Determining the Relative Importance of Factors Affecting the Selection of High School Students for University Colleges Using Machine Learning Algorithms

Identifying the critical elements that impact students' choice of university majors is critical for leading the nation towards future success. This entails developing a cohort of talented science experts who can pioneer innovations in a variety of industries such as technology, industry, and transportation. To uncover these crucial aspects, this study uses artificial neural networks, a sophisticated analytical technique, to anticipate and assess their relative importance. The study used a descriptive analytical technique, administering a detailed questionnaire to students enrolled in Iraqi universities, resulting in a strong sample size of 238 people. The data was then meticulously analysed using artificial neural networks, a process well-known for its ability to find nuanced patterns and correlations within complicated datasets. The study's findings provide fascinating insights into the factors that influence students' university major selections. The quality of interactions with instructors and classmates during high school is among the most important of these characteristics, demonstrating the substantial influence of mentoring and social dynamics on academic trajectory. Following closely after are thoughts about the economic condition of students' parents, demonstrating the ubiquitous effect of socioeconomic issues on educational decision-making. In summary, this study provides useful information that may be used to guide strategic activities targeted at creating a favourable environment for the development of future leaders. By identifying the fundamental determinants underlying students' university major choices, policymakers and stakeholders may develop focused interventions to assure the nation's preparation for tomorrow's knowledge economy's problems and prospects.