Due to numerous recessions the world economy has experienced, the twentieth century is reported as a specific period for the emergence of global unemployment (Benjamin and Kochin 1982). On the other hand, structural unemployment occurs when there is a mismatch between the skills of the unemployed workers and the skills needed for the available jobs.Ĭyclical unemployment is largely affected by the status of the economy, which can significantly diminish job vacancies. When there is not enough demand in the economy to provide jobs for everyone who wants to work (e.g., number of job seekers is larger than the number of openings), this is called cyclical (or Keynesian) unemployment. Various reasons can create this mismatch. Simply defined as “persons above a specified age not being in paid employment or self-employment but currently available for work during the reference period” (OCDE 1982), unemployment often reflects a mismatch between the number of job seekers and available job vacancies. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.Įmployment, or rather the lack of it, that is to say, unemployment, is without doubt one of the most negative economic phenomena due to its potential consequences on the cohesion and stability of societies. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. More and more studies are investigating data mining techniques for the prediction of employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. All these combined efforts certainly can contribute to increasing employability. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers.
Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Knowing their weaknesses and strengths, students might better plan their career. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Student employability is crucial for educational institutions as it is often used as a metric for their success.
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