Adaptive learning = custom-made learning

Every student is different. Even within the same class, pupils are often very different. Of course, there are differences in skill level: one learns languages more easily, the other is stronger in maths and mathematics. But there are also other differences that influence the learning process. Some pupils prefer more guidance and support, others prefer to work independently. Some want to get started right away, others prefer a thorough explanation first.

An adaptive learning environment takes these differences into account. It adapts to the preferences and level of the learner, for each individual learner. It is like a teacher adapting the content and format of a lesson to the skill level, preferences and interests of the class, but with a focus on the individual learner. It is important that the adjustments are made on-the-fly, and that they are continually adjusted, to make sure the learning process is not interrupted. This brings us to the next question: how does adaptive learning work?

To be able to adapt to a learner, a digital learning environment first estimates the skill level and preferences of the learner. To do this, the learning environment uses the information it has about the learner. On the one hand, this includes information about choices and characteristics entered by the pupil or teacher (such as age, year, or what the learning environment should look like). On the other hand, the learning environment captures the learner’s interactions with the environment. How long or how often does the student watch a video? Does the student answer the question correctly or incorrectly? How fast does the student answer a certain question? What is the pace at which the student progresses through the material? This information can reveal differences between learners and makes it possible to assess the learner’s skill level, preferences and motivation.

Based on this assessment, the learning environment is adapted. This can be done in various ways. For example, the content can be adapted to the preferences or motivation of the learner. Or the difficulty of the exercises can be adapted to the student’s skill level. But also the automatic feedback after an answer can vary depending on the type of error. The possibilities are endless.

There are different strategies for organising adaptive learning in a learning environment, but the idea remains the same: to adapt learning to the learner. Roughly speaking, there are two strategies. In the first strategy, educational experts record all possible adaptations. Using an “if this, then that” approach, the learning environment guides the learner through the content. For example: if a student does not answer an exercise well enough, an additional example is offered. If the answer is good enough, the extra example is skipped.

The second strategy makes use of computer algorithms. Such an algorithm, for example, first estimates the student’s level and, based on that, offers an appropriate next exercise. To do this, the algorithm not only uses information about the pupil itself, but also about other pupils within the learning environment. On could say the algorithm learns which combination of learning material and student best stimulates learning.

You can compare the second strategy of organising adaptive learning in a digital learning environment with how online streaming services work. Think of Spotify, Netflix, or Amazon. Based on the music or movies you choose, the streaming service makes an estimation of your preferences. Based on your preferences and your similarities with other users, you get new, tailor-made suggestions. (We’ve already dedicated another blog post to this).

There is of course an important difference between an adaptive learning environment and an online streaming service. In an adaptive learning environment, the focus is on learning rather than entertaining. The learning environment first and foremost wants to stimulate learning as much as possible by means of targeted adjustments. This immediately answers our last question: why do we put effort into adaptive learning?

A student learns most from targeted feedback, from an exercise or learning content that is challenging enough, but not too difficult. An adaptive learning environment tries to do just that: give each learner the content, the exercise, and the support or feedback that works best to stimulate their learning process.

Research has shown that this approach works. Students learn more efficiently when they use an adaptive learning environment compared to a non-adaptive learning environment. In addition, motivation plays an important role. Learning materials tailored to the learner, tailored to his or her personal preferences, stimulate and enthuse, and help the learner to engage attentively.

Make no mistake, an adaptive learning environment is not there to replace the school, the classroom, nor the teacher. Adaptive online learning is a supplement to classroom learning. For teachers, it offers the opportunity to present customised learning materials to each student, both in and out of the classroom. In short, adaptive digital learning goes hand in hand with traditional classroom learning.

In a digital world, learning is taking place online more often. At i-Learn we focus on adaptive learning. We stimulate ed-tech developers to make their learning environments more adaptive and want to support them in doing so. We research how adaptive learning environments can work in practice and develop new systems and algorithms to shape adaptivity. The goal is clear: customised learning for every student in Flanders.