Leila Ghorbel

An Adaptive Method for User Profile Learning

The user profile is a key element in several systems which provide adapted result to the user. Thus, for a better quality of response and to satisfy the user, the profiles content must always be pertinent. So, the removal of irrelevant content is necessary. In this way, we propose in this paper a semi-supervised learning based method for automatically identifying irrelevant profile elements. The originality of this method is that it is based on a new co-training algorithm which is adapted to the content of any profile. For this, our method includes a preparation data step and a classification profile elements process. A comparative evaluation by the classical co-training algorithm shows that our method is better.

Pertinent User Profile based on Adaptive Semi-supervised Learning

Abstract Several systems such as adaptive systems, etc. provide responses to the user by taking into account, among other, his profile. After each user-system interaction, new information should be added to the user profile content. By the time and after several updating operations, the profile can become overloaded and the removal of irrelevant content is necessary. In this paper, we tackle the profile overloading problem. We propose a new method based on co-training algorithm for detecting and removing irrelevant elements. Our method is automatically adapted to the content of any profile and allows us to obtain the most generic classifier to each one. An experimental study by qualitative and comparative evaluations shows that the proposed method can detect and remove irrelevant profile content effectively.

Learner’s Profile Hierarchization in an Interoperable Education System

In recent years, several education systems have been developed. Consequently, each learner can have different profiles which each one is related to a system. Each profile can be completed and enriched by the data coming from the other profiles in order to return results reflecting the learner’s need. The profile enrichment requires the establishment of an interoperable system which (i) resolves the problem of learner’s profile heterogeneity based on a matching process and (ii) integrates the data in the different profiles based on a data fusion process. The data fusion approaches mainly aim at resolving the conflicts occurring in the data values. They are based on non organized profiles which may produce inconsistent results. The profile organization is done either by using the machine learning techniques or the notion of temperature. In this paper, we propose a new data fusion approach to improve the conflict resolution by organized profiles. Each profile is organized by respectively merging a clustering algorithm and the temperature and by taking into account the data semantic relationship.

Improve the Adaptation Navigation in Educational Cross-systems☆

Abstract The adaptive educational systems include several solutions for providing personalized access to the learning process. The learners profile constitutes the key element of these solutions. Therefore, an educational system represents the learners profile by its own syntax, semantic and structure. Each system can have incomplete or partial learners data. As a consequence, there is a strong need to exchange the learners profiles between different systems to enhance and enrich the learners knowledge. However, the data exchange between the learners profiles implies interoperability problems. In our work, we are interested in the evolving learners profiles interoperability problem. In this context, we propose an architecture allowing the data exchange of the learners profile in educational cross-systems in order to improve the adaptation navigation. This architecture is automatically adapted to the learners profiles that evolve over time. These latter are syntactically, semantically and structurally heterogeneous. The evaluation values show the effectiveness of our approach.