The Ultimate Guide to Personalized and Adaptive Learning
The traditional one-size-fits-all approach to workplace learning simply can’t keep up with business needs. For frontline workers to perform, they deserve right-fit support from day one. Here’s how to use adaptive learning to make that happen.
Two employees may be doing the same job, but they have very different backgrounds. Yet, despite their unique potential, traditional training treats them like they are the same person.
They go through the same onboarding.
They’re scheduled for the same classes.
They’re assigned to the same eLearning.
This homogeneous approach forces employees with proven capabilities to complete unnecessary, irrelevant training, while less experienced employees are always trying to play catch-up.
There has to be a better way. There is a better way!
Solving the problem of cookie cutter training has the potential to redefine the value of learning in modern businesses. And this isn’t a theoretical future we’re talking about. Whether you’re a seasoned learning professional or a business leader, whether you work for a global organization or a small business, you can overcome the shortcomings of one-size-fits-all training right now. And the answers lie in this Ultimate Guide to Personalized and Adaptive Learning.
Before I joined Axonify as Chief Learning Architect, I had only worked for large companies, including AMC Theatres, Disney and Kaplan. I was constantly faced with the challenge of whether I should provide generic training for everyone or try to address specific problems for just a few people. Unfortunately, my limited time and resources resulted in pushing plenty of one-size-fits-all training to the masses while not being able to solve for individual needs. This realization prompted me to explore methods for personalizing learning during my later corporate roles. My efforts led me to become an early Axonify adopter and ultimately bring my insights to the team in my current role. Today, I’m working with the team to leverage artificial intelligence (AI) to drive our technology forward and take personalized learning to the next level.
One-size-fits-all training has failed
Corporate training teams have historically been stuck between a rock and a hard place when it comes to the needs of individual employees. Whether you work in an enterprise with tens of thousands of employees – or you are a mighty team of one supporting hundreds of people – you have a difficult choice to make. Do you provide generic training to everyone in an attempt to provide some type of support? Or do you provide something customized to a small group and let everyone else fend for themselves? It’s a lose-lose proposition for L&D and employees that results in poor results for the entire company.
What’s not working with training
Here are the top reasons frontline employees say their training is ineffective:
They only receive training a few times a year
The training is boring/not fun and engaging
Too much information is thrown at them at once
They only received training when they started their jobs
They are trained on the wrong things and it doesn’t help them do their jobs
PLUS: ~30% receive no formal training and 25% don’t receive training after onboarding
This inability to balance the needs of the individual with the scale and pace of modern business has called the value of training into question. At the same time, executives, now more than ever, recognize the importance of continued learning and development in a business climate where disruption and heightened consumer expectations are the norm. As the workplace is reshaped by AI and automation, the strategies and technology that support employees are falling further and further behind.
Organizations are ready for a new approach
7.8 hours. That’s how much time today’s consumers spend engaging with digital content each day. That’s more time than many of us spend sleeping! With so much content cluttering the digital landscape, people have become increasingly reliant on personalized experiences. And brands like Netflix, Spotify and Amazon are getting better and better at finding new ways to give consumers what they want. The thing is, your employees are those consumers. And their expectations don’t suddenly change when they walk through your doors. They wonder “why is it so much more difficult for me to get what I need at work?” when they compare it to most of the information experiences they have in everyday life. So, we must consider ways to communicate and contextualize a new learning and support experience based on the ways they already interact with technology and content every day.
Every organization now has the opportunity to personalize learning experiences – just like brands do consumer experiences. Rather than requiring everyone to do the same training, a personalized learning strategy provides the right support for each employee when and where they need it.
Whether you’ve realized it or not, the workplace learning community has already started to shift towards strategies that allow for more personalized learning in scalable ways. In fact, there are four prevailing trends within L&D that are paving the way for personalization.
Microlearning
Data
Self-Directed Learning
AI
Microlearning focuses on the delivery of content in short, focused bites. It aligns the delivery of training to the way people actually learn while balancing the priorities of the business with the needs of the individual employee. Yes, it’s often shorter than traditional training content, like 30-minute eLearning modules and all-day classroom sessions. But it’s not the duration that matters. It’s the focus.
Think about it: if content remains trapped in bulky, generic, one-size-fits-all courses, the experience will remain mostly the same for every employee. However, when you break content down to more specific pieces, each intended to achieve a particular result, the opportunity for a personalized experience increases. So, rather than send everyone to a class about safety in the workplace, you can provide resources on the specific safety behaviors that may be impacting an employee right now.
Historically, “learning data” has referred to quiz scores, seat times and completions. This is already changing thanks to the expanded use of data across the modern enterprise. Discussions around the Experience API (xAPI) have learning professionals thinking about how to design their content to allow for more effective and robust data collection on the user experience. At the same time, we are now seeing measurement strategies integrate a larger range of performance-related data. This includes everything from ongoing knowledge and confidence levels to real-world behavior observation and business results.
Data is essential for personalization. And not just any data will do. It needs to be quality, relevant, targeted data that gives us a direct window into the needs of individual employees. The ability to capture, analyze and apply multi-dimensional data from across the working experience provides us with an awesome opportunity to reimagine how we support people on the job.
Many organizations still consider learning a “push” activity; something you schedule people for at a specific place and time. Over the past few years, the concept of self-directed learning—enabling people to select their own training opportunities—has become increasingly popular.
Thanks to the internet, anyone can find learning opportunities on almost any topic using those super-powerful computers in their pockets. This everyday reality has challenged workplace learning and knowledge management teams to rethink how they provide access to information, making easy access and searchability more important. Many organizations are starting to make large volumes of content available to employees while only requiring completion for role-specific or compliance-related topics.
This embracing of self-directed learning has triggered a VERY important mindset shift when it comes to workplace learning: everyone does NOT have to take everything. If the need to check boxes stays at the forefront of learning strategy, generic, one-size-fits-all content will remain a staple. However, once an organization acknowledges that employees have individual needs and preferences, the opportunity to shape and personalize the experience becomes permissible.
AI and automation are already impacting how work is done. Repetitive tasks are slowly being shifted away from human workers and towards machines that can do them more quickly and accurately. This trend will fundamentally change the work experience, focusing people on tasks that are distinctly more human (collaboration, service, creativity, etc.).
L&D is just beginning to recognize both the impact of AI on the workplace as well as the potential for this technology to transform their practices in support of this new reality. As their data capability improves, so too will their opportunity to leverage AI-enabled technology to deeply personalize the learning experience and validate the impact of training on bottom-line results.
5 ways to personalize learning
Like so many newer concepts in the corporate training space, “personalized learning” is often loosely defined. I want to help you cut through the noise so you can clearly understand your options when it comes to creating personalized learning experiences for your employees. Here are five potential methods you can apply to your personalize learning experiences. Remember – you don’t have to pick just one. You can apply any or all of them to increase the value of your support. However, keep in mind that each method has benefits and limitations in terms of how effectively it can support individual knowledge and skill needs within your organization.
1. Audience Segmentation
You already use this method when you assign content or activities based on basic demographic data. For example, you may send every call center agent who represents Product X to a specific classroom session. Or you may make online information available to only retail associates who work in the Brand Z stores. While technology is typically required to execute segmentation at scale, it requires limited administration. This is typically the first step towards avoiding unnecessary, one-size-fits-all training.
BENEFITS:
Delivers a solution that’s simple to implement with most workplace/learning tech
Produces easily understood segments based on clear demographic data
LIMITATIONS:
Results in relatively large and generic groups as segmenting is based on simple HR data
2. Content Branching
This method is commonly applied within online training. It lets an employee view content based on the decisions they make within a module. For example, the employee may be presented with a scenario and, based on their answers to questions, progress down a set path. Another employee may make different selections, which moves them down another predetermined path. While the branch options are limited, this approach reduces the need for “100% slide view” completion requirements and lets employees use their knowledge to improve their learning experience.
BENEFITS:
Reduces training time and increases user engagement by introducing meaningful choices
LIMITATIONS:
Applies only to the specific online module. Branches are typically predetermined and design is limited by the capabilities of the development (often a rapid authoring tool) and delivery technology (LMS)
3. Curricula Pre-Assessment
For many years, L&D has been looking for consistent ways to test employees BEFORE the training was delivered to determine if they needed the training at all. Or, rather than complete everything in curriculum, maybe they just needed specific components. As a result, we could lessen our training expense and save employees some time away from the operation. Unfortunately, this can be difficult/impossible to scale, especially when the knowledge/skill to be assessed is too complicated for a computer to score automatically.
BENEFITS:
Provides employees with option to avoid wasted training;
Focuses only on topics for which the employee has observable gaps
LIMITATIONS:
Difficult to scale
Requires a structured training program
Assesses only moment-in-time knowledge/skill
Requires quality assessment design to ensure effectiveness
4. Content Recommendation
“Because you watched this, we think you will want to watch that.” That’s the gist of this method, which uses consumption and scoring data to connect employees with content they may find beneficial. This can help an employee wade through the sea of content many organizations make available. It also lessens the need to assign large amounts of content to an individual by allowing the system to find the right resources at the moment of interest. Organizations often leverage a learning experience platform (LXP) alongside or to replace their learning management system (LMS) to apply this approach.
That said, consumption data has inherent limitations. Think about it: how often does Netflix recommend content you really want to watch? Or do you spend time scrolling to find what you actually need at that moment? Assuming you should review training content based on the fact that other people similar to you (job, department, location, etc.) did the same or because it’s similar to something you did before is largely a false equivalence. If Netflix knew more about you, such as how your day went and what types of entertainment you prefer based on your mood, then its recommendations would probably be a lot more accurate, right?
BENEFITS:
Potentially limits the amount of content consumed by an individual
Reduces the need to explore and search within a large, aggregated content library
Provides a familiar experience with similarities to consumer technology
LIMITATIONS:
Makes assumptions regarding individual need due to limited data profile
Requires a sizable content library
Contains consumption to structured training content outside the workflow
5. Adaptive Experience
This final method blends together the best attributes of the previous approaches while overcoming one of their biggest limitations. Actually, this method is so much more impactful that it deserves its own section.
Adaptive learning extends personalization beyond structured training. It allows the experience to grow and flex with the needs of the individual employee over time. After all, learning never stops. People learn. People forget. Priorities change. Expectations evolve. Therefore, your learning strategy should be built to keep pace and provide support when its actually needed. Modern technology makes this possible.
Adaptive learning is the purposeful use of data, technology and content to provide the right support at the right time and help an individual employee improve their workplace performance.
It takes advantage of a continuous flow of data from across the employee’s working experience. This multi-dimensional data profile includes insights into the employee’s demographics, consumption, preferences, knowledge, confidence behavior, results and context – all in real time.
This data is analyzed by machine learning tools to provide learning and support options in the actual moment of need. Some of this content may be structured and required (push). Some may be unstructured and/or self-directed (pull). These inputs are strengthened and reinforced by continuous reinforcement and coaching activity. Overall, an adaptive experience helps the employee find the best ways to close their knowledge/skill gaps based on their current responsibilities and future aspirations.
BENEFITS:
Grows with the employee
Leverages the full range of workplace data
Provides continuous support beyond structured training programs
Uses a variety of content types and formats
LIMITATIONS:
Demands a large and persistent data profile
Requires system integration and IT support
Demands a continuous learning experience/habit to increase employee touch points within the workflow
Personalization solves four big business problems
We’ve already demonstrated how ineffective a one-size-fits-all approach to learning can be when trying to address complex performance problems. But what’s the real value in shifting your approach to include a focus on personalized learning? What benefit will your employees and your business get from the ability to adapt learning experiences to the individual?
1. Scaling your training
The perfect learning strategy would be to provide every employee with a mentor who can personally help them with their development. But you can’t do that in an organization with 30,000 employees and only 30 people in the training department.
Personalized, adaptive learning helps you maximize your limited training resources by taking advantage of the immense data resources within your company. By pulling together data on what people know, what they’re doing on the job and what they are (or are not) achieving, adaptive learning technology can trigger the right training experiences and resources for every employee. Think of it like a personal digital mentor—at the scale of a global organization.
2. Boosting knowledge retention
It’s time for the big product release. You’ve put every employee through training for everything they need to know. Then, 6 months later, you come to realize that a large number of employees haven’t been applying what they learned. In fact, they don’t even remember it. Each employee will likely apply the information from your product training at different times, which you often can’t predict. Some have the chance to apply the training immediately; others may not deal with the new product for weeks or months. These employees then forget what they learned, as people are prone to do.
An adaptive learning experience can make sure every employee is ready to handle any problem, regardless of when it arises. By continuously assessing an employee’s knowledge, modern technology can proactively find and close gaps, before the employee needs to use this knowledge. You can also use this knowledge data to identify the experts on your team and leverage them as coaches and mentors. Adaptive learning eliminates the forgetting curve—for good.
3. Improving employee engagement
How much time do you spend chasing employees down to make sure they complete their training before the due date? How successful are you at getting this done? Those responsible for training are constantly emailing managers with spreadsheets of names to ensure employees complete training that they don’t want to do. If the information is so important to the business, why is it so hard to get it done? Simple. Employees often don’t see value in one-size-fits-all training. If they can’t see how it will help them do their job any better, why would they waste the time completing it?
Adaptive learning ensures a value-add training experience—every time. Employees will quickly come to realize that, because the technology adapts in real-time to their personal needs, spending a few extra minutes on training will directly help them do their jobs. And, when you do have to provide one-size-fits-all training due to regulatory or other considerations, employees will be more understanding because it’s an exception rather than the rule. Adaptive learning helps leaders keep their teams focused on improving the organization, instead of spending countless hours chasing them to check boxes.
4. Clarifying priorities
Employees often find themselves in the eye of a priority hurricane. Their manager told them X was important. Another business unit leader sent an email saying Y was critical. And now training wants them to focus on Z. The employee is left to guess which activity takes precedence, or becomes paralyzed with inaction. In a perfect world, the entire organization’s priorities would be aligned. But modern business isn’t a perfect world.
Adaptive learning calms the priority storm by introducing individual need into the equation. Rather than worrying about what everyone needs to know, organizations can leverage adaptive technology to make sure only the right information goes to the right people. Everything can still be important to every stakeholder. But, rather than getting buried by everyone else’s priorities, employees now have help focusing on what they need to be successful.
How to build a successful adaptive learning strategy
Imagine a world in which Netflix only allowed you to watch what it felt was the right content for you. I’d be stuck watching reruns of The Office and Parks and Recreation for the rest of my life. No one wants that, and the same idea applies to workplace learning.
The ability to adapt should help your organization provide the most valuable content and experiences for your employees in order to maximize their time and effort. However, when employees want to go beyond their profile and explore new topics and interests, your strategies should be built to accommodate. Adaptive learning is not the performance version of Big Brother. It’s more akin to a virtual assistant who’s always looking out for you based on your needs and preferences, not just those of the organization.
How do you get beyond the basics of personalization to truly adapt your learning strategy? Here are 5 essential components to consider when creating adaptive learning experiences.
1. Foster continuous learning
Personalization requires more data than traditional training activities and platforms can provide. To build a robust data profile, an employee must engage in learning experiences as frequently as possible. That’s why continuous approaches to learning are essential. Not only does continuous learning have its own value proposition, but it will also yield a deeper, more consistent data flow, which is necessary for an adaptive experience.
2. Find your data sources
Adaptive learning is a data-driven concept. To provide a right-fit learning experience, you have to gather the right insights about your employees and make informed adjustments based on this data. The more inputs, the better!
Your company already collects a tremendous amount of data related to employee performance. Talk to your operations and business insight partners to determine what data is already available and how it may be used to create adaptive learning experiences in the future.
Remember that consumption data is just part of the adaptive learning formula. To identify the right content for the right person at the right time, an adaptive strategy must consider multiple data sources, including:
Demographic: who is this person?
Context: what is happening around this person that may have an impact on their performance?
Consumption: what content/experiences is the person consuming and how do these activities potentially relate to their performance?
Knowledge: what expressed knowledge growth/gaps have been identified at this time for the person?
Behavior: what behaviors is the person demonstrating on the job and how do these decisions relate to expectations/knowledge?
Results: what results is the person achieving and how do these outcomes relate to the other data categories?
Feedback: what are this person’s preferences and opinions related to their performance?
By continuously pulling together comprehensive data on what someone consumes, knows, does and achieves, we can get pretty darn close to a 360-degree profile on the individual. We can then use this data to ask better questions and target the right support when appropriate.
3. Apply a results-based approach to content development
Building content for an adaptive learning strategy requires an evolved design approach. To target an individual’s needs, L&D must break content down to focus on very specific problems. For example, rather than build a lengthy course on customer service, build resources that focus on specific, measurable service behaviors—greeting customers, for example. This content can then be matched with the appropriate data points to help employees who have proven gaps in those areas. At the same time, these resources must fit together as part of a larger puzzle that can help the user grow at their own pace. Classroom sessions and traditional eLearning modules may still play a part, but targeted content that can be pushed and/or pulled at the moment of need better align to an adaptive approach.
Depending on the needs of the user, this content could include anything from brief videos and question/answer knowledge checks to interactive experiences and on-demand reference materials. This is why microlearning content is effective when designing adaptive experiences.
4. Select adaptive technology
Technology enables the adaptive experience. Without the right tech, it’s almost impossible to scale personalized support. This is why L&D pushes so much generic content into the world – to make sure we’re “covered.” This devalues the learning experience for everyone.
Plenty of learning and workplace tech is “targeted” in that it can assign content to an individual based on demographic attributes like location, job title and hierarchy. However, to be truly adaptive, the system must continuously ingest a wide range of data and adjust the user experience accordingly. Axonify is an example of an AI-enabled adaptive learning technology because the system is always assessing and reacting to the data provided at an individual and organizational level.
Personalization is one of the most exciting use cases for AI in the workplace. Machine learning can apply a wide range of models to people and organizational data in order to find and address performance gaps – before they become problems. AI can also identify how training is impacting (or not impacting) business results and help management become more proactive in their strategy adjustments. As AI-enabled platforms, including Axonify, expand within the global marketplace, personalized and adaptive learning at scale will become a foundational component of business agility.
5. Make it okay to personalize
You may need to have conversations with key partners, including HR, Legal and Compliance, to shift their mindset away from a one-size-fits-all approach to training. You can help ease the transition by introducing small steps towards personalization, such as branching modules, to prove the value before you move towards more robust, adaptive experiences.
To truly enable adaptive learning, however, we must empower the person. The sad fact is that many employees are used to the generic, spoon-fed, one-size-fits-all approach. Few have been empowered to drive their own improvement. Even if they have been handed the keys, they don’t have time to drive development activities among all of their other work responsibilities.
To maximize the potential of adaptive learning, employees must understand this approach and accept the extra layer of insight and feedback. They shouldn’t be shocked or confused by personalized recommendations and activities. You have to help them realize that they probably can’t see all of their knowledge and performance gaps and therefore will benefit from an adaptive learning experience. At the same time, you should enable workplace curiosity so employees can expand their interests and not become overly reliant on pushed training for the long-term. We must also remember that adaptive learning is not just about content. Human interaction, including reflection, coaching and collaboration, are integral parts of the experience.
Tips for assessing adaptive technology
To scale an adaptive learning experience across your organization, you’ll need the right technology. With so many platforms from which to choose, how can you determine which is the right solution for your company? This decision is likely to get even more difficult as vendors start to use the terms “personalized” and “adaptive” to mean different things just because they are so popular. I hope this resource has armed you with the fundamentals to have an informed conversation about personalized and adaptive learning.
To help you make the right technology decisions, here are 10 questions you should ask any potential vendor.
1. Does your solution create a personalized/adaptive experience? How?
The HOW is the important part. Ask the vendor to fully explain how the experience is personalized/adapted to the needs of an individual employee. Refer to the “5 ways to personalize learning” section for comparisons.
2. How does your solution apply artificial intelligence to create a personalized/adaptive experience?
AI technology is becoming the standard for creating a continuous adaptive experience. Without algorithms and machine learning capability, the solution is likely to be limited in its ability to personalize.
3. What types of data does your solution use to personalize the learning experience?
Look for more than test scores and completions. Refer to the “key components of an adaptive learning strategy” section for the types of data you may need. The more data a platform can pull in, the more it can personalize the learning experience.
4. How does your solution continuously measure employee knowledge?
Look for knowledge measurement beyond basic test scores and assessments. To adapt to an individual’s needs over time, knowledge is just one measurement that should be taken continuously.
5. How does your solution integrate behavior and observation data?
This is critical for determining if new knowledge is transferring to real-world performance.
6. How does your solution integrate business results data?
This is critical for determining how learning is impacting business results and identifying ROI.
7. How much do I have to know about data and analytics to use your solution?
A quality technology solution should not require a data scientist to implement. While strong data knowledge will always be beneficial, the solution should be simple enough to implement with a limited need for additional support.
8. What types of content does your solution use as part of the personalized/adaptive experience?
Look for a wide selection of content types, including videos, modules, questions, reference materials, etc. More content types will typically mean two things: more opportunities for right-fit support and more chances to collect meaningful data.
9. How does your solution help me balance the priorities of my organization with the needs of individual employees?
This is a primary value proposition for personalized and adaptive learning. If the vendor cannot tell a solid story about how they can support this balance, they may not be the right choice.
10. This is a very different approach to learning for us. How would you explain how your solution works to my frontline employees? Managers? Executives? Legal/Compliance?
You will need buy-in for a new approach from these key stakeholder groups. Challenge potential vendors to determine how they would communicate this change to these audiences to determine how well it aligns with your learning culture and philosophy.
Taking adaptive learning from theory to practice
So, now you better understand the power behind providing personalized, adaptive learning experiences to every employee, and the key things you should be considering when planning out your own strategies. But what does supporting every employee with a right-size-fits-one experience look like in practice? And how would everyone benefit?
Onboarding
Ongoing
New Topics
Two employees performing the same job have the same training requirements and role expectations to start. However, that’s where their similarities end. Information like their past experience, tenure with the company and more is used to customize their learning experience from the start. For example, an employee who is moving to a new role within the company will not be required to consume content about the company, culture and compliance. Rather, he’ll jump straight into foundational training on the role. Someone who is new to the company, but has previous experience in a similar role, will also jump into role training, but at an elevated level due to her proven experience. She will also see company, culture and compliance content along the way.
Both employees experience a blend of digital, classroom and on-the-job training to match their needs. Data from these experiences is collected in real-time to identify knowledge and behavior gaps. This data is used to personalize their job training and coaching. It will also set the baseline for ongoing learning experiences.
Data from the onboarding experience is used to establish a baseline and shape ongoing learning experiences. All employees receive ongoing online reinforcement training in the form of questions and refresh modules. However, the topics vary based on individual needs. They almost never see the same content because they are developing at different paces.
Data collected from ongoing reinforcement training is added to real-world manager observations and performance results. Individual data profiles now include insights on their current levels of knowledge, confidence, behavior and outcomes. This data is used by managers to inform coaching conversations. Administrators also use this data at an aggregated level to assess the overall progress and capability of the organization.
All employees have immediate access to on-demand resources and extra training to augment their personal development beyond the standard expectations of their role. Resource and training recommendations are made individually using their data profiles to help them sort through the volume of available content and identify opportunities.
A variety of new training topics come up during initial months on the job. This includes everything from product releases to process changes and compliance requirements. Administrators continue to assign these topics based on demographic considerations, including team, location, role, etc.
Individual data profiles are used to prioritize training opportunities. New training—such as product rollouts and compliance requirements—may become immediate priorities. In other cases, the new topics may not be as important to their performance success and therefore take a back seat until the data says otherwise. Sometimes, an employee’s demonstrated knowledge and ability may show that they do not need additional training at all, saving them time and effort.
This BETTER way is possible. Right now
The data already exists. The technology is readily available. Organizations just need to start thinking differently about the overall learning and support experience to put the right pieces in place. Adaptive technology. Targeted content. Personalized experiences. This isn’t the future of learning. This is the NOW of workplace performance.
Real-world adaptive stories
Northgate Market
See how one of southern California’s premier supermarket chains realized big savings by training over 6,000 multigenerational employees to all play by the same rules.
Read how Walmart has achieved stunning safety results by personalizing learning to target and close individual knowledge gaps across four distinct generations of employees.
Personalized learning requires data. Personalized learning requires technology. Personalized learning requires targeted content. But, at its core, personalized learning is really about acknowledging people’s individual needs and using the latest techniques to provide the right support at the right time to help them solve problems and achieve their goals. It’s about enabling people to focus on the right things for themselves and for your business.
Bring the future of learning to your organization
I hope you now see just how transformational workplace learning can become when data, technology and content are brought together to empower employees in new and exciting ways. Personalization has become a standard in our everyday lives, and it will soon be an expectation in the workplace. You have the pieces necessary to radically evolve the way you support your people and enable your business. Now it’s up to you to shift mindsets away from meaningless, wasteful one-size-fits-all training and towards an approach that leverages the individual potential of each and every one of your employees. With the pace of business today, you can’t afford to wait. You have the power to bring a flexible, personalized, adaptive experience to your organization now!
Adaptive learning resources
This ebook is BY FAR the most comprehensive guide available on personalized and adaptive learning. But more information is always good, right? So, here are some additional resources to help you make the shift away from one-size-fits-all towards a personalized learning strategy.
