Adaptive Learning: what it is why you should care - TTRO
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If there is one element of modern progression that should have everyone talking, it’s Artificial Intelligence (AI), and what it means for education, specifically; adaptive learning. Sure, there are plenty of trends that are exciting, Virtual and Augmented Reality are bound to shake education up, and the ever-pervasive mobile phone is changing the meaning of accessibility for everyone, but AI promises to change everything. And in some respects, it already has.

AI is behind personalisation. It is the reason that your Facebook feed has an uncanny knack for knowing precisely what your interests are, and it is also the reason that those shoes on that online store you were browsing keep following you everywhere (it may be annoying, but it works). It is also the reason that education is starting to become more and more powerful. The image below explains it nicely.

On the left, there is the traditional learning path, in which all content is available to all learners. But that isn’t really an efficient way of doing things. Every student is going to have their own strengths and weaknesses, and this effectively renders the content that students already know as redundant. On the right, however, the content is tailored to the individual needs of the learner – a far more efficient way of doing things.

All the material that they already know is put aside and the material that they may struggle with is focused given more attention. This means that learners can spend more time on the material that they’re struggling with, less time on concepts that they’ve already grasped, and less time on learning in general, something that has caught the eye of the corporate training world.

So how does it all work?

Content

Preparing the learning material is essential. It needs to be positioned in a way that it can be effectively delivered to students in a modular fashion. While the principle works at various levels – from small sections of a subject to subjects as a whole – it is best when content is distilled into core points using microlearning methodologies. For example, Axonify – an Employee Knowledge Platform – calls them ‘Key Learning Points’, and creates micro topics around a group of them.

In the business world, this can be extremely beneficial. Once objectives have been established, the behaviours that employees need to adjust or include can be identified. This informs the parts of the training content that will eventually be included in the training. Businesses can actually train their employees based on specific and desired behaviour changes. What’s more, is the employees that already demonstrate comprehension of the content, which is associated with a behaviour, will be provided with other content. This makes adaptive learning extremely effective and efficient.

Finding the gaps

Education is not a siloed industry or sector, education is how we progress in every single industry.

Before learners can be provided with material that is customised, their current knowledge needs to be assessed. There are a variety of ways in which this can be done, some organisations prefer to complete formal assessments, while others prefer a more ‘on the go’ approach. A simple way of measuring a learner’s skill level, is with computerised adaptive testing (CAT).

Learners are separated, and sometimes grouped, based on their presumed skill level. They are then provided with questions which have a difficulty related to their presumed base skill level. As the questions are answered, the computer adjusts the learners score by adjusting the difficulty level.

Algorithms

The algorithms can range from relatively simple to quite complex, but the basic premise is the same. In the case of a CAT-style assessment, the computer establishes a learner’s maximum and minimum skill level, it then pushes questions which are at a difficulty level that is half way between the established maximum and minimum. Depending on whether the question is answered correctly or not, the computer will adjust the learner’s score; raising the minimum in the event of a correct answer and lowering the maximum if incorrect.

In most cases, there is a margin of error built into the algorithms, in order to ensure that questions that are not necessarily indicative of skill level do not skew the learner’s score level.

In more complex algorithms, the actual answer is categorised and can influence the succeeding questions for the learner. An example would be as follows:

How does adaptive learning adjust to a learner’s skill level?

  1. Through questions with varying difficulty levels
  2. It is adjusted manually by the instructor
  3. With technology

If ‘b’ is chosen, the fundamental principles are clearly missing and the computer can adjust the score more substantially. There is a little more insight into the level of the learner given.
Furthermore, platforms like Axonify have even more complex algorithms, incorporating behavioural change, confidence levels and gamification.

Measurement

One major area of adaptive learning is the data that is generated throughout the learning process. It can provide a massive amount of insight into what the learners are struggling with, as well as where their current understanding resides. This can give an institution or company a wealth of insight into their members or employees.

Specifically, with a company, knowing how employees understand things, like products, value propositions, vision, business objectives etc. can give leaders priceless information, which can be used to inform training, as well as positioning and messaging.

The benefits of adaptive learning don’t just stop there. Others include:

  • Faster learning: students can concentrate on the material that they’re struggling with, rather than waste time on the information that they already know.
  • Quality learning: for pretty much the same reasons as the point above. But in addition, in instructor-led environments, there is more time for one on one learning, which is known to be more beneficial.
  • Improves understanding: adaptive learning also manages the pace at which learners learn.
  • Engagement: we all know how boring it is when someone explains something to us that we already know.

The AI that is behind adaptive learning is set to change everything, as mentioned earlier. But, its impact on education is unprecedented. Education is not a siloed industry or sector, it is how we progress in every single industry.

Improving how we learn has the potential to improve how we do everything. And that is where the real power lies.

Resources:
https://en.wikipedia.org/wiki/Adaptive_learning#Technology_and_methodology

 

Author: Kyle Hauptfleisch


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