Hybrid Soft Computing System for Student Performance Evaluation
DOI:
https://doi.org/10.24193/subbeng.2023.1.1Keywords:
Hybrid; Soft Computing; Clustering Algorithm; Machine learning; Optimization Algorithm.Abstract
Education Institutions have deployed technology accelerated learning systems and innovations for effective learning outcomes. Evaluating student’s performance in these systems must align with the cognitive, affective, and psychomotor learning domains. In this research, a Hybrid soft computing system comprising of the Clustering Algorithm, Machine learning technique, and Optimization algorithm were hybridized and implemented to evaluate student academic performance using academic, social, and economic data of students. The proposed model demonstrated the best results with the lowest mean square error (MSE) and root mean square error (RMSE) values of 0.17 and 0.41, respectively. Additionally, the GANFIS model achieved values of 0.25 and 0.50, respectively, which slightly outperformed the proposed FCM-PSOANFIS model. The proposed model works better with bigger datasets, and it de-livers higher predictive findings under settings that depict student learning capacities while assessing student academic achievement.References
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