Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.caeai.2021.100043
Title: On the radar: Predicting near-future surges in skills’ hiring demand to provide early warning to educators
Authors: Yazdanian, R
Lee Davis, R
Guo, X
Lim, F
Dillenbourg, P
Kan, MY 
Issue Date: 1-Jan-2022
Publisher: Elsevier BV
Citation: Yazdanian, R, Lee Davis, R, Guo, X, Lim, F, Dillenbourg, P, Kan, MY (2022-01-01). On the radar: Predicting near-future surges in skills’ hiring demand to provide early warning to educators. Computers and Education: Artificial Intelligence 3 : 100043-100043. ScholarBank@NUS Repository. https://doi.org/10.1016/j.caeai.2021.100043
Abstract: The AI-driven Fourth Industrial Revolution and the COVID-19 pandemic have one important thing in common: they both have caused significant and rapid changes to the skill set landscape of various industries. These disruptive forces mean that the early identification of the newly rising skills in a labour market — which we call its “emerging skills” — is crucial to its workforce. It is also crucial to the educators who, in order to provide lifelong training to the workforce, need to quickly adapt their curricula to the new skills. We propose a classification methodology that uses the past job ad trends of skills to predict the emerging skills of a future period, defined as the skills that have experienced a surge in hiring demand in said period. This general definition allows for freedom in specifying the criteria for a skill being emerging (through thresholds on hiring demand and its growth), which could be important to educators. Applying our methodology to the Information and Communication Technologies (ICT) labour market in Singapore, we show that we are able to predict future emerging skills with good precision and recall and beat two baseline classifiers for multiple threshold sets. Our methodology also allows us to see where job ads fail to provide sufficient predictive signals, pointing to auxiliary data sources (such as Stack Overflow for ICT) and skill ontologies as potential remedies. The success of our method shows how AI can be used to empower learners and educators in the ICT domain (and potentially other domains) with useful and well-curated insights at a moment's notice, thus helping speed up the process of curricular change.
Source Title: Computers and Education: Artificial Intelligence
URI: https://scholarbank.nus.edu.sg/handle/10635/229619
ISSN: 2666920X
DOI: 10.1016/j.caeai.2021.100043
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