Predicting Academic Performance of University Students Using Adaptive Neuro Fuzzy Inference System (ANFIS)- Subtractive Clustering Algorithm (ANFIS-SC): A Case Study in the UK

In academic institutions, among the most important success criteria is students’ academic performance. However, one of the biggest challenges facing institutions has been the early detection and enhancement of students' academic performance at all levels. Students may run into several issues that hinder their ability to study, hereby, having a detrimental effect on their academic achievement. These problems can be effectively resolved if student data is pre-analyzed with early performance predictions, to enable prompt support decisions. Thus, this work applied an Adaptive Neuro Fuzzy Inference System (ANFIS) with subtractive clustering to predict students’ academic performance and identify factors that influences the students’ performance. Hence, this would be helpful in making inform decisions that support students who require assistance; also, taking effective steps to improving their academic performance. Furthermore, due to the benefits of mixing both neural networks and fuzzy systems, the applied ANFIS
model, which is a hybrid learning algorithm processes information quickly to produce more comprehensible and interpretable insight. Also, subtractive clustering (SC) was used to group similar characteristics in the dataset, to decrease the number of rules and membership functions of ANFIS; hereby, reducing the complexity of ANFIS. Academic records of computer-science students at the University of Huddersfield from 2017-2022 were used, which provided several features useful in predicting the students’ performance. Evaluation the results of ANFIS-SC with recommended machine learning techniques in articles showed that Decision Tree, Ada Boost Regression, Neural Network have a high accuracy score, likewise, the Adaptive Neuro Fuzzy Inference System with Subtractive Clustering (ANFIS-SC).