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Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We examine the possibility of using a student's browsing path during a learning session, based on his navigation traces, to update the learner model. We assume that the domain concepts examined outside the learning platform but that are related to the course concepts are problematic to the learner. Knowing about these concepts may allow the course's author to adapt the course to the learner's needs regarding these concepts, as well as allow the tutor to help and assist the learner on these problematic concepts. We rely on Web data mining methods to filter, organize, and analyze the student's browsing path. More precisely, we use a domain ontology of the course and the similarities that exist between external documents (visited pages) and the domain concepts (the course keywords). This analysis process makes it possible to detect students' learning difficulties and to adapt the course based on the learner's model.

This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data. One of the main challenges facing the light stemming algorithm is cutting off the input word, to extract the initial segments. When initiating the light stemmer with strong initial segments, the final extracting stems and roots will be more accurate. Therefore, a new enhanced segmentation based on deploying the Direct Acyclic Graph (DAG) model is utilized. In addition to extracting the powerful initial segments, the main two procedures (i.e., stems and roots extraction), should be also reinforced with more efficient operators to improve the final outputs. To validate the proposed enhanced stemmer, four data sets are used. The achieved stems and roots resulted from our proposed light stemmer are compared with the results obtained from five other well-known Arabic light stemmers using the same data sets. This evaluation process proved that the proposed enhanced stemmer outperformed other comparative stemmers. [ABSTRACT FROM AUTHOR]

Due the increasing number of published Semantic Web services (SWs) rendered the distributed discovery within repositories a critical issue and a major problem that can reduce the capability and functionality of SWs in terms of efficiency and scalability. Peer-to-Peer (P2P) computing is considered as the most dominant technology to discover new distributed and heterogeneous collaborative applications for SWs. In this paper, we propose an efficient approach for improving the performance and effectiveness of automatic and cooperative discovery of large-scale distributed systems in the unstructured P2P networks. The approach exploits a scalable epidemic algorithm that uses different sources of network knowledge, such as exponential distribution, to fulfill the users requirements in order to ensure high recall, further reduce the number of messages exchanged and reduce the execution time for discovering SWs in the unstructured P2P network. In order to improve the applicability of the scalable epidemic algorithm for discovering SWs, we propose the semantic matching of OWL-S process model which improves the recall while keeping an acceptable matching quality level. The experimental results show that our efficient approach is able dynamically to adapt to network changes and preserve high levels of recall. [ABSTRACT FROM AUTHOR]

Several education hypermedia systems (EHS) use learning styles (LS) as a criterion for adaptation and tracking. To measure these styles, EHS are generally based on the questionnaires provided by the used LS model, and that learners should answer before the first session. This approach has a major drawback: learners' LS are defined only once. To overcome this limitation, recent researches are currently being done on the detection of LS based on learner's interaction traces. Their general criticism is related to the use of a specific environment, and therefore specific traces and indicators. Therefore, we aim to identify the learner's LS automatically, based on simple navigation traces. In this paper, we present experimental results of identification of sequential/global and active/reflective LS, for 45 students, using supervised classification. The findings provide initial evidence that LS can be automatically identified based on learners' navigation behaviours. [ABSTRACT FROM AUTHOR]

The remote learning evolution and particularly the e-learning permits to more and more people access to education. One of the learning fields which are to be developed is the learner evaluation. The accuracy and the pertinence of the evaluation results can provide information which can be very exploitable by the tutor in order to let the latter help the learner who has some troubles. The evaluation can also be exploitable by the learner because it provides a feedback on what he has really understood. Within this context we are interested by two types of cognitive evaluations, the first one concerning the self-evaluation by questionnaires with multiple choices and the second one concerning the collective work evaluation. For the first case, the work consists to make the questionnaires with multiple choices intelligent, e.g., the questions which will be asked will progress in junction of the learner's answers in order m identify the knowledge on which he has difficulties. In the second case, we will use a multi-agent system in order to achieve the evaluation of a group of learners working on the same project. [ABSTRACT FROM AUTHOR]

Arabic diacritics are often missed in Arabic scripts. This feature is a handicap for new learner to read َArabic, text to speech conversion systems, reading and semantic analysis of Arabic texts. The automatic diacritization systems are the best solution to handle this issue. But such automation needs resources as diactritized texts to train and evaluate such systems. In this paper, we describe our corpus of Arabic diacritized texts. This corpus is called Tashkeela. It can be used as a linguistic resource tool for natural language processing such as automatic diacritics systems, dis-ambiguity mechanism, features and data extraction. The corpus is freely available, it contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language. The corpus is collected from manually vocalized texts using web crawling process.