Information System for Vocational Guidance, Employment and the Forecasting of Labor Demand: The Case of Kazakhstan

Turganbayev, Yerken; Adilgazinov, Gainelgazy; Barabanova, Yelena; Zhakupov, Aslanbek; Zhukibayeva, Gulden

doi:10.1109/sist50301.2021.9465984

Cited by 2 publications

(1 citation statement)

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“…Online vocational guidance communities help the student validate the results of face-to-face guidance, with the advantage of easy access and the diversity of knowledge received from other people. In [33], a web interface is presented for vocational guidance with different guidance alternatives and age ranges; however, the tool's accuracy in aspects of career classification is not shown.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Model of Classification of Professions in Vocational Guidance Systems

González-Serrano

2022

Rev. Fac. Ing.

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Vocational guidance is part of psychosocial development and is understood as a method that helps to determine the most appropriate profession according to the aptitudes and abilities of the student. The processes of vocational guidance are dynamic and focus on educating and favoring the decision-making process in the professional choice for a learning pathway throughout the student's life, which will benefit society in the long run. Most of the current solutions, both theoretical and applied, from Europe and North America differ when used in the Colombian context, mainly for adults, since the process of classifying professions is not accurate nor precise. In addition, there are various educational projects and evaluation systems in secondary education level institutions. At this level, the students have a changing vocational choice which implies taking into account specific characteristics of the context, also, the student profile vocational guidance determinants. The objective of this article is to describe the adaptive model of occupational classification integrated into the Intelligent Web Platform used in educational institutions in the Department of Cauca. The use of the CRISP-DM methodology allowed finding the Naive Bayes and Deep learning algorithms as those with the best performance in the classification of professions.

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Section: Introductionmentioning

confidence: 99%