What is adaptive learning?
For many years, classrooms around the world have taught in a way that assumes all learners are equally inexperienced and need the same level of guidance. Research, however, has often proven that people have different learning styles and needs, which makes a one-size-fits-all approach to teaching ineffective. This is where adaptive learning can help.
How can we define adaptive learning?
Adaptive learning refers to customized learning activities tailored to individual learners’ needs. When it comes to corporate learning, this could mean providing custom learning paths based on your employees’ varying skills, work experience, and learning paces. When training methods are adjusted to individual needs, learners are more likely to receive accurate and — therefore — effective professional guidance.
For example, imagine being an experienced paid marketer who wanted to pick up organic marketing skills. While a training program may help, it would be ineffective if the first few sessions were spent diving into the basic principles of marketing — something you probably already know and aren’t interested in learning all over again. Instead, you’re more likely to be engaged if the program accounted for your existing marketing experience and adapted the learning path accordingly.
The example mentioned above can be credited to Malcolm Knowles’ theory of andragogy, which assumes that adult learners learn better when they can make clear connections to their prior experience and when the knowledge is immediately applicable.
Adaptive vs. personalized learning
At this point, it may be easy to confuse adaptive learning for personalized learning. And understandably so. Both aim to cater to individual learning needs by providing a customized experience. But there is an important difference. While a personalized learning path is exclusively designed to meet learners’ needs, adaptive learning makes use of technology to make the necessary adjustments.
Using algorithms, adaptive learning technology constantly measures a learner’s engagement and performance to identify patterns and trends, which then allows it to adjust the type of content it pushes out. A Learning Experience Platform (LXP), for instance, usually generates adaptive learning paths based on an algorithm. This is particularly useful because learners don’t always know what subjects they need more training in.
For example, you could be convinced that you spent two hours less on your iPhone this week than you did last week, but your iPhone’s weekly Screen Time report might reveal that you, in fact, spent more time. Similarly, while a learner may feel confident about a certain skill or ability they have, adaptive learning technology can help clarify their actual level of competency. The use of an algorithm, therefore, makes adaptive learning a more precise way to customize learning paths.
Adaptive learning strategies
Implementing an adaptive learning strategy for the first time can be a lot to plan for. To help you get started, we’ve put together a few steps you can take:
1. Create the right environment for adaptive learning
To facilitate adaptive learning accurately, you’ll need a digital platform powered by an algorithm that can monitor learner engagement. As mentioned earlier, an LXP is an example of a learning platform that generates learning paths based on algorithms. LXPs are also known for their powerful search functions, which also monitor users’ search histories with the goal of providing custom experiences. Some Learning Management Systems (LMS) are beginning to incorporate learning paths and algorithm-based search functions.