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A cooperative Cuckoo Search–hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance

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The accurate prediction of student academic performance facilitates admission decisions and enhances educational services at tertiary institutions. This raises the need to have an effective model that predicts student performance in university that is based on the results of standardized exams and other influential factors, such as socio-economic background. In this study, a novel approach to the prediction of student academic performance based on the Cuckoo Search (CS) - hierarchical Adaptive Neuro-Fuzzy Inference System (HANFIS) model is proposed. Firstly, the most appropriate factors were selected and a dataset was constructed. Then, the proposed model was used to predict academic performance. In the model, a hierarchical structure of ANFIS was suggested to solve the curse-of-dimensionality problem, the CS algorithm was utilized to optimize the clustering parameters which helped form the rule base, and ANFIS optimized the parameters in the antecedent and consequent parts of each sub-model. The findings showed that the proposed model is accurate and reliable. The results were also compared with those obtained from the Artificial Neural Network (ANN), GA-HANFIS (the combination of Genetic algorithm and HANFIS), and HANFIS models, indicating the proposed approach performed better. It is expected that this work may be used to assist in student admission procedures and strengthen the service system in educational institutions. Do you want to read the rest of this article?Request full-text A cooperative Cuckoo Search - hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance. Available from: https://www.researchgate.net/publication/278074839_A_cooperative_Cuckoo_Search_-_hierarchical_adaptive_neuro-fuzzy_inference_system_approach_for_predicting_student_academic_performance [accessed Aug 31, 2017].

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