Career Interest Assessment: College Students Career Planning Based On Machine Leaning

Abstract

College students' career planning based on career interest assessment and machine learning integrates advanced technological tools with traditional career counseling methods to provide personalized and data-driven guidance. By utilizing career interest assessment tools, students can explore their strengths, preferences, and aspirations across various career domains. Machine learning algorithms analyze this data to generate tailored recommendations, matching students with career paths that align with their interests and aptitudes. This paper presents a novel approach to career assessment and planning by leveraging the Sugeno Fuzzy Optimized Weighted Machine Learning (SFOwML) framework. The proposed framework integrates fuzzy logic principles with machine learning techniques to provide personalized and accurate career recommendations tailored to individuals' skills, preferences, and values. Through the incorporation of optimization techniques, the framework refines the career assessment model, enhancing its accuracy in predicting suitable career paths for individuals. The practical application of the SFOwML framework offers a valuable tool for career counselors, educators, and individuals seeking guidance in their career choices. The framework incorporates numerical values to quantify individuals' skills, preferences, and values on a scale from 0 to 100, allowing for a more precise evaluation of their career profiles. Through the integration of fuzzy logic principles with machine learning techniques, personalized career recommendations are generated based on these numerical assessments.