## 2 Related Works
They have been used in various ways, such as examining and assessing student characteristics to generate student profiles, for the purpose of evaluating their level of knowledge to be used as bases for building educational systems [28; 29]. These techniques are also used to facilitate the diagnostic process in order to adjust course content to meet the needs and preferences of each learner [30; 31]. [PERSON] et al. [32] proposed a personalized recommendation system for an English article based on accumulated learner profiles. This system employs the fuzzy inference method, memory cycle updates, learner preferences, and analytic hierarchy process to improve the English language abilities of students in an intensive reading environment. Moreover, in the research conducted by [PERSON] and [PERSON] [33], the rule-based fuzzy logic technique has been used for an automatic learning process to provide adaptive instructions to learners through an e-learning system. The fuzzy knowledge defined with personalized brilliancy evaluation (FuzKPBE) is introduced in this paper to predict the related course and concepts based on the specific individual skills of each concept. [PERSON] et al. [18] proposed a fuzzy logic based system that can learn the preferred knowledge delivery to the various students, based on their individual characteristics, to generate a personalized learning environment. A web-based educational system that performs individualized instruction on the domain of programming languages has been developed by [PERSON] and [PERSON] in [1]. This approach is implemented and evaluated in an educational application module called \"fuzzy knowledge state definer\" (FuzKSD). FuzKSD operates based on fuzzy cognitive maps (FCMs) to perform user modeling by dynamically identifying and updating the knowledge level of students related to all the domain subjects. Additionally, a system has been presented by [PERSON] et al. [34] to evaluate student performance in ITS environments, in which a fuzzy inference system provides the students with step-by-step instructions as to their learning status. Another well-known AI technique used to construct intelligent e-learning systems is Bayesian networks. Andes, which is an adaptive educational system developed by [PERSON] and [PERSON] [35], uses Bayesian networks to find former probabilities of knowing a set of knowledge elemental parts in teaching physics. Moreover, Bayesian networks have been used [36] to construct an ITS named Virtual Physics System (ViPS) to teach physics concepts in middle schools. In this system, the Bayesian network is used to represent the domain knowledge, to find the possible setups that can be created using components created by an individual student during the learning processes, as well as to generate an adaptive feedback and dynamic hints regarding student actions. Another Bayesian student model has been proposed to measure the difficulty of the problem of parameter specification [37]. This research conducted many experiments to compare the performance of two Bayesian student models. In one of them, the parameters were specified by the experts, in the other, these parameters were learned from the data. It was concluded that both student models provided a satisfactory result in estimating the variables of knowledge. The Personal Home Page (PHP) Intelligent Tutoring System (PHP ITS) was developed by [PERSON] and [PERSON] in [38]. PHP ITS aims to support novices in learning the PHP language for the purpose of developing dynamic web pages. This system provides exercises for students to solve and then provides appropriate feedback based on the answers. In PHP ITS, the Bayesian network is used to update the students' knowledge level of each topic based on student progress. In addition, the Bayesian network has been used to develop a solution-based intelligent tutoring system (SITS) for teaching computer programming [39]. SITS takes advantage of Bayesian networks to manage uncertainty based on probability theory for decision-making, to help students in learning.