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Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

Author

Listed:
  • Albert Weichselbraun

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
    webLyzard technology, Liechtensteinstrasse 41/26, 1090 Vienna, Austria)

  • Norman Süsstrunk

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • Roger Waldvogel

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • André Glatzl

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • Adrian M. P. Braşoveanu

    (Research Center of New Media Technology, Modul University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria
    Modul Technology, Am Kahlenberg 1, 1190 Vienna, Austria)

  • Arno Scharl

    (webLyzard technology, Liechtensteinstrasse 41/26, 1090 Vienna, Austria
    Research Center of New Media Technology, Modul University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria)

Abstract

Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements.

Suggested Citation

  • Albert Weichselbraun & Norman Süsstrunk & Roger Waldvogel & André Glatzl & Adrian M. P. Braşoveanu & Arno Scharl, 2024. "Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills," Future Internet, MDPI, vol. 16(5), pages 1-19, April.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:144-:d:1380839
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    References listed on IDEAS

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