Francisco Ruiz Martínez
Knowledge Management Manager
Expert Contributor

Adaptive Learning vs Adaptive Training

By Francisco Ruiz | Thu, 09/01/2022 - 12:00

Adaptive learning and adaptive training. These are two sometimes confused concepts that I would like to clarify. It is common to use them as synonyms when talking about application functionalities and training platforms, which sometimes leads to unwanted confusion.

Adaptive training is the ability of a training system or program to adapt to the needs of the trainee. This capacity can be greater or lesser depending on the degree of granularity with which the training program has been designed.

For us to be able to speak, therefore, of adaptive training, there must be a training program, such as a sequence or training path established on the basis of clearly defined training objectives. These training objectives are associated with certain knowledge content, as well as a specific didactic method through which to transfer this knowledge to students.

We are talking about adapting the contents to the learner's needs with respect to the learning path; that is, to the learner's previous knowledge of the subject. In this way, the learner will only have to go through those parts of the training whose contents he/she has not yet mastered, whose objectives have not yet been covered.

The most traditional way to do this is through knowledge pre-testing. This is called "as found."

The ability to adapt, as indicated above, depends on the previous design of the program. If the program has been designed with a high degree of detail at the objective level (objective/knowledge ratio), the system will be highly adaptable to the needs of each learner. If the program has been designed with a lower degree of detail at the objective level (objective/lesson ratio, objectives/module, etc.), the adaptation will be less personalized.

Carrying out adaptive training in a face-to-face training model is a complicated task due to logistical issues. It is necessary to coordinate very well the times for the different students and teachers in order to improve the effectiveness of the process without negatively influencing its efficiency. However, from online platforms, adapting to the student's previous knowledge becomes a simple and highly automatable task. Based on successive evaluation tests, it is the training application itself that adjusts the presentation of content to the results of these tests.

Let us now turn to the concept of adaptive learning.

Adaptive learning supposes the capacity of a training system to adapt to the personal ways of learning that each student has. This capacity, therefore, does not have to do with previous knowledge that demands an adaptation of the content but with the learning style of each student.

And if we don't adapt the content, what do we adapt then? Possibly everything else: formats, sequences, durations, combinations.

When a training platform claims to incorporate "adaptive learning," it must be able to propose the content that the student needs to learn, in the formats that best adapt to his personal way of consuming information, offering the possibility of establishing infinite ways of arranging the materials. The approach to learning can be done from more theoretical or absolutely practical perspectives. Time requirements will have to become absolutely flexible.

In short, everything must be configurable by the student. Or better, everything should be configured by the platform according to that student.

The challenge here is to be able to profile students in the same way and with the same accuracy as data processing systems are able to profile a consumer or potential customer. Addressing all the diversity that a virtual classroom can present is a priority task.

To carry out this task, it is necessary to rely on technology and its data processing capacity. Systems that incorporate information from hundreds of students on when they connect to the platform, how long they stay on it, what content they consume, which ones they abandon, which practical proposals are most successful, which content sequences are most frequently constructed and which presentation formats are most successful.

And all possible combinations of this data.

All this information can also be enriched with the sociographic characteristics of the student: gender, age, origin, employment situation if applicable, previous studies, etc.

Quite a challenge, no doubt.

Differentiating these concepts can help us to better understand the possibilities of applying new technologies to our training reality, if we are a company, or educational, if we are an institution of this type. A good understanding of what technologies provide us will help us to make better decisions, especially because we will know to what extent these technologies are capable of fulfilling their promises.