PhD theses

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Start date: 2010

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    Models and Tools for Usage-based e-Learning Documents Reengineering
    (2019-04-25) Sadallah, Madjid
    Providing high-quality content is of utmost importance to drive successful reading. Besides, designing documents that are received the way the author wishes has always been difficult, and the digital world increases this difficulty by multiplying the possibilities related to mixed medias and interactivity. This compels authors to continuously review the delivered content to meet readers' needs. Yet it remains challenging for them to detect the comprehension barriers that may exist within their documents, and to identify how these latter can be improved accordingly. This compels authors to continuously review the delivered content to meet readers' needs. Yet it remains challenging for them to detect the comprehension barriers that may exist within their documents, and to identify how these latter can be improved accordingly. In this thesis, we focus on an educational context, where reading is a fundamental activity and the basis of many other learning activities. We propose a learning analytics approach for assisting course authors to maintain their courses to sustain learning. The proposals are based on theoretical background originated from research on learning analytics, reading comprehension and content revision. We advocate \usage-based document reengineering", a process defined as a kind of reengineering that changes document content and structures based on the analysis of readers' usages as recorded in their reading traces. We model reading activity using the concept of reading-session and propose a new session identification method. Using learners' reading sessions, a set of indicators related to different aspects of the reading process are computed and used to detect comprehension issues and to suggest corrective content revisions. The results of the analytics process are presented to authors through a dashboard empowered with assistive features. We instantiate our proposals using the logs of a major e-learning platform, and validate it through a series of studies. The results show the effectiveness of the approach and dashboards in providing authors with guidance in improving their courses accordingly.
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    Scalable and Fault Tolerant Hierarchical B&B Algorithm for Computational Grids
    (2012-06-07) Bendjoudi, Ahcène
    Solving to optimality large instances of combinatorial optimization problems using Branch and Bound (B&B) algorithms requires a huge amount of computing resources. Nowadays, such power is provided by large scale environments such as computational grids. However, grids induce new challenges: scalability, heterogeneity, and fault tolerance. Most of existing grid-based B&Bs are developed using the Master-Worker paradigm, their scalability is therefore limited. Moreover fault tolerance is rarely addressed in these works. In this thesis, we propose three main contributions to deal with these issues: P2P-B&B, H-B&B, and FTH-B&B. P2P-B&B is a MW-based B&B framework which deals with scalability by reducing the task request frequency and enabling direct communication between workers. H-B&B also deals with scala- bility. Unlike the state-of-the-art approaches, H-B&B is fully dynamic and adaptive, meaning it takes into account the dynamic acquisition of new computing resources. FTH-B&B is based on new fault tolerant mechanisms enabling efficient building of the hierarchy and maintainingits balancing, and minimizing of work redundancy when storing and recovering tasks. The proposed approaches have been implemented using ProActive grid-middleware and applied to the Flow-Shop scheduling Problem (FSP). The large scale experiments performed on Grid’5000 proved the efficiency of the proposed approaches.
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    Scalable and fault tolerant hierarchical B&B algorithms for computational grids
    (Université Abderrahmane Mira de Béjaia : Faculté des Sciences Exactes, 2012-06-07) Bendjoudi, Ahcène; Talbi, El-Ghazali
    La résolution exacte de problèmes d’optimisation combinatoire avec les algorithmes Branch and Bound (B&B) nécessite un nombre exorbitant de ressources de calcul. Actuellement, cette puissance est offerte par les environnements large échelle comme les grilles de calcul. Cependant, les grilles présentent de nouveaux challenges : le passage à l’échelle, l’hétérogénéité et la tolérance aux pannes. La majorité des algorithmes B&B revisités pour les grilles de calcul sont basés sur le paradigme Master-Worker, ce qui limite leur passage à l’échelle. De plus, la tolérance aux pannes est rarement adressée dans ces travaux. Dans cette thèse, nous proposons trois principales contributions : P2P-B&B, H-B&B et FTH-B&B. P2PB& B est un famework basé sur le paradigme Master-Worker traite le passage à l’échelle par la réduction de la fréquence de requêtes de tâches et en permettant les communications directes entre les workers. H-B&B traite aussi le passage à l’échelle. Contrairement aux approches proposées dans la littérature, H-B&B est complètement dynamique et adaptatif i.e. prenant en compte l’acquisition dynamique des ressources de calcul. FTH-B&B est basé sur de nouveaux mécanismes de tolérance aux pannes permettant de construire et maintenir la hiérarchie équilibrée, et de minimiser la redondance de travail quand les tâches sont sauvegardées et restaurées. Les approches proposées ont été implémentées avec la plateforme pour grille ProActive et ont été appliquées au problème d’ordonnancement de type Flow-Shop. Les expérimentations large échelle effectuées sur la grille Grid’5000 ont prouvé l’efficacité des approches proposées