5th OJA Forum: How can online job advertisements help to predict the demand for skills? – Part 2

In this blog series, international experts present their work on predicting the demand for skills using online job advertisements (OJAs). They explain whether and to what extent skills can currently be predicted by using OJAs and address opportunities and challenges. One of these experts is Kasper Kok. As part of Textkernel’s R&D team, he is working on innovations in extracting information and in processing natural language for labour market analytics.

Can skill demand be predicted? The potential and limits of different approaches

At the 5th OJA Forum of the Bertelsmann Stiftung and the Federal Institute for Vocational Education and Training, Kasper Kok talked about the potential and limits of different approaches to predict the demand for skills. Predicting this demand is on the wish list of many organizations. But is it even possible, and what kind of data would be needed? In the video, Kasper Kok presents different approaches based on a vast, historical dataset of skills mentioned in online job ads. Exploration of this data suggests that it is possible, at least to some extent, to make predictions by extrapolating from historical trends. However, it should be acknowledged that the demand for skills is in many ways symptomatic of factors such as economic, demographic, and technological developments. Effective skill prediction models should therefore consider external forecasts of such factors and the interplay between them.

 

Weitere Beiträge zum „5th OJA Forum“:

Johannes Müller – How can online job advertisements help to predict the demand for skills?

Kasper Kok – How can online job advertisements help to predict the demand for skills?

Fabian Stephany – How can online job advertisements help to predict the demand for skills?

Wyatt Clarke – How can online job advertisements help to predict the demand for skills?

Elisa Margareth Sibarani – How can online job advertisements help to predict the demand for skills?



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