Open AccessTransversal Diagnosis of the Panorama of Professional Education: A Case Study of the City of Itaporã – Mato Grosso do Sul
Author(s) -
Nelma Lina Almeida Castro,
Sandra Martins de Brito Carvalho,
Cristina Paula Pereira,
Maria Neide De Almeida,
Patricia Coelho De Almeida,
Leandro Rachel Arguello,
Antonio Carlos Dorsa,
Patricia Kubalaki Onaka,
Luis Henrique Almeida Castro
Publication year - 2021
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol9.iss3.2977
Subject(s) - panorama , chart , profiling (computer programming) , order (exchange) , identification (biology) , sociology , medical education , psychology , public relations , political science , business , medicine , computer science , statistics , mathematics , finance , computer vision , operating system , botany , biology
This case study aims to identify the demand for professional education courses in the city of Itaporã, in the state of Mato Grosso do Sul, in the year 2020, offering a cross-cutting diagnosis of this educational panorama; for this purpose, the technique of guided interview in loco was implemented with the action coordinating agents designated by the municipal administration. All those involved were aware of their free and voluntary collaboration. During the entire collection phase, the information was recorded through a free association chart which, later revised, gave basis to the identification that in the disclosure phase there is no time to contact the possible students taking into account that, besides the districts, the municipality aggregates a vast rural region. In addition, the fact that the course is offered only at night, ends up discouraging the participation of some potential students who, despite showing interest, need to pay attention to their labor routines. It is concluded that a greater concern is needed to create a database of course graduates in order to establish a periodic sense capable of identifying professional life after the course, in addition to profiling the demand for skilled labor in order to detect which training is important at the moment.