ADAPTING LEARNING IN A CONTEXT-AWARE MOBILE LEARNING SYSTEM USING THE DYNAMIC CASE BASED REASONING

Artificial intelligence has become an increasingly present technology in our daily lives and mobile applications are no exception. Artificial intelligence offers many advantages in the development of mobile applications, on the one hand improving the user experience and on the other hand opening up new possibilities for developers. Learning is one of the areas concerned with the integration of artificial intelligence in the development of mobile applications, it is mobile learning which consists of using mobile devices to access content and educational resources at anytime and anywhere by adapting and creating individualized learning paths based on each learner's needs, interests and prior knowledge. This article presents an approach to an adaptive learning system designed for mobile devices. This approach allows real-time personalized monitoring of the learner during the learning process, providing a personalized learning path based on observation of the context and centered on the learner using mobile devices. This monitoring is carried out using the Felder-Silverman learning style to detect the learning style of each learner, and the artificial intelligence approach of Case-Based Reasoning to ensure automatic prediction and adaptation to the dynamic changes in the behavior of the learner during the learning process (their profile and the characteristics of their mobile device) through the search for similar past learning paths.