Naïve Bayes-Based Intelligent Model For Identification and Analysis of Learners' Intelligence Potential

Academic achievement, especially report card scores, is frequently the only factor used to evaluate a student's intelligence in education, ignoring other aspects, including kinesthetic, musical, interpersonal, and intrapersonal intelligence. This traditional method limits opportunities for students whose abilities are outside of typical academic disciplines, marginalizing them. In order to close this gap, the current work is to: (1) develop a decision support system (DSS) that uses the Naïve Bayes approach to determine students' intelligence potential; and (2) integrate the method into the DSS to accomplish rigorous and precise identification. Using PHP and a MySQL database, the system was created utilizing the Unified Modeling Language (UML), which included use case, activity, sequence, and class diagrams. Because of its track record of handling labeled data and producing highly accurate probabilistic predictions, Naïve Bayes was chosen. To capture all of the learner characteristics, information was gathered through interviews and observation. Based on the results, the suggested method successfully recognizes various forms of intelligence and offers tailored learning suggestions that are in line with each student's abilities. The objective, effective, and data-driven approach to intelligence identification provided by this study helps educators create more inclusive and talent-oriented teaching methods. Ultimately, the model fortifies the paradigm of holistic education by guaranteeing that teaching practices are enriched by the recognition, cultivation, and integration of a variety of intelligences.