation of rookie employees who have far more experience adapting to new software systems.

By implementing the theory of fun for game design, the adaptive learning platform can lower the stakes for an employee like this. He can engage with the material, getting affirmation for his strengths and low-stakes training for his weaknesses. Instead of feeling isolated or detached, he can engage with the material and hone new skills to continue forward with the company.

Ebbinghaus forgetting curve

Finally, the Ebbinghaus forgetting curve suggests that to truly learn something, learners must commit it to long-term memory and that the peak time to do so is just as learners are about to forget it. Adaptive platforms incorporate this theory by using data to predict when a concept will likely slip away from a learner's short-term memory. In that exact moment, the platform will reintroduce the concept before it vanishes, thereby securing it in the learner's long-term memory.

It's easy to see the implications and advantages of learners forming long-term memories during corporate training. For example, imagine a hospital that performs a training course for nurses on new inpatient procedures. It's imperative that these employees retain their knowledge beyond the length of the course. Lives depend on it.

No matter the field, transforming short-term lessons into lasting knowledge means learners are far more likely to implement the concepts they've learned. It means not having to circle back to cover the same material every year. It means no more wasted time rehashing the same concepts.

And, perhaps most importantly, it means the content actually sticks, giving talent development teams demonstrable positive results coming from their training campaigns.

Because, at the end of the day, talent development executives want knowledge retention and measurable success, not simply a workforce that can pass assessments without real-world application in the post-training office. For corporate learning to be a true success—an investment that proves its value, shows signs of change, and improves performance across a wide range of learners—long-term memory formation is essential.

If these four theories are built into their driving algorithms, adaptive platforms make corporate training incredibly personalized, and that creates vastly superior learning campaigns. Learners get the kind of dynamic experience they might have with a one-on-one tutor, even with massive, diverse teams spread across the globe.

And in today's corporate world, that's real value.

About the Author
Zach Posner is a senior vice presient at McGraw-Hill Education (MHE), heading up the learning science platforms team, working to open and license MHE’s technology platforms that allow for learners to learn more effectively and efficiently through adaptive learning to corporations, publishers, and education institutions globally. The platforms include authoring, delivery, and reporting capabilities that not only allow authors and designers to continuously improve their content but also enable instructors to tailor their classes based on learner engagement, progress, and outcomes. Zach joined MHE through its acquisition of Engrade, where he was the CEO and co-founder. Engrade is an instructional management platform that integrates systems, data, and tools into a single cloud-based platform that connects millions of administrators, teachers, students, and parents. Zach led Engrade from corporate formation to growth and exit, and in the process raised several rounds of financing from leading venture capital firms, including Rethink Education and Javelin Venture Partners. Prior to Engrade, Zach led digital activities at media companies and worked in finance and venture capital. In recent years, he served as an adjunct professor at the Annenberg School of Communications at the University of Southern California.
About the Author
Christina Yu works for McGraw-Hill Education Learning Science Platforms, a venture that applies artificial intelligence to learning through adaptive technology. She holds an AB in English and creative writing from Dartmouth College and is completing her MBA at the NYU Stern School of Business. Prior to her current role, she managed a portfolio of higher-ed adaptive products, worked at the edtech startup Knewton, and as a professor of English at Kean University and Southern Connecticut State University.
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Very interesting and informative article. Thank you for posting!
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This is a fantastic article as it clearly demonstrates that personalized adaptive learning technology will lead the future of k-12, higher education, and most importantly corporate training and development.
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