Adaptive learning. Everyone wants it, but few truly offer it.
As the word suggests, ‘adaptivity’ is all about adapting. In a learning context, adaptivity means adjusting to the learner. While it is often focused on catering to learners who struggle, it is also smart to adapt a learning process to learners who are finding it very easy. But what exactly is adapted? The level, the pace, the way learners learn? The short answer is all of the above. Here’s the long answer →
Detail: adaptivity in learning is generally used by computers (algorithms) or by people themselves (such as an instructor). When people apply adaptivity, it is often referred to as personalized learning. In practice, however, these terms are often used interchangeably.
Forms of adaptive learning
Education science shows that adaptive learning works. People achieve the best learning outcomes when they can work at their own pace and on their level, and when they get instantfeedback from an expert. Learning will then be more effective than without adaptivity.
Adaptive learning with Drillster
Drillster is an adaptive learning application. As you just read, adaptivity is a fvairly broad concept. Let us, therefore, explain here what it means to us.
The more adaptivity, the better. We apply this in our algorithm in various ways, both in the short and in the long term. Drillster isassessment-based, meaning that learners learn by answering questions. With every question answered, the algorithm learns something about the person: was the answer right or wrong, how often does this person get answers to questions andquestion variants about this knowledge element right or wrong, and when did this person last do the drill? Someone may get an answer right without truly mastering the knowledge. Suffice to say that the chances of guessing correctly are factored in and learners get multiple questions about each knowledge element.
All this information is used to select the next question, determine when someone has reached the right knowledge level, and detect when knowledge has dropped below the required level. After all, knowledge is forgotten rather quickly. Based on the results, the algorithm calculates when it is time for someone to work on getting their knowledge levels back up. They will then get a notification asking them to do the drill again. The questions they then get will focus mainly on the material they turned out to struggle with previously. Drillster also checks if the material that turned out to be easy is still available in the learner’s memory. If it turns out that the knowledge is increasingly well anchored, theinterval at which someone is prompted to practice again will gradually become longer.
Discover more about how Drillster applies adaptive learning in the article ‘Adaptive learning on a micro level‘.
Online and offline adaptivity
Ideally, you would offer every single student or worker a fully personalized learning pathway. However, this will involve a lot of time and effort, especially when you already have a learning program up and running that is not yet adaptive. Persuaded, but don’t know where to start? Start small. You can go for partial adaptivity with ablended learning process. You could, for example, choose an adaptive learning tool such as Drillster and use it in classroom-based training as an additional resource. The results from the tool can then be used to adapt your lessons (flip the classroom). This way, you can use adaptive learning both online and offline for deeper learning.
Subscribe and we will send you more like this from time to time. No spam, ever. Just thoughtful reads on the science of learning and how it meets critical business needs.
