## 1 Introduction  
Recently, there has been a rapid growth in web-based intelligent tutoring systems (ITSs) to support teaching processes, with the aim of helping students adaptively navigate through online learning materials. Web-based educational systems facilitate distance learning, and offer easy access to any knowledge domain and learning process, at any time, for learners from different backgrounds with different needs, preferences, and characteristics [1]. According to [PERSON] et al. [2], the differences among student characteristics play an important role in developing web-based educational environments. Therefore, successful online teaching demands multimedia techniques, adaptive techniques, and reasoning abilities in addition to a user-friendly interface. Students often enjoy the ease of use and communication in virtual learning environments, as well as personalized learning paths [3]. Thus, the challenge is to develop web-based learning systems that are dynamically adapted to each individual user in order to deliver knowledge effectively. Therefore, web-based educational systems have to be dynamically adaptable for the individual learner and they must be capable of monitoring the learner activities and be capable of providing personalization for specific needs, preferences, and knowledge. Moreover, these systems have to offer students more freedom in order to navigate through online course content and control their learning pace and learning sequence [4]. Because of these features, educational institutions, whether traditional or online, rapidly began to adopt adaptive learning environments, virtual learning environments, and e-learning management systems to increase the number of students of online courses [3; 5]. Adaptive navigation supported technology, which has the ability to help students acquire knowledge faster and improve learning outcomes, is one of the common technologies applied to web-based educational systems [6]. Many intelligent tutoring systems (ITSs) adopt such technology, such as the intelligent tutoring systems platform (ITSPL) [4], which supports student navigation in cyberspace by adapting to the goals and knowledge of the individual user [7; 8]. The ability of an ITS to provide adaptivity is based on the technology of student modeling. According to [PERSON] [9], student modeling is a process that is responsible for representing a student's goals and needs, analyzing the student performance and determining prior and gained knowledge. However, the student modeling process is not a black-or-white one, but it often deals with uncertainty, and it cannot be accurately said that a student has learned the concept or not [1]. Therefore, the challenge is to construct an effective student model, which is the key component in an ITS to deal with uncertainty. Uncertainty in educational systems can occur from examining student variables such as assessing the level of student knowledge [10]. ITSs use artificial intelligence (AI) techniques to automatically adapt the teaching content to fit learners' needs and goals. Personalized and adapted e-learning environments can be established using AI techniques, which are mainly applied in knowledge representation, managing learning strategies, and monitoring students' status [11]. AI is a branch of computer science interested in making computers behave like human beings. Its rich resources of tools, technologies, and paradigms of computing such as fuzzy logic and Bayesian networks, and it has proved to be extremely useful in solving challenging problems in different fields as well as educational environments involving incomplete and/or uncertain knowledge. Bayesian network and fuzzy logic are widely used in the literature to develop the student model and solve the problem of uncertainty in adaptive e-learning systems [12; 13; 14]. The Bayesian network is a tool used to manage knowledge from different situations and model the interdependencies between domain topics.