REAL-e

Reliable Architectures for Adaptive and Mobile e-Learning Applications

The main objective of this research is to propose an architectural model for reliable, adaptive and mobile e-learning applications using the mobile agents paradigm.
Learning is a source of health, wealth and happiness. Effective learning, starting at birth and continuing into old age, gives each individual the best hope of a successful life. The first priority of the new learning agenda is summarized in the phrase "lifelong learning for all". This phrase demonstrates how far ideas about learning and attitudes to education have changed in recent years.
The rapid growth of computers, the arrival of Internet and the gain in the popularity of distance education greatly influence our educational environment. Educational information on the Web has increased exponentially, and web-based learning is currently an important research-and-development area. Web-based learning environments are becoming mainstream applications for the educational community. However, they have a number of common deficiencies, such as limited or non-existent adaptability for individual students in different contexts (related to the fact that a "virtual" teachers do not "really" know their students), the real-time interaction between student and system is hard to achieve because of connection reliability and bandwidth limitations. As in real life, student behavior needs to be understood before correcting or educating him. To understand a student’s behavior, it should first be observed within its relative context. Using brain, mind and learning models, the student’s behavior can be analyzed in order to develop the adequate learning method for that student. Several points have to be taken into account, such as the motivation to learn, the student’s environment, the understanding, rather then memorizing, his past learning, in support of all subsequent learning, and the usefulness of what he is currently learning. To find out all of this information, the student needs a permanent mentor by his side. In this particular case, mobile agents have ample potential to address these deficiencies.

Contact: Nicolas Guelfi
Project Page :