Personalize Learning to Create Engaged High Performers · 2020. 11. 4. · The Power of Personalized and Adaptive Learning By Maria Ho Organizations often view personalized learning

© 2020 ATD | ALL RIGHTS RESERVED

Personalize Learning to Create Engaged High Performers

1PERSONALIZE LEARNING TO CREATE ENGAGED HIGH PERFORMERS© 2020 ATD | ALL RIGHTS RESERVED

A one-size-fits-all approach to talent development doesn’t work. Personalized and adaptive learning enables employees to focus on learning the skills most critical to their performances and to build workforces with continuous learning mindsets that agile, innovative companies need to remain competitive in today’s environment.

ATD Research found that high-performance organizations are far more effective at personalized and adaptive learning. Its Personalized and Adaptive Learning: Shaping Employee Development for Engagement and Performance report notes that half of talent development professionals use some adaptive learning, and its use is expected to grow.

Because adaptive learning focuses on the gaps in a learner’s knowledge, it saves the organization time and money. For the individual, there may be less frustration because they don’t need to spend time on content they already know. And because personalized learning takes into consideration interests and experience of the learner, the individual is likely to be more engaged with the content.

Adaptive Learning 101By Anirudh Hindupur

“Adaptive learning” is a popular buzzword in today’s discussion about education and training. But what does it really mean? What can it do? What are its limitations? Let’s explore the potential of this learning concept.

What Is It?The fundamental idea behind adaptive learning is that the learning system adjusts to an individual’s learning requirements. Adaptive learning uses algorithms to tailor a learning experience to an individual. It delivers customized learning activities based on the learner’s mastery in a given topic or course. This is not a new concept, but in recent years technological advancements driven by the growing demand for customized learning experiences that cannot be achieved on a large scale using traditional methods have turned it into a reality.

How Does It Work?The technology behind adaptive learning involves artificial intelligence, psychology, psychometrics, and machine learning. Most systems use a similar framework.

Knowledge MapThe instructor creates a knowledge map that links the topic areas within a course and the concepts

within each topic area, showing how important concepts are related to each other. This is often referred to as an inventory of knowledge and is organized using various interconnected nodes to make it easier to locate information.

Below is an example of a simple knowledge map for a course involving six concepts. For the purposes of this example, let’s name them concepts A, B, C, D, E, and F.

For learners to be able to learn concept E, they must first master concepts A, B, and C. If learners can correctly answer questions from concept F, it indicates they have already mastered concepts A, B, C, D, and E.

Knowledge CheckOnce the knowledge map is in place, the adaptive learning system performs a knowledge check or knowledge test to identify which concepts the learner already knows, just as a personal tutor may evaluate the learner’s knowledge by asking questions. In an adaptive learning system, the learner takes an online assessment or quiz that is graded automatically.

https://www.td.org/insights/the-abcs-of-adaptive-learninghttps://www.td.org/research-reports/personalized-and-adaptive-learning-shaping-employee-development-for-engagement-and-performance


Learning PathUsing the knowledge check, the system learns about the learner’s current level of mastery for each topic. The learning program combines this information with the knowledge map to form a learning path. This learning path focuses on the concepts the learner has yet to master.

Case StudyUsing this example, suppose that a group of learners must master six concepts in a course. Sara’s quiz results show she is already an expert in concepts A, B, and C, knows little about concept D, and knows nothing about concepts E and F. The adaptive learning system will create a custom learning experience for her that covers little from concepts A, B, and C, quite a bit from concept D, and even more from concepts E and F. On the other hand, Sara’s classmate, Ravi, is familiar with concept A but not with concepts B through F. The system will ensure that Ravi sees more content from the areas in which he is least proficient.

What Are the Benefits for Instructors and Learners?One of the goals of adaptive learning is to make the learning experience scalable and accessible to a wider audience and with less effort on the instructor’s part. An adaptive learning system can replace many of the routine and repetitive activities that instructors perform—for example, providing information, administering tests, grading, and analyzing student performance—leaving them free to address specific learner difficulties and teach higher-order thinking. Instructors can monitor learners’ progress, and the system can be designed to alert instructors to when a learner is struggling with a concept and needs additional in-person coaching.

An adaptive learning system customizes content for the individual learner, focusing only on the content the learner needs rather than presenting the same material to all learners as traditional learning methods do. The learning system may also be self-paced, which allows learners to go at their own speed and practice activities as many times as required. Remote learners and learners who do not have access to traditional classrooms can benefit from this learning method.

What Are the Limitations?Adaptive learning tends to work well for lower-order thinking. It provides information and allows learners to practice straightforward problem-solving. When the material demands higher-order thinking and open-ended responses, this system is less effective.

The limitations of auto-gradable question formats used in most adaptive learning systems play a part in this. For instance, in quantitative subjects like math or physics, the quizzes focus on learners reaching a final correct answer; however, in these subjects, most of the learning occurs as learners work through the steps and calculations that lead to a final answer. One way to overcome this limitation is to structure questions in such a way that the system evaluates learners based on the steps and calculations they take to arrive at the final answer. Even better, an ideal system would provide for multiple approaches to solve the same problem.

Just as a good personal tutor monitors the approach a learner takes to solve a problem, an effective adaptive learning system guides the learner through the steps required in problem-solving. An efficient feedback system that points out exactly where the learner has gone wrong is essential in adaptive learning. This feedback system can be automated to provide relevant question-level and step-level hints to help the learner.

Learning depends on more than just presenting content. Learners also benefit from the social factors that accompany the traditional aspects, like being exposed to a variety of viewpoints and interactions with instructors and peers. A combination of a good instructor and the technology that adaptive learning offers strikes the perfect balance for a well-rounded, effective learning experience.

By using learner feedback, algorithms, and instructor coaching to deliver personalized, customized, targeted learning experiences, adaptive learning is an


excellent tool to advance a learner’s knowledge and complement traditional teaching methods.

This article appeared as an ATD blog post on August 7, 2019. Anirudh Hindupur works with ansrsource (ansrsource.com), a learning design company. He started as a content developer for mechanical engineering titles and, after working in various roles within the organization (including as an editor and content programmer), now manages a content programming team that codes algorithmic content for mathematics and statistics. Hindupur holds a bachelor’s degree in mechanical engineering. In his spare time, he plays the bass guitar, dabbles with coding and web development, and streams video games online.

The Power of Personalized and Adaptive LearningBy Maria Ho

Organizations often view personalized learning and adaptive learning as ways to meet the demands of a workforce with diverse learning styles, skill levels, and needs. Personalized learning is any instruction tailored to an individual based on learner characteristics, such as interests, experience, preferred learning methods, learning pace, or job role.

Meanwhile, adaptive learning is a specific type of personalized learning that uses computer-based technology to adapt content to a learner’s needs. Applying algorithms or artificial intelligence, the technology modifies content in real time based on learner behaviors. Today’s adaptive learning platforms take advantage of advancements in computer science, brain science, and psychology.

To better understand the state of personalized and adaptive learning and how top organizations are using them to drive learning and business results, the Association for Talent Development and the Institute for Corporate Productivity authored Personalized and Adaptive Learning: Shaping Employee Development for Engagement and Performance, sponsored by McGraw-Hill Education.

The study polled 271 talent development professionals, with more than half representing global and multinational organizations. According to the survey, 83 percent of companies use personalized learning in at least some of their learning programs. The most frequently used criteria for personalization are the learner’s job role and experience level, followed by the learner’s choice of modality (for example, if the learner favors a certain delivery method or to learn alone versus with a cohort) and the learner’s preferred pace. The research found that personalization is effective when it comes to developing leaders and future leaders, including high-potential employees and

midlevel managers.

Adaptive learning use is less widespread. About half of organizations have

experimented with it, and only 7 percent of

organizations have used it in most of their learning programs. However, the survey found that using adaptive learning for many different types of training—including sales training,

first-year employee training, interpersonal skills

development, leadership and managerial development, and compliance training—is

associated with more effective talent development functions and better market performance.

“No one likes being told what to do,” says Adrian Stevens, vice president of learning and professional development at Hewlett Packard Enterprise. “Adaptive learning gives employees the autonomy to decide for themselves what they need and pursue it. We aren’t telling them to take this learning path or that course. Our employees perceive that as our company’s taking genuine interest in them and investing in their growth and development.”

https://www.td.org/insights/adaptive-learning-101https://www.td.org/research-reports/personalized-and-adaptive-learning-shaping-employee-development-for-engagement-and-performancehttps://www.td.org/research-reports/personalized-and-adaptive-learning-shaping-employee-development-for-engagement-and-performancehttps://www.td.org/research-reports/personalized-and-adaptive-learning-shaping-employee-development-for-engagement-and-performance


Stevens continues, “We light a positive fire to inspire individuals’ appetites to become lifelong learners committed to building their acumen. For organizations, that translates to employees who bring greater value to meetings, decision making, innovation, customer engagement, leadership—all the elements that drive business performance.”

This article appeared in the April 2018 TD magazine. Maria Ho is the associate director of ATD Research.

Personalizing Adaptive LearningBy Zach Posner and Christina Yu

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 why 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 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 learner.

To understand how such personalization works in adaptive learning, it’s important to understand that specific philosophies are 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.

https://www.td.org/magazines/td-magazine/the-power-of-personalized-and-adaptive-learninghttps://www.td.org/magazines/td-magazine/the-power-of-personalized-and-adaptive-learning


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 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 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 them to implement the skills they’ve acquired in a training setting.

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

Theory of Deliberate PracticeThe 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 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.

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—benefits both the employee and the firm.

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 advisors) 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 DesignDeliberate practice is a crucial component of adaptive learning technology, and it ensures learners are 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 frustrated with a game.

Adaptive platforms incorporate this strategy directly into their algorithms. For example, if too many questions are


answered wrong in a row, an adaptive platform 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 could 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 could feel self-conscious about his tech knowledge. They might feel isolated, insecure, or even disadvantaged while struggling 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. They could engage with the material, getting affirmation for their strengths and low-stakes training for their weaknesses. Instead of feeling isolated or detached, the employee could engage with the material and hone new skills to continue forward with the company.

Ebbinghaus Forgetting CurveThe Ebbinghaus forgetting curve suggests that learners must commit something to long-term memory if they are to truly learn it, 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 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 personalize corporate training, which 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.

This article appeared in the January 2018 TD magazine. Zach Posner is a senior vice president 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. Christina Yu works for McGraw-Hill Education Learning Science Platforms, a venture that applies artificial intelligence to learning through adaptive technology.

https://www.td.org/magazines/td-magazine/personalizing-adaptive-learninghttps://www.td.org/magazines/td-magazine/personalizing-adaptive-learning

TechKnowledge 2021 is no typical virtual conference; it’s a yearlong virtual learning experience.The conference week—January 25–28—is just the beginning. During the conference week, experience keynote speakers, super sessions, virtual classroom sessions, hands-on learning labs, byte-sized learning sessions, networking events, an exhibitor showcase, and a hackathon. The learning continues throughout the year with workshops, toolkits, speaker closeups, networking events, and more.

Don’t miss this unique experience.

REGISTER NOW
https://techknowledge.td.org/registration

Documents

Personalize Learning to Create Engaged High Performers · 2020. 11. 4. · The Power of Personalized and Adaptive Learning By Maria Ho Organizations often view personalized learning