Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system - Parkavi, R.; Karthikeyan, P.; Sheik Abdullah, A. (Applied soft computing, 2024)
In the realm of Educational Technology, personalized learning is pivotal, yet predicting students' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using Machine Learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self-Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO's superiority. The study identifies the significant impact of ICT on self-progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision-making. Further exploration in real-world educational settings and comparative analyses with alternative optimization techniques are recommended for future research. •Study presents a decision support system using ML, swarm intelligence, XAI to gauge student performance comprehensively.•The proposed model utilizes Chaotic Particle Swarm Optimization (C-PSO) to achieve superior performance.•The study compares the performance of different metaheuristic-based optimization methods.•The research highlights the significant role of ICT in students' self-progress and its impact on learning outcomes.•Spearman Rank correlation confirms model's efficacy in pinpointing key risk factors statistically.