Adaptive_learning is the use of algorithms to develop a unique learning path that meets the specific needs of each student. Adaptive learning will consider each student's strengths and weaknesses or how well they are performing. In short, adaptive learning learns from the student. It uses student responses to guide the presentation of the learning and the difficulty of the learning experience. It will adjust itself over time, depending on how well each student learns.

It is a fact that there are as many learning styles as there are students. For this reason, it has lost credibility to design a single education that can meet the needs of all students at the same time. Adaptive learning techniques should be considered part of the personalized learning model to meet the expectations of each student and save time and money. The relevance of the content is vital in increasing user engagement.

For example, sit in a classroom with many students. If the teacher does not have a tool to use to analyze students, they will move from one topic to the next regardless of whether everyone understands the topic well or not. It is relevant for the student to practice after school or take lessons from a private teacher; Thus, learning can be adapted to its needs and pace, rather than “one size fits all”. Organizations can apply Adaptive learning to provide a "special" feel to learning by following the learner's pace and monitoring the data to adapt to each student's level.

Adaptive learning is practiced by segmenting to keep students at appropriate levels. If there are many students, it will be challenging to personalize learning for each. By segmenting audiences, it may be easier to teach something. Segmentation can be done based on external characteristics (e.g., age group, function, etc.), competency performance, and stance on training (e.g., competence, level of participation, etc.).

Adaptive learning creates a fully customized learning path and adapts it to the student's abilities using analysis.

Feedback is also an essential part of learning. Here too, data can be used to generate and automate feedback based on performance and knowledge gaps. Adaptive learning systems can apply further education with a “call to action” that activates the learner based on their performance. Feedback is a meaningful way to help students progress and achieve goals, and it enables the learner to understand where they need improvement and guide them.

Van Seters, J. R., Ossevoort, M. A., Tramper, J., & Goedhart, M. J. (2012). The influence of student characteristics on the use of adaptive e-learning material.Computers & Education, 58(3), 942-952.

Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments.Computers in Human Behavior,27(1), 118-130.

Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an adaptivelearning system with multiple perspectives based on students' learning styles and cognitive styles. Educational Technology & Society, 16 (4), 185–200.