When it comes to learning, people have different goals, strengths, skill and knowledge gaps, experience, and aptitudes. What it takes to address those needs varies greatly from one person to the next.
Learning and development professionals learn sooner or later that "teaching to the middle" is not effective. Personalization through adaptive design is a far better solution.
This isn't really a new idea, but it seems that coming up with the instructional solutions required in order to satisfy the scope and the scale of the variation and to engage learners has become more challenging.
- Learners today are different, they don't have a lot of time, if working from home they have lots of distractions, and the result is that they can be hard to engage.
- The world of work is different after 2020 and jobs are more complicated.
- Personal goals and skill gaps change constantly along with technology, career opportunities, and other developments.
What if eLearning (speaking of asynchronous eLearning) did not have to push the same content to everyone, without considering what they already know or have already done? What if we could adapt an eLearning course "on the fly", based on what the learner already knows and has already done in whatever authoring tool you may be using?
Some of the challenges
An instructional designer can't engage all learners the same way. What does it take? Learning must provide:
- A highly individual approach matched to each learner—one size does not fit all.
- Relevance to improving each learner's personal performance at work or for mastery of a task
- Personal experience, skill, or knowledge to fill a gap
- A solution matched to an actual moment of need
- Feedback or guidance relevant to the learner's performance vs. their goal.
Finding the need
Instructional designers have a variety of ways to get this information. There are different methods and practices for synchronous or asynchronous delivery, but depending on the case, these are the broad categories:
- Embedded questions or practice
- History (xAPI/LRS, Al, machine learning)
Strategy: Personalization or adaptive learning
There are some differences to understand between personalization and adaptive learning.
- Personalization provides a unique, focused learning path for each individual.
- Adaptive learning systems are software where algorithms optimize the content to adjust for the learner's goals and current state of knowledge.
What's the difference?
- You can't buy personalized learning "off the shelf".
- Adaptive learning is one method of personalization: data-driven, meant for large numbers of learners, often technology-supported (not always digital); there are other ways to personalize learning.
- You can buy software to support adaptive technology and design its application to fit the case and the learners. The technology and the design builds the path "on the fly" and may include remediation as well as instruction and practice.
Why adaptive learning?
With adaptive learning, each person gets different content and a different experience, even when all are aiming for the same outcome. Those with no experience begin with the basics; those with more experience begin at a more advanced level, In this way, each learner makes the best use of time by skipping what they already know. Adaptive learning is also effective for those who only need a refresher.
How does this work?
For synchronous learning, an instructor may do the delivery, adjusting what is delivered and when according to the needs of those in the training situation. This may look like blended learning or a "one-room schoolhouse". For asynchronous delivery, an adaptive learning platform handles the details of who gets what and the sequence. In some cases, a pretest drives content selection and sequencing. In other cases, xAPI programming may identify previous experience and sequencing, AI, machine learning, and microlearning may also handle selection, sequencing, and delivery of content
Adaptive learning is often time-consuming to develop, and may require programming skills.
Want the details?
One of the key ways to create asynchronous adaptive learning is to capture previous experiences from your learning record store (LRS). On Thursday, June 10, 2021, Jeff Batt will demonstrate how to adapt, personalize, and change the content of an asynchronous course to the specific learner in his session, "One Size Won't Fit All: Using xAPI to Personalize Content to the Learner."
With xAPI, we can not only track what the learner is doing but also read what they've already done. Once we have that information, we can take them down different paths, alter what they see, and make things more relevant. In Jeff's session at the Online Conference Pushing the eLearning Envelope, he will show you how to design and develop such adaptive courses. He will also show you how to pull the data back into your course from previous learning experiences and use that throughout your course. You'll leave this session with examples and methods for how to adapt your courses regardless of the authoring tool you use.
In Jeff's session, you will learn:
- How to pull data from an LRS
- How to update course variables within Storyline or Captivate courses
- How to capture the actor taking the course
- How to target what data you want to know about the learner