Author(s):
- Yeremenko Ivan Maksymovych, ORCID: https://orcid.org/0009-0009-4375-6821
- Oksanych Iryna Hryhorivna, ORCID: http://orcid.org/0000-0002-4570-711X
DOI: https://doi.org/10.32782/2307-9770.2024.12.03.05
Paper Language: UKR
Abstract
The aim of this research is to develop a method for assessing the professional suitability of schoolchildren using machine learning algorithms. The study is aimed at improving the process of career guidance, increasing the accuracy of assessing students' professional aptitudes and forming personalized recommendations for choosing a future profession. The study uses an integrated approach that combines mathematical models, psychodiagnostic indicators, and modern information technologies. The main tool is artificial neural networks that analyze large amounts of data, identify hidden dependencies and make predictions about students' professional suitability. The developed method allows us to accurately assess students' compliance with professional requirements and predict their career prospects. The use of a multilayer perceptron provides flexibility and accuracy in decision-making, which allows us to adapt recommendations to the individual characteristics of students. The use of modern machine learning algorithms significantly increases the efficiency of the vocational guidance process and minimizes the subjective factor in decision-making. The scientific novelty of the study lies in the use of artificial intelligence algorithms to automate the process of assessing the professional suitability of schoolchildren. The proposed method differs from traditional approaches by integrating personalized recommendations based on the analysis of multifactorial data. For the first time, a comprehensive combination of psychodiagnostic techniques and machine learning algorithms has been implemented, which allows not only to predict professional aptitudes but also to formulate recommendations for the development of the necessary skills. The developed method can be implemented in the education system to improve the effectiveness of career guidance in schools. It allows teachers, school psychologists and counsellors to quickly and objectively assess students' professional aptitude and provide them with individual recommendations on choosing a career path. The proposed approach can also be used in employment centers, educational platforms and online learning systems to select personalized learning paths.
Keywords
career guidance, machine learning, neural networks, data analysis, personalized recommendations, psychological diagnostics, educational technologies, career forecasting, automated systems
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