Adaptive learning, or Adaptive Learning, is a pedagogical approach that aligns with the profile of each learner through new technologies. Concretely, This method aims to adapt the course and content of the courses according to the individual needs of the students. It combines cognitive science, big data, and technology. What is the real situation with this method? An overview of adaptive learning.
Adaptive learning: definition
Adaptive learning is a teaching technique that allows better assimilation of content. elearning thanks to artificial intelligence. In this sense, the content and modules of the training courses are personalized according to the learner's profile. Adaptive learning uses data referring to student behaviors during training. Afterwards, this data is processed so that the training is better adapted to his preferences and skills. Concretely, this method focuses on three specific elements, including:
- Appearance the training presented to the learner: this may be the content, the explanatory videos, the graphics used, the texts, etc.;
- The order of the training : the progress of the training depends on the pace at which the learner progresses in his quest for knowledge;</li>
- Accompanying him towards the goal : all the means implemented to support the learner in difficult phases and ensure his success.
The principles of adaptive learning?
Adaptive learning is an innovative teaching method. To offer personalized training to learners, everyone should start by taking a test. The results of this test allow artificial intelligence and algorithms to create a personalized learning path. The content is also modified according to the needs of each learner. Throughout the apprenticeship, data is collected to automate the adjustment of training. Note that if two learners have a similar profile, their learning itinerary may be the same.
The benefits of adaptive learning
Adaptive learning focuses primarily on e-learning or online training. While in traditional training, the trainer gets to know his learners better through assessment, in online training, artificial intelligence makes it possible to better know his learners in real time. This technology personalizes the content and the journey of each learner by referring to their acquired knowledge, their rate of progress, their goals to be achieved and their preferences. It thus makes it possible to generate satisfactory results and a better success rate, as well as to better assess the return on investment of training.
In adaptive learning, learners don't have to review topics they already know, unless they want to. They can stay on a subject until they assimilate it and are not forced to understand everything under pressure. Algorithms play a valuable role in identifying the student profile, customizing the program, and optimizing student success. Adaptive learning works on learners' needs, motivation, and abilities.
Independent of pedagogical inconveniences, adaptive learning ensures continuous training for learners. The presence of an in-person trainer is not required, as the majority of the modules are done online. This tool works in the same way as the flipped classroom. The learning itinerary is sequenced based on the responses and profile of each learner. Its results make it possible to adapt the content to its level. The exercises are also adjusted to touch the learner's proximal development zone. Everyone has in some way their own training plan. In short, this tool aims to promote the acquisition of skills.
Implement adaptive learning
To ensure the success of training, adaptive learning plays on time. How? By proposing the ideal exercise at the right time, i.e. not too early or too late. The tool takes into account the learner's learning pace. It adjusts the frequency of the exercises to be treated to avoid forgetting and to ensure a good assimilation of the content. Adaptive learning is based on learning the right subject at the right time. It can use specific applications or reminder alarms on the smartphone, being also compatible with the Mobile learning.
In addition, adaptive learning takes into account the behavior of the learner in the previous modules. Commitment, success, failure, and other factors allow algorithms to find the next module adapted to the learner. Module redundancies are avoided, because the tool strives to choose the ones that would best interest the learner.
Adaptive learning still raises questions, but it is positioned at the center of training systems. This educational trend is at the crossroads of artificial intelligence, neuroscience and big data. Its scope of use and contribution are not yet limited, because adaptive learning is constantly evolving. Is it about theThe future of e-learning ?
