This paper proposes a similarity measure to compare cases represented by labeled graphs. We first define an expressive model of directed labeled graph, allowing multiple labels on vertices and edges. Then we define the similarity problem as the search of a best mapping, where a mapping is a correspondence between vertices of the graphs. A key point of our approach is that this mapping does not have to be univalent, so that a vertex in a graph may be associated with several vertices of the other graph. Another key point is that the quality of the mapping is determined by generic functions, which can be tuned in order to implement domain-dependant knowledge. We discuss some computational issues related to this problem, and we describe a greedy algorithm for it. Finally, we show that our approach provides not only a quantitative measure of the similarity, but also qualitative information which can prove valuable in the adaptation phase of CBR.

Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

Active reading of audiovisual documents is an iterative activity, dedicated to the analysis of the audiovisual source through its enrichment with structured metadata and the definition of appropriate visualisation means for this metadata, producing new multimedia objects called hypervideos. We will describe in this article the general decomposition of active reading and how it is put into practice in the Advene framework, analysing how its activities fit into the Canonical Media Processes model.

In this paper, we present how the SEPIA system can be used to plug pedagogical and technical assistance systems in applications used by learners in an educational context. The SEPIA system consists in two main tools: an assistance editor that enables assistance designers to specify the assistance they wish for existing applications, and a generic assistance engine that executes the specified assistance in order to provide the application end-users with personalized assistance. We also present an experimentation of an assistance system setup with SEPIA in the context of a bachelor degree.