A Hybrid Model of Enhanced Teacher Learner Based Optimization (ETLBO) with Particle Swarm Optimization (PSO) Algorithm for Predicting Academic Student Performance

Objectives: A hybrid ETLBO-PSO model is developed to improve student performance predictions. It assesses intellectual, social, and economic background of students to increase accuracy of students performance predictions. The model optimizes selecting features, which reduces redundancy and increases efficiency. The efficacy is compared with existing Educational Data Mining techniques. Methods : This study integrates Enhanced Teachers Learners Based Optimization (ETLBO) and Particle Swarm Optimization (PSO) algorithm for optimal feature selection. The suggested technique is utilized as an algorithm for selecting features to identify the most significant elements for predicting student academic performance. The efficacy of the proposed feature selection technique is evaluated using three machine learning classifiers: Extreme Gradient Boosting (XGB), Light Gradient Boosting (LightGB), and Category Gradient Boosting (CatGB) in Student achievement Dataset in secondary education for Mathematics. Findings: The experimental results of ETLBO-PSO provides sustained excellent model performance while reducing accuracy decline. The Meta-Class model of ETLBO-PSO has an F1-score of 82.43%, which makes it an increasingly robust and reliable strategy. Furthermore, an innovative visual and intuitive method is employed to identify the aspects that most significantly impact the score, facilitating the interpretation and comprehension of the complete model. Novelty: ETLBO_PSO is integrated with SHAP (SHapley Additive exPlanations), and Meta-class Model are used to optimize student performance predictions with higher accuracy. Unlike traditional approaches, it continuously refines selecting features throughout training, solving high-dimensional data issues. SHAP's approach assures precise feature attribution, hence improving accessibility and making decisions. Keywords: Feature Selection, Enhance Teacher Learner based Optimization, Particle Swarm Optimization, Academic Student Performance, Classification Algorithm, Optimization Techniques, XGBoost, LGBoost, CATBoost