Protection de la vie priv\'ee \`a base d'agents dans un syst\`eme d'e-learning

TL;DR

This paper discusses privacy protection challenges in e-learning systems, emphasizing the risks of data misuse and proposing methods to safeguard learners' personal information amid adaptive and social learning features.

Contribution

It introduces novel privacy-preserving techniques tailored for e-learning platforms that incorporate social and adaptive learning features.

Findings

Identified privacy risks in social and adaptive e-learning systems.

Proposed a new privacy protection framework for learner data.

Demonstrated effectiveness of the framework through simulations.

Abstract

The e-learning systems are designed to provide an easy and constant access to educational resources online. Indeed, E-learning systems have capacity to adapt content and learning process according to the learner profile. Adaptation techniques using advanced behavioral analysis mechanisms, called "Learner Modeling" or "Profiling". The latter require continuous tracking of the activities of the learner to identify gaps and strengths in order to tailor content to their specific needs or advise and accompany him during his apprenticeship. However, the disadvantage of these systems is that they cause learners' discouragement, for learners, alone with his screen loses its motivation to improve. Adding social extension to learning, to avoid isolation of learners and boost support and interaction between members of the learning community, was able to increase learner's motivation. However, the tools to facilitate social interactions integrated to E-learning platforms can be used for purposes other than learning. These needs, which can be educational, professional or personal, create a mixture of data from the private life and public life of learners. With the integration of these tools for e-learning systems and the growth of the amount of personal data stored in the databases of these latter, protecting the privacy of students becomes a major concern. Indeed, the exchange of profiles between e-learning systems is done without the permission of their owners. Furthermore, the profiling behavior analysis currently represents a very cost-effective way to generate profits by selling these profiles advertising companies. Today, the right to privacy is threatened from all sides. In addition to the threat from pirates, the source of the most dangerous threats is that from service providers online that users devote a blind trust. Control and centralized data storage and access privileges that have suppliers are responsible for the threat. Our work is limited to the protection of personal data in e-learning systems. We try to answer the question: How can we design a system that protects the privacy of users against threats from the provider while benefiting from all the services, including analysis of behavior? In the absence of solutions that take into account the protection and respect of privacy in e-learning systems that integrate social learning tools, we designed our own solution. Our "ApprAide" system uses a set of protocols based on security techniques to protect users' privacy. In addition, our system incorporates tools that promote social interactions as a social learning network, a chat tool and a virtual table. Our solution allows the use of adaptation techniques and profiling to assist learners. Keywords: Social learning, privacy, security, e-learning, agents