The trend of big data in workforce frameworks and occupational standards towards an educational intelligent economy

Abstract

The complexity of creating and updating workforce frameworks and standards is limiting the manoeuvrability of TVET educational outcomes as the world of work is constantly changing, putting stakeholders at risk of poor visibility. A literature review based on 39 documents from various types of documents including articles, conference papers, book chapters, short surveys, conference reviews and reviews within the period of 2011 to 2020 found that existing occupational taxonomies are not granular enough to support occupational classification in big data applications. On the other hand, workforce frameworks and standards although sufficient in providing detail occupational taxonomies, they are complex to develop and update thus making them inflexible to changing workforce requirements. Trend in the literature also suggests that organizations that employ occupational classification in big data applications among developed countries are in a transition from evidence-based decision making which is based on limited sampling; to a data-driven decision making which is based on the total population. This trend establishes a notion on the emergence of a data driven organization. Given evidence that support the need to improve in the flexibility of workforce frameworks and standards, and the emerging prevalence of a data driven organizations, the idea of improving workforce frameworks and occupational standards through educational intelligence is proposed to improve the manoeuvrability of TVET educational outcomes.