Exploring the impact of data analysis on identifying key predictors of student performance and improving outcomes for diverse groups

Identifying key forecasters of student performance is important for improving well-organized educational strategies. Traditional studies commonly focus on standardized student populations, but thoughtful performance predictors between diverse groups can considerably enhance outcomes for all learners. This study aims to rigorously examine student performance by enhancing a novel data-driven strategy that identifies and analyzes key predictors across diverse educational groups, with the ultimate goal of improving educational outcomes for all learners. Data is collected from various educational sectors indicating diverse student backgrounds, including demographic information, academic records, and socio-economic status. This diversity ascertains a complete analysis of factors impacting multiple student groups. Normalization technique is helpful to make sure dependent data scaling. To decrease dimensionality, Principal Component Analysis (PCA) is used to extract the most significant features while decreasing data unemployment. Data from various student groups is pre-processed and analyzed through a proposed framework expected to determine key performance predictors. The data is processed through the Advanced Penguin Search Optimized Adaptive Boosting (APS-AdaBoost) machine learning (ML) model. APS-AdaBoost is an advanced ensemble learning technique that employs the Penguin Search Optimization Algorithm to fine-tune the adaptive boosting process. In terms of predicting student outcomes, the suggested APS-AdaBoost model performed better than conventional approaches, as demonstrated by its 0.801 accuracy, 0.798 precision, 0.754 recall, 0.762 F1-score, and 0.891 AUC. These findings illustrate the efficacy of APS-AdaBoost in recognizing critical performance indicators for a range of student populations. This model focuses on identifying predictors of performance across diverse student groups and optimizing learning pathways to enhance academic outcomes. Results show that APS-AdaBoost exceeds conventional models mostly in addressing the difficulties of diverse student backgrounds. The findings highlight the efficiency of modified interventions based on identified key predictors, resulting in improved outcomes for inadequately represented student groups.