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How to Deliver Truly Personalized Learning

Personalized learning has become a bit of a buzzword in L&D, but with the right approach, it can deliver learning that is truly tailored to your learners’ needs. In this blog post, we explore some of the misconceptions around personalized learning and effective approaches you can use to deliver learning journeys that really work.

The following is an adapted excerpt from our ebook, ‘How to Create Digital Learning That Works’.

Personalized learning is catered to the needs of your learners

What Is Personalized Learning (Really)?

Here’s what we sometimes hear about personalized learning:

“Personalized learning is adding a role filter so learners only get what they need.”

“Personalized learning means my team will only create the minimum content required.”

Personalized learning sometimes gets misconstrued as delivering the bare minimum that learners need in as short a time as possible. Microlearning is also caught up in this perception because it splits a learning course into smaller ‘chunks’, which enables it to be delivered based on very specific criteria.

But personalized learning is really about giving your learners their own learning journeys in a meaningful, thoughtful way.

This means:

  • Delivering learning that gives people what they need right now to do their jobs effectively, based not just on their roles but on other factors, such as confidence and competence.
  • Designing effective blended learning journeys that give learners the flexibility to create their own learning paths and empower them to pursue self-directed learning.

How to Deliver Truly Effective Personalized Learning

So what should you be thinking about to deliver truly personalized learning journeys?

First, as mentioned above, reframe the idea of personalization away from being purely role-based. True personalization could, for instance, consider a learner’s:

  • Competencies
  • Needs
  • Confidence levels
  • Attitude

This requires a thoughtful approach to design upfront, but when implemented correctly it delivers a far more effective learning experience that really feels individually tailored.

Second, think about microlearning as discrete components. Learning that is meaningful, not just small. Microlearning is effective when it delivers the right type of learning for the right context. That might be an array of different components packaged in a way that empowers your learners to choose what they need.

Three Examples of Effective Personalized Learning

Here are three short examples that demonstrate a considered approach to giving learners their own learning journeys.

1) Pre-learning Diagnostics
Use a diagnostic to deliver a tailored learning program by asking learners to complete a series of Likert scale questions on their confidence levels, attitudes and working practices.

Based on the answers they submit, learners then receive a tailored ‘menu’ of learning made up of a variety of different components. This can display learning by subject area, or even by priority, depending on how a learner has answered the diagnostic.

2) Microlearning components
The diagram below shows the vast array of different learning components we created for one learning program. This was a large-scale, long-running program, so don’t be put off by the sheer volume and variety outlined here.

Some of these components formed a core element of the program, while others supported it. Part of the design of the program was to ‘pull’ learners in and empower them to learn in their own way, asynchronously. The sheer variety of different formats and channels made this possible, with learners given access to what they required in any given moment.

This is a good example of how to empower learners to define their own learning journeys by delivering an array of discrete components, designed to support self-directed learning.

3) Information design
Underpinning the example above is strong information design. When you’re considering giving learners the freedom to define their own learning journeys, conduct a discovery phase that enables you to understand what it is that people need and the best way to deliver that training content to them.

This includes understanding what learning is already out there in the business that can be leveraged and then supplementing that material with pieces of new content. Then think about how to present and curate that information in a way that makes it easy for learners to search and find that content.

Adaptive Learning: Data-Fuelled Highly-Personalized Learning

The examples we’ve described above are based on taking a design approach upfront that supports personalization. But another, more data-led approach, can enable the delivery of tailored learning in a different way.

Adaptive learning uses data to adjust the path, pace and content of a learning program according to the learner’s needs. This adaptation occurs while the learner is completing the learning, rather than at the start.

Adaptive learning can harness data from learning activity as well as behaviors, interactions and external activities that may take place outside of it. This enables L&D teams to gain a detailed understanding of learner performance and then deliver highly-personalized learning that targets individual strengths and weaknesses.

This type of approach is greatly enhanced by the use of xAPI technology which enables the collection of data on a wide range of learning activities. This data can then be used to plot a learner’s activity and progress against other known factors, such as the learning and behaviors associated with evidenced successful learners, and adapt the learning journey accordingly.

Adaptive learning is potentially the future of personalized learning as the use of data analytics becomes more widespread in L&D—so it’s worth thinking about how you can start getting to grips with learner data to start take this approach to personalized learning.

To find out more about how to cut through the hype and create effective digital learning, get your copy of the ebook, ‘How to Create Digital Learning That Works’ now.

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