Adaptive learning (adaptive learning) is a method that individualizes the teaching-learning strategies according to the needs and preferences of the student. It has particular applications in blended and online education, developed with the support of virtual learning environments.
These platforms collect data associated with the student’s progress, their performance, their interests, their abilities, and their learning style. Through adaptive learning technologies, this data is analyzed and used to create personalized experiences.
Origins and actuality of adaptive learning
Adaptive learning is a model that is associated with the incorporation in teaching the computer and the movement of artificial intelligence (AI), into the 1970s.
From a methodological point of view, adaptive learning is based on the theory of artificial learning. These are three of its main antecedents:
- The scales of levels and learning capacities in children of the psychologists Alfred Binet and Théodore Simon.
- The theory of programmed learning and the teaching machine of Burrhus Frederic Skinner.
Intelligent teaching programmed tutorial systems. For example, the PLATO system by Donald Bitzer and the Scholar by computer scientist Jaime Carbonell.
From the point of view of adaptive learning technologies, its origin dates back to the adaptive theories presented by Snow around 1980. This researcher and his colleagues highlighted the importance of considering human psychological differences and the demands of each student, at the time of offer you training support.
In current adaptive learning models, three main elements converge:
- Sophisticated technologies and affordable platforms: more powerful computers, mobile devices, high-speed networks, and cloud computing.
- Analytical and forecasting technologies: big data, analytics learning (learning analytics), AI, and machine learning (machine learning).
Cognitive models that study how to improve people’s learning.
- These elements are integrated into Learning Management Systems, also known as Learning Management systems (LMS).
Main characteristics of adaptive learning
Adaptive learning models are characterized by the following advantages:
- The learning process is personalized and individual.
- The activities, content, routes, and learning styles are automatically adapted, based on the indicators and data collected.
- They promote autonomous learning.
- They motivate students based on positive stimuli.
- Hits are rewarded and immediate feedback is provided when mistakes are detected, including through the use of chatbots, online tutoring, and automated evaluation.