The Concept of a Dynamic Model of Competencies for the Labor Market Analysis

Ibadov, Ilkin; Aksenov, Alexander; Iumanova, Irina; Sozykin, Andrey

doi:10.1109/usbereit48449.2020.9117691

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The ESCO taxonomy is underpinned by a continuous improvement process which benefits from feedback captured from all the European Union member states but updates are only published every few years Career and training decisions (European Commission, 2017). The ESCO team are beginning to use NLP techniques to update the occupations in the list and a similar process could be used to identify emerging skills (Ibadov et al, 2020) and suggest the placement of these skills in the taxonomy. Combining the insights generated from the machine learning with a crowdsourcing process (providing interim visibility to the suggested changes and asking taxonomy users to provide feedback on these suggestions) could also help to ensure that the taxonomy is kept up to date.…”

Section: Directions For Further Researchmentioning

confidence: 99%

Illustrating the application of a skills taxonomy, machine learning and online data to inform career and training decisions

Mason

Chen

Evans

et al. 2023

IJILT

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PurposeThis paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.Design/methodology/approachUsing the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.FindingsThis study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.Originality/valueThis study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

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Section: Directions For Further Researchmentioning

confidence: 99%