What you should know about Adaptive learning

Seb Azzo Adaptive learning

1. What is Adaptive learning?

Simply put, adaptive learning is a form of personalised learning that optimises a student’s learning experience. Powered by a data-driven algorithm, the software will present learners with content that is best suited for their current level of understanding, performance and speed.

Software leveraging adaptive learning techniques start by presenting students with a set of baseline content, that becomes more and more optimised for the learner’s abilities, as they begin to use the system regularly. The better the system understands its learners, the more suited the content provided to them will become.

In short, adaptive learning puts learners on a unique, and truly transformative learning path.

2. How does it work?

To experience the advantages of an adaptive learning system, we need to look at its key components. Let’s start with the protagonist – an algorithm that is able to understand our learners’ behaviour. The algorithm takes note of a user’s repeat interaction with the system, and adjusts both the learning path and the speed with which the content is delivered, over the course of time. The second key component of adaptive learning is content. Content needs to be prepared and structured in a more robust and detailed way, as the algorithm needs to be able to link together different parts flexibly, without human intervention.

There are different ways how adaptive learning systems can be set up:

Closed system

A closed system is a system that comes with a set of readymade courses. In a closed system, the administrator is not able to change variables or settings within the algorithm. A closed system allows for a fast turnaround time and implementation.

Open system

An open system allows the administrator to create their own content and also adjust variables and settings within the algorithm. In an open adaptive system, it’s important to set aside more time for adaptive learning content creation, as this requires additional preparation compared to standard eLearning content.

Hybrid system

Hybrid systems are a popular choice amongst companies who need to start their training efforts imminently with readymade courses, but still require the possibility to create custom content and have control over the algorithm variables and settings.

3. Why is adaptive learning important?

Adaptive learning systems take the actual knowledge level of the learner into consideration, which makes the overall learning experience enjoyable and more effective. The algorithm ensures that the learner masters the content. For administrators and trainers, adaptive learning systems help identify which content is working well and what might need to be improved. Overall, one is able to achieve greater eLearning success while also preventing cheating. An adaptive learning system will create unique learning paths for each learner, making every learning experience dissimilar from the next.

4. What are the downsides?

The Implementation of adaptive learning systems is more expensive when compared to traditional eLearning solutions and therefore will require more time. Content creation will also require more resources as content connections would need to be made possible throughout.

5. Conclusion

The implementation of adaptive learning technology can introduce many advantages to a company focussed on training their workforce more effectively. The more they are used by learners, the better the results will be. Adaptive learning won’t make sense for every organisation. Setting up requires a fair amount of time and content becomes more complex to make. All in all, it’s a more expensive approach when compared with traditional eLearning methods. If you’re trying to decide whether adaptive learning is for you, ask yourself: Does the additional time needed for content creation and implementation outweigh the the benefits gained in the long term?

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