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Abstract
Today, the integration of web services and agent technology into Internet applications has attracted the attention of many researchers, so that these applications allow a web service to call an agent service and vice versa. Web services are emerging and promising technologies for the development, deploy-ment and integration of the Internet applications and the use of agents makes them dynamic and automatic, they can provide updates when there is new infor-mation available and improve the qualities of web services by exploiting the ca-pacities and the characteristics of agents. In this context, we propose a prototype of a multi-agent adaptive learning system based on Incremental Hybrid Case Based Reasoning in order to support the learner in his learning process by offer-ing him a learning path adapted to his profile and predict his future learning. This support will be achieved through the execution of a hybrid cycle of Case Based Reasoning which brings together a set of agents collaborating and interacting with each other to provide specific services.
Highlights
Since the appearance of the web, it was a huge warehouse of text and images, its evolution has made it become a service provider
To overcome the difficulty of real-time monitoring, we propose the implementation of an multi-agent adaptive learning system based on Incremental Hybrid Case Based Reasoning (IHCBR) in order to support the learner in real time during his learning process by offering him individualized learning according to his needs and preferences as well as predicting his future behavior, by integrating at each step of the hybrid cycle of Case Based Reasoning (CBR) [3] a set of agents to ensure a specific task and benefit advantages of agent technology
We propose a prototype of a multi-Agent adaptive learning system based on IHCBR which is based on the past experiences of other learners and the Felder-Silverman learning style model (FSLSM) [15], in order to create dynamically the personalized learning paths, to provide individualized monitoring in real time of each learner and to predict their future learning situation which changes dynamically over time
Summary
Since the appearance of the web, it was a huge warehouse of text and images, its evolution has made it become a service provider. The exponential growth of the Web has contributed to the development of adaptive learning by expanding its possibilities in terms of interaction such as personalization This growth has enabled it to involve a considerable variety of technologies derived from artificial intelligence. Adaptive learning hypermedia systems allow to personalize the content and the learning path in an online learning environment to minimize learner disorientation [1], using methods and techniques to adapt content of these systems to the needs, interests, objectives, and individual traits of the learners [2] This personalization requires individualized monitoring of the learners which plays an important role, because without information on the progress and the results obtained by the learners, it will be very difficult to support and control their behaviors. We present the implementation of our architecture by using the JADE platform and the WSIG component
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