A multidimensional analysis of self-esteem and individualism: A deep learning-based model for predicting elementary school students' academic performance

Abstract

The current research descends into the discrete connection between self-esteem, individuality and academic achievement among primary school learners. It addresses the essential need for predictive algorithms to analyze and forecast academic results. Self-esteem and identity, as diverse psychological variables, have been highlighted as crucial determinants in creating academic success. The fundamental goal is to develop an effective prediction model for elementary school children's academic performance by including self-esteem and individuality in the analytical framework. We suggested an Owl Search Optimized Dynamic Deep Neural Network (OSO-DDNN) based model for making more precise predictions. The sample size for the research was 147,210 elementary school students who took the LegiLexi exam between 2016 and 2021. To standardize the data and improve model performance, the suggested methodology uses a pre-processing strategy based on the Min-Max scalar approach. Furthermore, Independent Component Analysis is used to detect and extract essential factors related to self-esteem, individuality and academic achievement. The architecture and parameters of the suggested approaches are optimized using the OSO algorithm to provide a prediction model that remains accurate and efficient. We quantify the effectiveness of the assessments using the following metrics: precision, recall, accuracy and F1-score to develop a design using conventional methods. The findings demonstrate that the model's resilience and promise for anticipating primary school pupils' academic success are based on psychological aspects.The OSO-DDNN is an essential instrument for educators and policymakers in identifying at-risk pupils and implementing targeted interventions, supporting a comprehensive approach to education that combines psychological well-being with academic performance.