Adaptive Learning Design – What Is It and How to Use It?

One of the key trends of today is personalisation. Whether it’s shopping, marketing, watching movies or any other thing, we have come to expect personalised experiences. The same is true for learning also, even in the corporate setting. Employees expect the employers to provide learning that is relevant and empowers them both professionally and personally. For the best learning experiences and results, the employer’s and employee’s interests need to align. The modern learner expects learning content of the right difficulty, delivered with the right mediums, reflecting their professional and personal interests. Luckily, adaptive learning design helps us do all of that, and more.

What is adaptive learning design?

Adaptive learning design means that learning journeys transform from straight lines to something resembling a spider’s web. Each milestone of progress influences what the learner sees next and hence where the learner moves on that web. By taking into account the learner’s prior experience, roles and operational experience, we can assign content that is complementing their existing competencies and reinforces their skill-set. Endless amount of factors can influence the content assigned to the learner: prior learning results, prior job experience, age, position, geographical location or own interests.

Adaptive learning design aims to produce relevant learning taking into account as many of these factors as possible. More personalised learning improves engagement, motivation and retention. Employees gain knowledge on items that benefit them in many areas – professional careers as well as personal lives. Thus, they are more likely to stay engaged in their jobs, maintain continuous improvement and deliver better work.

How to make adaptive learning work?

Adaptive learning can be designed in many ways, but one factor is crucial – data. For adaptive learning to hit the spot, the organisation needs to leverage big data and pool information from different sources. Once the required data infrastructure is in place, here are some methods on getting started with adaptive learning.

  1. Initial Competency Mapping

    Before the learners start a course, you should test their existing knowledge on the subject. Then, assign learning content accordingly. This ensures that secret subject matter experts don’t have to waste time on basis level things. Also, the beginners don’t get overwhelmed by too much content too soon.

  2. Post-learning evaluation

    You should also evaluate the learners after they have completed the course. People learn at different paces and in different ways, and good evaluation helps to support learners accordingly. Slower learners can keep on reinforcing what they learner, and faster learners can start to tackle other topics. You can do this by testing, but perhaps a better approach would be to leverage learning data to analyse the learners’ progress. Thus, you can eliminate some of the dreadful formal assessment.

  3. Curate multiple versions of the content

    Naturally, personalised and adaptive learning requires curating the content into the spider’s web model. You can start by mapping out the same content for different positions and seniority levels in the organisation. This can also help in learning engagement, as enthusiastic and competent employees can take on learning above their current role, hence preparing them for more advanced duties.

  4. Get Artificial Intelligence (AI) to automatise the cycle

    Once you’re accumulating the data and have built content into the spider’s web, it’s time to dive into AI. AI tools can analyse this data and help to direct the learner in their own personal journey by recommending materials suitable for their personal style, seniority, experience, etc. With proper integration, AI can take into account changes such as position, job duties and operational issues in real-time. Furthermore, advanced AI capabilities can even help the learner to understand what learning style works best for them and assign materials accordingly. AI in learning is still new, so many learning systems don’t necessarily have the capabilities. But some do, and we’ll be happy to recommend them.

Finally, you should embrace continuous iteration and improvement. AI and advanced analytics provide us deep insights on what works and what not. You should use these to continuously improve the content and get the highest return on investment for your learning.

Would you like to implement adaptive and more personalised learning? Are you interested what AI can do in this aspect? Let us know, we’ll help you.

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