A Hybrid FCM-PSO-ANFIS Model for Predicting Student Academic Performance

Authors

DOI:

https://doi.org/10.12928/jstie.v12i3.29519

Keywords:

Student Performance Prediction, Hybrid Soft-Computing Model, Fuzzy C-Means Clustering, Particle Swarm Optimization, Adaptive Neuro-Fuzzy Inference System

Abstract

This study introduces a novel hybrid soft-computing model integrating Fuzzy C-Means (FCM) clustering, Particle Swarm Optimization (PSO), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance student academic performance prediction in higher education. We developed a hybrid FCM-PSO-ANFIS model using a comprehensive dataset encompassing pre-admission data, academic constraints, and student performance records. The model's performance was evaluated against standard ANFIS and Genetic Algorithm-optimized ANFIS using multiple error metrics. The proposed PSO-optimized ANFIS model demonstrated superior performance, achieving the lowest error metrics in both training (MSE: 0.16667, RMSE: 0.40826) and testing (MSE: 0.19748, RMSE: 0.44439) phases. Comparative analysis showed that our model outperformed standard ANFIS and GA-optimized ANFIS in terms of prediction accuracy and generalization capability. The hybrid FCM-PSO-ANFIS model offers a robust, adaptive tool for early identification of at-risk students, enabling timely interventions and personalized learning approaches. This research contributes to improving educational outcomes and retention rates in higher education institutions by providing more accurate and reliable predictions of student performance. Future work should focus on enhancing model interpretability, addressing computational complexity, and exploring applications in diverse educational contexts.

References

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Published

31-10-2024

Issue

Vol. 12 No. 3 (2024): Oktober

Section

Articles

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Copyright (c) 2024 Victor Osasu Eguavoen

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