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.

Identifying learners' behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learners' behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learners' behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a 'navigation type' indicator to analyze learners' behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.

Automatic correction of misspelled words means offering a single proposal to correct a mistake, for example, switching two letters, omitting letter or a key press. In Arabic, there are some typical common errors based on letter errors, such as confusing in the form of Hamza ‫,ھﻤﺰة‬ confusion between Daad ‫ﺿﺎد‬ and Za ‫,ﻇﺎء‬ and the omission dots with Yeh ‫ﯾﺎء‬ and Teh ‫ﺗﺎء‬. So we propose in this paper a system description of a mechanism for automatic correction of common errors in Arabic based on rules, by using two methods, a list of words and regular expressions.

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Emotions have a major role in the player-game interaction. In serious games playing contexts, real-time assessment of the player's emotional state is crucially important to enable an emotion-driven adaptation during gameplay. In addition, a personalized assessment and adaptation based on the player's characteristics remains a challenge for serious games designers.This paper presents a generic and efficient emotion-driven approach for personalized assessment and adaptation in serious games, in which two main methods and their algorithms are proposed. The first one is a method for assessing, in real time, the player's emotion taking into account the personality type and the playing style of the player.The second one is an emotion-driven personalized adaptation method based on Markov modeling of dependency between the serious game events and the change in the player's emotional state. Therefore, the proposed approach has been evaluated by playing an affective vs. non-affective version of a serious game that we have developed to illustrate the applicability of the above-mentioned methods. The overall results showed that owing to our approach, a serious game become able to enhance its adaptivity toward playing outcomes and improve its overall playability.