Illustrating the Application of a Skills Taxonomy, Machine Learning and Online Data to Inform Career and Training Decisions

Autor/inn/en	Mason, Claire M.; Chen, Haohui; Evans, David; Walker, Gavin
Titel	Illustrating the Application of a Skills Taxonomy, Machine Learning and Online Data to Inform Career and Training Decisions
Quelle	In: International Journal of Information and Learning Technology, 40 (2023) 4, S.353-371 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mason, Claire M.) ORCID (Chen, Haohui) ORCID (Evans, David) ORCID (Walker, Gavin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2056-4880
DOI	10.1108/IJILT-05-2022-0106
Schlagwörter	Taxonomy; Artificial Intelligence; Data Collection; Data Analysis; Job Skills; Careers; Decision Making; Career Choice; Data Use; Skill Development; Career Counseling; Labor Needs; Trend Analysis; Dislocated Workers; Job Applicants; Job Search Methods; Resumes (Personal); Relevance (Education); Foreign Countries; Courses; Language Processing; Career Change; Information Technology; Australia + Suchen Sie Ihr Suchwort? Taxonomie; Künstliche Intelligenz; Data capture; Datensammlung; Auswertung; Produktive Fertigkeit; Career; Karriere; Decision-making; Entscheidungsfindung; Kompetenzentwicklung; Qualifikationsentwicklung; Labour needs; Arbeitskräftebedarf; Trendanalyse; Arbeitsloser; Bewerber; Arbeitsplatzsuchtheorie; Lebenslauf; Relevance; Relevanz; Ausland; Kursangebot; Sprachverarbeitung; Career changes; Berufswechsel; Informationstechnologie; Australien
Abstract	Purpose: This 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/approach: Using 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. Findings: This 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/value: This 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01