Strategic factors for systematic monitoring of evasion in undergraduate courses

Authors

  • Ivan Londero Hoffmann Universidade Federal de Santa Maria -UFSM
  • Raul Ceretta Nunes Universidade Federal de Santa Maria - UFSM
  • Felipe Martins Muller Universidade Federal de Santa Maria - UFSM
  • Debora de La Vega Hoffmann Universidade Federal de Santa Maria - UFSM

DOI:

https://doi.org/10.5007/1983-4535.2017v10n4p157

Abstract

The dropout rates in higher education is a complex phenomenon and represents a concern in management of higher education institutions. In Brazil, there is no official guidance for systematizing the collection and calculation of evasion. This paper presents a methodology that allows the identification of strategic factors that are capable of supporting a systematic monitoring of dropout rates. The work was divided into two stages. In the first stage was carried out a systematization of data from the case study's environment, for the analysis of dropout rates, using as a data source the Census.The result was that from the Census data model, the indicators can be extracted systematically involving all courses and teaching centers of the higher education institution. In the second stage, the tacit knowledge of the institution's experts that developed by teachers who passed the courses coordination function was sought. This step used Delphi method of scientific research, to define consensually, which factors are more important in causes of evasion. As a result, it was prepared a panel on which the main causes of dropout rates. From this panel, it is possible to elaborate strategies and actions that allow to minimize these factors and their influence in the rates of evasion of the institution.

Author Biographies

Ivan Londero Hoffmann, Universidade Federal de Santa Maria -UFSM

Mestre em Engenharia de Produção, Técnico em Tecnologia da Informação.

Raul Ceretta Nunes, Universidade Federal de Santa Maria - UFSM

Dr. em Ciência da Computação, Professor do Departamento de Computação Aplicada.

Felipe Martins Muller, Universidade Federal de Santa Maria - UFSM

Dr. em Engenharia Elétrica, Professor do Departamento de Computação Aplicada.

Debora de La Vega Hoffmann, Universidade Federal de Santa Maria - UFSM

Mestranda em Engenharia de Produção, Aluna.

Published

2017-12-20