A Matrix Taxonomy of Knowledge, Skills, and Abilities (KSA) Shaping 2030 Labor Market

This paper proposes a dynamic Knowledge, Skills, and Abilities (KSA) matrix-based taxonomy for the Industry 4.0 workforce. The study methodology consisted firstly of identifying the KSAs through a literature review and secondly of a KSA relevance analysis using information from World Economic Forum (WEF) global reports and the Organization for Economic Cooperation and Development (OECD). Finally, we identified the correlation coefficients of the KSA matrix elements concerning the data on jobs and occupations using information from the European Skills, Competencies, and Occupations (ESCO), Occupational Information Network (O*NET), and the strategic intelligence platform of the World Economic Forum. One of the goals was to make the taxonomy compatible with existing and future machine learning methods (i.e., AI-ready) that will enable efficient and effective use of AI in mining and explaining existing and potentially proposing novel trends and strategies. Preliminary results show that the KSA Industry 4.0 Taxonomy can serve as an international reference guide for designing 2030 educational approaches to active and experiential learning in Higher Education Institutions.

I. Introduction

The high demand for workers and recent gra duates trained in the so-called Future Skills is partly due to the pressure to meet, by 2030, the labor market requirements related to Industry 4.0 Knowledge, Skills and Abilities (KSA) taxonomy [1]. The combination of two phenomena, the high demand for skilled workers and the skills gap, requires a disruptive comprehensive solution: not only must the teaching-learning experience of young people in their Higher Education years be made more flexible, but also a culture of continuing education in recently graduated professionals must be implemented [2], [3].