# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 1 Introduction
The authors proposed a hierarchical Adaptive Neuro-Fuzzy Inference System model. They used the hierarchical structure of ANFIS in solving the curse-of-dimensionality problem, and they used CS algorithm to optimize the clustering parameters; then, they formed the rule base. In [27], the authors developed a fuzzy logic application to evaluate the students' academic performance. In this paper, the authors used Mamdani fuzzy inference methods. They used three exams' results as input variables of the fuzzy logic-based expert system. Each input variable had five triangular membership functions, and the triangular membership function was specified by three parameters (a, b, c). The authors applied the system to a group of students and compared the results to traditional methods of performance evaluation. The authors used MATLAB for implementing their fuzzy system and tested it using 20 students' marks obtained from Exam1, Exam2, and the practical Exam. In [28], a fuzzy classification system was proposed to evaluate the students' academic performance. The system used in this paper is based on qualitative link analysis methodology instead of using a rule-based method. The authors took a fuzzy classification problem as link analysis. They provided a fuzzy classification framework that integrated network representation scheme and link-based analysis. They represented the variable terms and classes as graph nodes, while their observation associations are encoded as corresponding edges. Then, the likelihood that a new data instance belonging to a specific class is determined via the notion of link-based similarity measure. To measure the similarity, the authors used link analysis based on examining relation patterns amongst objects in a given link network, which can be specified as an undirected graph. In [29], a genetic fuzzy model is proposed to evaluate distance education students' academic performance. The authors proposed a mathematical model that uses a combination of genetic algorithms and fuzzy logic to evaluate the academic performance of distance education students. The authors first predicted the pass of the grade using a classical fuzzy model. Then, they improved the model using a genetic fuzzy approach. They developedtheir model using a dataset of distance education students from the Moodle record for a MATLAB course at a Turkish university. In [30], the authors proposed a fuzzy logic system that seeks to facilitate educational institutions in their understanding of the existing relationships among the different academic degrees and, in turn, to carry out corrective tasks where the impact of academic performance is higher and then predicted student marks. They processed several algorithms, aiming to optimize their process and their results after analyzing the relevant data and choosing the optimization algorithms. Then, they implemented a Takagi-Sugeno-type fuzzy inference system (logic-based system) which consisted of multiple inputs and four outputs. In [31], a neuro-fuzzy model was used to predict the students' academic performance. The authors examined the factors influencing student academic achievement. They used the variations in courses' grades among students using the course's category, the student's course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. In [32], a fuzzy logic approach was used to predict the students' outcomes in regard to the instructor's persona. The objective of this paper was to realize in which way fuzzy logic can be applied to predict the outcome of students' performance with three parameters--trust, perception, and usefulness of the instructor--while seeking help in academics. In this paper, the authors used the Mamdani fuzzy method. They did their study on a survey data of 1250 students from various colleges. They handled partial truth range varying from absolutely true to absolutely false and used Mamdani-type fuzzy logic for predicting students' outcomes.