Behavior of employability indicators in university graduates

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
N. Sánchez, Daniel Esteban Casas-Mateus, Luz Deicy Alvarado Nieto
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引用次数: 1

Abstract

Introduction: This article is the result of research entitled the behavior of employability indicators in university graduates, developed at the Universidad Distrital Francisco José de Caldas in 2019. Problem: The Emple-AP project promotes the creation of an observatory for labor insertion and the strengthening of employability in countries of the Pacific Alliance (PA), which particularly benefits Colombia, because one of its objectives with the PA is to overcome the socioeconomic inequality that exists among its inhabitants. Objective: To identify the relationship between employability indicators through classification methods used in Artificial Intelligence. Methodology:The indicators’ behavior description involves data pre-processing, a formal global study in statistics and a specific formal study through comparison of classification methods. Results: Descriptions of these employability indicators show characteristics of the situation in the studied population. Conclusion:Given the analysis of the classification model, it is determined that the diversity and disparity of the dataset makes the RandomTree model the most accurate in this research, finding that the system has characteristic behaviors of an adaptative complex system. Originality:Through this research, employability indicators were analyzed through data mining tools, additionally the analysis presented in this article could be replicated under particular conditions in other countries of the PA. Limitations:The information comes from the Universidad Distrital Francisco José de Caldas graduate’s office. A single source generates a limitation in the data and in the population studied.
大学毕业生就业能力指标的行为
引言:本文是2019年由弗朗西斯科·何塞·德卡尔达斯大学开发的题为“大学毕业生就业能力指标行为”的研究结果。问题:Emple-AP项目促进在太平洋联盟(PA)国家建立劳动力插入观察站和加强就业能力,这对哥伦比亚尤其有利,因为哥伦比亚与PA的目标之一是克服其居民之间存在的社会经济不平等。目的:通过人工智能中的分类方法识别就业能力指标之间的关系。方法:指标行为描述包括数据预处理、统计学的正式全局研究和分类方法比较的具体正式研究。结果:对这些就业能力指标的描述显示了所研究人群的情况特征。结论:通过对分类模型的分析,确定数据集的多样性和差异性使得RandomTree模型在本研究中最准确,发现该系统具有自适应复杂系统的特征行为。独创性:通过这项研究,通过数据挖掘工具分析了就业能力指标,此外,本文提出的分析可以在特定条件下复制到其他国家的PA。限制:这些信息来自弗朗西斯科约瑟·德卡尔达斯大学研究生办公室。单一来源会对数据和所研究的人群产生限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ingenieria Solidaria
Ingenieria Solidaria ENGINEERING, MULTIDISCIPLINARY-
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