## 1 Introduction
Moreover, it is able to increase the ability of an ITS to make the appropriate decision based on students' characteristics. The Bayesian network is a direct acyclic graph (DAG) that is used to model dependency between various concepts of a particular domain based on a probability distribution [15]. Bayesian networks have been used as a probabilistic framework to solve the problem of dynamically managing and updating student models [16]. These networks can represent different components of a student model such as knowledge level, learning styles, goals, and motivation, etc. Bayesian networks receive a great deal of attention from designers and developers of adaptive educational systems due to their sound mathematical foundations, as well as for their ability to handle uncertainty, using probabilities. Another tool, fuzzy logic is able to increase the ability of an ITS to examine and assess a student's academic performance, which is one of the most important parts of the educational process. Fuzzy logic can be viewed as an extension of the concept of a fuzzy set theory proposed by [PERSON] in 1965 [17]. It reflects how people think and attempts to model the human sense of words and decision-making. It is a form of multi-valued logic that allows the definition of intermediate values between conventional evaluations, such as true/false, yes/no, high/low, and big/small. The fuzzy logic system (FLS) maps a crisp input into a fuzzy output using the fuzzy sets theory. In general, an FLS consists of four stages: the fuzzifier, rule base, inference engine, and defuzzifier [18]. The fuzzy logic technique, with its ability to handle imprecise information and uncertainty, has been used to improve the performance of an adaptive e-learning system and assessing and evaluating student knowledge [19; 20]. This paper develops an intelligent tutoring system, called FB-ITS, using a hybrid method based on the Bayesian network and fuzzy logic to adaptively support students in learning an Excel course; the adaptation is achieved by modeling the students according to their knowledge level. Since this paper presents a study concerning the combination of two techniques of artificial intelligence that include the Bayesian network and fuzzy logic, in the development of ITSs where educational materialscan be personalized for individual learners, it takes a step toward the evaluation of the proposed system by comparing it with the existing models. FB-ITS takes the advantages of fuzzy logic and the Bayesian network. The fuzzy logic is used to determine the student performance in a particular topic of domain according to her/his prior knowledge; the Bayesian network is used to identify the state of the related topics in which they are ready to learn, or not, according to the evidence that comes from the fuzzy logic system. Therefore, this study develops and implements three versions of the ITS. The first version is created using the Bayesian network only; the second version is created using the fuzzy logic only, and the third version is created using a combination of the Bayesian network and fuzzy logic, and is called FB-ITS. Moreover, the evaluation is conducted by comparing the proposed FB-ITS with a traditional e-learning system that has been developed without the use of artificial intelligence technologies  
The purpose of this study is to develop and suggest an intelligent tutoring system, called FB-ITS, by combining the positive sides of both fuzzy logic and the Bayesian model, using a hybrid method to adaptively support students in learning, in which the adaptation is achieved by modeling the students according to their knowledge level. The research questions of this study, in line with this general purpose are:  
Does the building of a student model, using Bayesian networks based on fuzzy logic, increase the performance of ITS in terms of student academic performance compared to using fuzzy logic and Bayesian networks separately?