6 Minutes of reading
Adaptive learning and machine learning are two distinct, yet closely related concepts within the field of vocational training. In this article, we’ll show you the ins and outs of these two systems, both of which have their roots in artificial intelligence.
What is machine learning?
You might think that machine learning is a relatively new form of technology—but this is a misconception. In fact, it’s been around for several decades, and started to develop rapidly from the 1990s onwards. Here’s some more detail about how it all works.
Machine learning: definition
Before we dive any deeper into the workings of machine learning, let’s take a moment to define the term properly. Machine learning is a form of technology based on artificial intelligence. It automatically analyses and processes data, and uses that information to make predictions in real time.
Machine learning goes hand in hand with big data. It handles vast amounts of varied and constantly changing data in order to generate relevant results for the end user.
It’s important to think about data in a broad sense here. Statistics and numbers form one piece of the puzzle, but so too do images and text. Machine learning is able to pull in information from practically any kind of source—if it can be stored digitally, it can be used.
Data analysis and automatic predictions
Traditionally, computers have simply followed instructions provided to them. Machine learning turns this on its head by allowing computers to replicate the kind of intelligence that the human brain is capable of. So, instead of acting on the orders given to it, a machine learning system is able to complete tasks independently. It is able to make guesses as to what kind of data it should provide in order to achieve a certain goal.
The automated nature of machine learning is made possible because the system is able to expand its own knowledge. So, how does this process work? In short, the system processes an enormous amount of data. Over time, this data becomes ever more varied, as well as growing in scale. By handling all of this information, the system is able to hone its abilities, with its performance improving the more it practises.
In order to complete all of the above steps, machine learning uses algorithms. These are what makes it possible for the system to automatically create models and to make predictions based on its analysis of data.
What is adaptive learning?
A tailored approach to gaining new skills
We turn now to the second part of our equation: adaptive learning. This name is given to systems which take individuals’ needs into account when recommending educational content and structuring their learning pathways. In order to do so, it uses data provided by the user via an LMS or an LXP.
Users can then use the same platform to access a learning environment that’s tailored to their profile and to their needs—even as those needs change over time. It’s this ultra-personalised approach to vocational training that sets adaptive learning apart.
Employee-centred training courses
Before adaptive learning came along, vocational training was generally standardised. This meant that no attention was paid to the individual, and that courses failed to take into account their existing knowledge or skill set.
Adaptive learning changes all of that by putting employees at the heart of training. This helps to speed up the learning process, helps staff to gain more skills, and offers greater flexibility. This makes it an ideal solution for the ever-changing world of work.
Thanks to adaptive learning, LMSs and LXPs are able to analyse each user’s results, as well as their behaviour while using the app. From this, they can learn more about each employee and offer them more suitable content and training materials. It’s exactly this approach that Rise Up takes, too.
An increasingly advanced system
Over time, adaptive learning has become more and more sophisticated. Nowadays, it’s capable of tailoring multiple different aspects of the training process. These include…
- The form in which training is offered, including its design and the type of materials used, such as videos, text, infographics, etc.
- Creating individualised training pathways, with each step following on logically from the last, adapted so that everyone can learn at their own pace.
- Selecting the content offered to each different learner so as to help them achieve the best possible results—for example, modifying the difficulty level.
Adaptive learning is a particularly good fit for larger businesses due to the sheer volume of data that needs to be processed. The system itself will, over time, be able to learn to work more efficiently and improve its own performance.
Based on this, it’s clear to see the similarities between adaptive learning and machine learning. Both of them use data analysis to complete tasks automatically, saving your business time and money and improving the training experience.
Why machine learning is key to adaptive learning
The dawn of adaptive machine learning
Machine learning wasn’t always as powerful as it is today. In the beginning, it was able to perform only simple tasks. Even then, using it was a slow process, and its data analysis was often imprecise. These early forms of machine learning had limited applications, and the results they generated were not necessarily reliable.
Despite these numerous shortcomings, though, these systems represented an important step forward. Indeed, they still form the basis for the way machine learning works today. Nowadays, however, they use an approach known as adaptive machine learning.
The word ‘adaptive’ is key here. Thanks to advances in the field of artificial intelligence, machine learning systems are able to adapt in real time as they receive new information. Algorithms are becoming ever more agile, and are able to respond instantly and accurately to any user issues.
How machine learning powers adaptive learning—and why your company needs it
In this article, we’ve discussed the difference between machine learning and adaptive learning. However, the reality is that machine learning underpins how adaptive learning works, and provides the technology necessary for it to function.
When you use an LMS or an LXP, machine learning algorithms are quietly working away in the background, extracting data and assessing the way each different employee uses the platform. This is what allows the platform to provide each learner with a personalised training pathway, as well as to change and adapt its recommendations at each stage.
Because of this, training systems are becoming data-driven and predictive.
Despite the many advances in adaptive learning, however, not every aspect of training can be automated. After all, someone needs to be there to assess how well each course has gone, to figure out what has and hasn’t worked, and to check how well each user has done. An adaptive learning system can provide plenty of information, but it’s up to trainers and managers to make sure that results in something concrete.
Finally, it’s worth remembering that machine learning and adaptive learning are not two opposing systems, but instead complement each other. Without machine learning, adaptive learning simply wouldn’t be possible.
