Adaptive Learning definition: all you need to know about adaptive learning

Adaptive Learning is a new approach to education. It involves adjusting learning to the individual needs of each student, using technologies such as Big Data. Find out everything you need to know about it.

In France, as in many countries around the world, education has always been ruthless…. From the very first years, schoolchildren are divided into two groups. Those who succeed in assimilating the concepts are often those who will study long and successful lives, while those who encounter difficulties usually end up dropping out early. The problem is that standardized curricula do not take into account the differences between individuals.

Adaptive Learning: What is Adaptive Learning?

adaptive learning what

In this sense, Adaptive Learning is a technology that is set to revolutionize education. To define the concept simply, it is a learning method in which one or more characteristics of the learning environment adapts to the apprentice.

In detail, this adaptability concerns three main elements : appearance, order, and accompaniment towards the goal. Appearance, or the form of learning, is the way in which learning actions (content, text, graphics, videos…) are presented to the apprentice.

Order is the way in which learning actions are ordered and connected according to the rate at which the learner progresses. Finally, goal orientation refers to the actions of the system that lead the apprentice to success, e.g. level of difficulty.

Adaptive Learning: What is it for? What are the benefits?

adaptive learning benefits

The main idea of Adaptive Learning is to meet the individual needs of each apprentice during the learning process. Even for the best teachers, it is difficult to fully understand the profile of each student and to adapt the program accordingly. That’s why technology can be a great help.

Rather than imposing a single curriculum on all students without regard to their respective abilities and needs, Adaptive Learning allows for the development of personalized learning that they will be motivated to follow.

Adaptive Learning and Big Data: Data-Driven Learning

adaptive learning big data

There are many Adaptive Learning solutions available today. Most of them focus on adjustment of the learning path. For example, a student can take a test on the first day. Based on the results of this test, a personalized learning path will be proposed to the student. Also, the content can be adapted to his or her individual needs. Other solutions focus on adjusting the pace and style.

Many recent Adaptive Learning platforms are based on Big Data. Data is captured as learning occurs, and this data is used to provide automated data-driven adjustment. the rhythm, the learning path or content.

It is also possible to analyze data from students who have successfully completed learning, in order to identify the elements that allowed them to succeed. Thus, if two students have a similar profile, it will be possible to offer them the same learning. Big Data is therefore playing a major role in the education revolution.

Adaptive learning in companies

L’adaptive learning is not only about the world of education. Indeed, technology can also be of use to a society. In fact, many professionals are already offering their services to companies for training of this kind for their employees. One example is NOVAE Digital Learning.

And these providers are successful. This is not surprising since this approach enables employees toAcquire new knowledge fasterreducing costs to the employer.

In addition to accelerating learning, it does it better than the old methods. In fact, the training courses focus on concrete goals. As a result, they are more efficient and really strengthen the skills of the employees.

As adaptive learning is based on the information available about the participants. The more data available, the better the results. In other words, the more the algorithm deals with different cases, the more it can determine the appropriate solutions for each one. Thus, this type of learning keeps getting better with time.

Finally, the technique avoids redundancies in the courses. Indeed, the system records the modules already taken by an individual. For his next training, the algorithm will make sure that he will not be offered the same course again.

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