December 4, 2025F1000ResearchOpen Access

Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement

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Hybrid PSO-GA algorithm improves student performance evaluation, suggesting enhanced holistic assessment frameworks.

A hybrid Particle Swarm Optimization and Genetic Algorithm enhances student performance metrics, leading to improved holistic evaluations.
The model achieved a mean fitness score of 1.273 and a standard deviation of 0.004, showcasing its effectiveness.
Observational analysis with 25, 50, and 100 population sizes optimized cognitive effort allocation based on workload limits.
Educational policy implications highlight the need for adaptive workload management and robust assessment strategies.

Jena et al. (2025) studied this question.

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