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Why Adaptive Blended Learning Is the Future of Workforce Training

Oct 28, 2022

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When millions were forced to stay home during the pandemic, eLearning shot up. Coursera, for instance, in its 2021 Impact Report, said more than 20 million new users registered for courses that year, equivalent to total growth in the three years pre-pandemic. 

But, regardless of Covid, eLearning is popular—the market nearly doubled in the past decade. A PR Newswire release says the eLearning market passed US$315 billion in 2021, with a projected compound annual growth rate of 20% from 2022 to 2028. Corporations and academia are the biggest drivers.  

While eLearning is cheaper, more accessible, and more convenient than other modes of learning, not all eLearning is customized. Much of it consists of prepackaged courses in a one-size-fits-all approach. For context-aware ubiquitous learning, the next step is Smart Learning, which uses technology to adapt educational materials. 

Can adaptive learning software help meet the need for individualized training options? 

Adaptive learning is already widely used in universities, schools, and some business LMSs. It can improve workforce training in the current environment where upskilling and reskilling are priorities, and where the Great Resignation is still ongoing. With millions quitting their jobs and many others disengaged at work, HR needs innovative ways to motivate, train and retain people. Although it’s not a silver bullet, adaptive learning can be a useful tool to help achieve these goals.  

Although it’s been researched for decades, delivering customized learning at scale has been problematic until relatively recently, when advances in AI and machine learning made it possible. 

Adaptive learning pays more attention to the varied ways individuals learn, and customizes content or delivery style to suit their needs. When people see you are taking their unique learning needs seriously, they’re more motivated to not only learn with you but stay with you. 

What is adaptive learning? 

Adaptive learning uses computer algorithms and AI to customize learning.

Smart computers track your behavior and other data to learn your goals and needs. The AI then curates and adapts content to suit you. 

For instance, AI software can note your environment, needs, and job role to recommend courses. It can track your learning journey and behavior, remember your preferences, and note how you performed in different modules. 

Then it uses the data to deduce your particular learning issues, problems, goals, and needs. Based on that, it adjusts the content to suit you.  

There’s a significant range in what adaptivity tools can do. Entry-level AI tools use simple criteria to identify a learner’s pathway. More advanced AI tools may use an inference engine to analyze many more variables to provide a richer, more tailored learning experience. 

The two approaches to adaptive learning 

There are two approaches to adaptive learning: personalizing your training pathways or your training content. 

The first approach uses the same training modules for all learners. Learners work through the course one module at a time. However, if they already have the knowledge or skills in a module, they can skip it and move on. But if AI detects they have knowledge gaps, it will prompt them to go back over the content they need to master before going on to the next module. 

The second approach uses AI and neuroscience to offer different modules from person to person. It varies not only in the pacing or sequence of material, but the nature of the content itself, depending on individual considerations. 

What is learning analytics for adaptive learning? 

Learning analytics is when you use software to measure, collect, analyze or report data about learners and their contexts. You would do this to better understand and improve your learning systems, processes, and environments. 

Learning analytics for adaptive learning involves detecting patterns, making predictions, and making recommendations. It gives you insights into trends, learner engagement, and learning pathways. That helps you create better learning strategies.  

Why a blended approach? 

Some people are visual learners. Others prefer note-taking, reading, and listening to lectures. Others respond better to hands-on experiences. And others remember learning best when they talk about it with peers or with mentors—social learning. 

Because people learn differently, adaptive learning helps. But it’s just one tool that will work best when combined with others. This is why we recommend blending your approach. You can use adaptive learning alongside other methods, such as  

  • in-person learning,  
  • engaging video content,  
  • gamification,  
  • social learning,  
  • mobile learning,  
  • experiential learning—e.g., case studies, scenario-based learning, or simulations for complex decision-making, and 
  • coaching and mentoring. 

You can also provide lots of supplementary material, such as infographics and quick reference guides, to ensure there is a ready source of helpful information. 

Aligned to your organization’s goals, a blended, adaptive learning approach will keep your training interesting, relevant, engaging, and effective—people will remember and apply their training a great deal better. 

Why is personalized learning so powerful? 

It’s good to remember that personalized learning has been around for centuries—long before the invention of the first computer chip. Any time a teacher takes the time to speed up, slow down, or change their teaching strategies and course content to cater to a particular student, that’s personalized learning in action.  

However, human, face-to-face personalized teaching and learning are not practical or economical for very large groups of people. Adaptive learning software meets the need for customized online training for large groups of people. 

An AI-enabled personalized learning system has other benefits. For instance, it helps make relevant content easier to find by minimizing user navigation. Many large firms have an abundance of courses, and employees may feel swamped by all the choices. But if smart software has enough data on an employee, it can present exactly what they need, when they need it, saving much time. 

Adaptive software also makes it easier to identify skills gaps and deliver relevant content. It can detect “unconscious incompetence”—people who don’t know what they don’t know. The smart learning system can then present material to address that knowledge gap. 

An AI-powered adaptive learning system makes better recommendations over time as it learns more about individual learners’ needs. 

Personalized learning gives people more ownership and control of their learning journey. It helps make learning more interesting, relevant, and effective.  

And that leads to valuable skills development in your workforce. 

 

 

Create an inspiring learning experience.

Contact Pixentia to consult on how best to plan your learning technologies.

We can assess your needs, deploy appropriate software, and help you optimize your workforce learning.

Discover our learning solutions on our website:  https://pixentia.com/services/hcm/learning

 

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 About Pixentia

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

 

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