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

• Level. First of all, you can adapt the material to the level of the group or individual. In most cases, the target knowledge level that learners have to attain is known. You can estimate their current knowledge level based on results achieved thus far, although that does require some insight into each learner’s history. If you do not know anything about the learners yet, you may be able to estimate their current level by having them take a formative test – a kind of mock test about the material without a binding result. This will give you an idea of individual learners’ current knowledge levels. You can then give someone who knows a lot about subject A but little about subject B more material about subject B. This way, every learner has their own learning pathway.
• Pace. Adaptivity can also be about pace, although this is, of course, also closely related to the level. When a learner picks up the material on a certain subject fairly well and quickly reaches the target knowledge level, you give them more in-depth material on that subject. A fellow learner who is lagging behind somewhat, on the other hand, will benefit from some more explanation and attention to reach the target level. In some cases, it may be possible to have learners set the pace themselves. Everyone will then ultimately reach the same knowledge level, but one sooner than the other. It is comparable to one student graduating high school in five years and another doing it in seven. Alternatively, learners can learn with different intervals and intensity levels to ultimately all reach the same level at the same time.
• Medium. Sometimes adaptive learning means to adapt to learners’ lives, so that they can learn wherever and whenever it suits them. An app you can use on your smartphone, tablet, or desktop is easier to adapt to learners’ preferences than having them turn up for in-person training at a specific time on a specific date.

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 instant feedback 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 is assessment-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 and question 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, the interval 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 a blended 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.