# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 1 Introduction
The authors applied their model on real data from an undergraduate program in the Electrical Engineering and Information Technology department at an Indonesian university. In [19], a fuzzy neural network model is used to predict the students' academic performance. In this paper, the authors considered in their model most of the students' academic profiles, including age, gender, education, past performance, etc., and they designed their model using MATLAB and JAVA. In [20], a data-driven fuzzy rule approach was proposed for evaluating student academic performance. The authors performed Criterion-Referenced Evaluation (CRE) and Norm-Referenced Evaluation (NRE). They used training data from students' performance in two courses in the form of numerical crisp scores, then used a fuzzy approach for utilizing these data in the form of fuzzy values; then, they applied CRE and NRE to see how a fuzzy rule-based approach can be used for aggregation of students' academic performance. In [21], the authors used the concept of Pythagorean fuzzy sets in academic performance. They provided a decision-making approach for career placements based on academic performance using max-min-max composition. They used their approach in solving the multi-criteria decision-making problems. In [22], the authors developed an intelligent tutoring system using Bayesian networks and fuzzy logic to support students in learning environments and enhance their academic performance. Their system was based on the classical architecture of intelligent tutoring systems, and they used a fuzzy logic system to determine the students' performance in a particular topic, taking two factors into account: the pretest grade and the topic test grad. They used three fuzzy sets for each input variable to describe the students' grade as poor, good, or excellent and two fuzzy sets to describe the output (low and high). They used 9 fuzzy rules configured with two input variables (Pretest-Grade and TopicTest-Grade) and one output variable (Performance). In [23], the authors presented an integration framework that integrated fuzzy clustering and regression to predict the students' final grade. They composed four distinct subsystems, each of which implements partition, regression, offset value generation, and estimation. These activities were integrated together to form the overall student score-predicting mechanism. They used four datasets, each containing hundreds of instances collected from two Portuguese schools (GP and MS) about their students in Math and Portuguese language study. In the complete dataset with more than 30 attributes, they split each dataset into subsets for 10-fold cross validation, and they reported their results based on an average of 10 times of the 10-fold cross validation. In [24], a fuzzy learning model was proposed to predict student academic performance. The model in this paper was built based on methods that merge the fuzzy concept with the concept automated machine learning. The authors used the data of 866 students who attended, 456 of which were male and 410 females; they collected their data from the three compulsory courses in the spring semester of two successive academic years, 2017-2018 and 2018-2019, at the Aristotle University of Thessaloniki. Then, they employed fuzzy-based classifiers to student performance (pass/fail) in higher education courses. The authors examined six fuzzy classifiers, FMARR, MFPC using OWA and AIWA aggregation operators (MFPC OWA/ AIWA), Local and Global Genetic FPCs (FPC GA/LGA), PTs, PTTDs, and the hybrid FPC using OWA operator (FPC OWA). In [25], a decision-making fuzzy VIKOR approach was used to rank academic performance. The authors used the fuzzy set concepts in their approach; they ranked the academic performance in linguistic variables (excellence, honors, average, pass, and fail), and then they transformed these linguistic variables into fuzzy numbers. In [26], a neuro-fuzzy model based on the Cuckoo Search algorithm was used for predicting the students' academic performance.