Detection of Candidate Skills from Job Offers and Comparison with ESCO Database

Detection of Candidate Skills from Job Offers and Comparison with ESCO Database

연구 분야: Software Development

학회: International Conference on Computational Collective Intelligence

This paper explores the critical nexus between employer-demanded skills and academic learning outcomes, aiming to bridge the gap between the evolving demands of the job market and educational curricula. Employing a comprehensive research methodology, the study identifies and compares skills emphasized in job offers with learning outcomes cataloged in the ESCO database. The linguistic approach, utilizing morphological, syntactic, and semantic analyses, unveils insights into employer requirements in the Polish job market. The research introduces innovation by employing the Unitex linguistic analyzer, enriched with dictionaries and automated local grammars. The groundbreaking aspect lies in the automation of linguistic resource generation, addressing the time-intensive manual creation of transducers. The paper underscores the importance of efficiency and relevance in adapting educational programs to meet contemporary workforce expectations. Future implications are discussed, emphasizing continuous refinement of methodologies, integration of emerging technologies, and anticipation of future workforce trends. The study provides a foundation for ongoing dialogue between academia and industry, offering practical insights for adapting curricula to dynamic job market needs. The research not only realizes its initial objectives but also serves as a catalyst for future endeavors, ensuring educational systems remain agile and aligned with the evolving dynamics of the professional landscape.