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

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Information and Learning Technology Pub Date : 2023-06-15 DOI:10.1108/ijilt-05-2022-0106
C. Mason, Haohui Chen, David Evans, Gavin Walker
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Abstract

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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说明技能分类法、机器学习和在线数据的应用,为职业和培训决策提供信息
本文旨在展示技能分类法如何与机器学习结合使用,以整合各种在线数据集并揭示技能差距。本研究的目的是展示如何利用综合数据集揭示的技能差距来实现更好的劳动力市场调整,保持教育产品的最新状态,并帮助毕业生传达其资格证书的价值。本研究使用ESCO分类和自然语言处理,从三种类型的在线数据(招聘广告、课程描述和简历)中获取技能数据,使我们能够比较三种不同职业的技能需求和技能供应。本研究说明了整合数据的三种实际应用,展示了如何使用这些数据来帮助被技术颠覆的工人确定替代的职业道路,帮助教育工作者确定课程设置中的差距,并支持学生向雇主传达其培训的价值。原创性/价值本研究基于机器学习的现有应用(从单个数据集检测技能),通过使用技能分类来整合三个数据集。这项研究展示了如何将这些互补的大数据集整合起来,以支持教育者、雇主和求职者的需求和产品之间更大的一致性。
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来源期刊
International Journal of Information and Learning Technology
International Journal of Information and Learning Technology COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.10
自引率
3.30%
发文量
33
期刊介绍: International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.
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