Banca de DEFESA: NEIDE MARIA PALHANO DOS SANTOS

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : NEIDE MARIA PALHANO DOS SANTOS
DATE: 05/12/2025
TIME: 09:00
LOCAL: Banca Remota
TITLE:

DROPOUT INDICATORS IN SPECIALIZATION COURSES DISTANCE LEARNING/ONLINE

KEY WORDS:

Distance education. Evasion indicators. Methodological study.

PAGES: 70
BIG AREA: Ciências Humanas
AREA: Educação
SUBÁREA: Planejamento e Avaliação Educacional
SPECIALTY: Avaliação de Sistemas, Instituições, Planos e Programas Educacionais
SUMMARY:

Evasion in Distance Education (DE) courses represents a significant challenge to the quality and sustainability of educational systems, especially in the context of training health professionals within the Brazilian Public Health System (SUS). In light of this, the present methodological study had as its main objective the identification of dropout indicators through the development of a low-resolution prototype monitoring panel for the risk of dropout in online specialization courses offered in the SUS Virtual Learning Environment (AVASUS).The methodological process, based on the framework by Polit and Beck (2019), began with a scoping review for the identification and categorization of evasion factors, followed by the mapping of available data resources in AVASUS and, finally, the development of the prototype based on prioritized User Stories. The most important results revealed that evasion is a multifactorial phenomenon, with 50 indicators identified and distributed into three categories: Institutional Aspects (48%), Personal Aspects (40%), and Social Aspects (12%). The frequency analysis in the reviewed articles showed that personal and institutional aspects are the most addressed, with 100% and 72.72% prevalence, respectively. The discussion on the topic reinforces the interdependence of these factors, indicating that student retention in DE depends both on the quality of academic and pedagogical support (institutional) and on the student's self-management capacity and psychosocial well-being (personal), with the developed prototype being a strategic tool to validate the transformation of interaction data into visual and actionable alerts. In conclusion, the study delivered a conceptual artifact that allows the early identification of students at risk, contributing to the efficient management of courses and the strengthening of professional qualification in the SUS. As a proposal for future studies, the implementation of the solution, the application of Learning Analytics to create a more robust predictive risk model, and the evaluation of the impact of personalized interventions on reducing evasion rates are suggested.

COMMITTEE MEMBERS:
Presidente - 1422699 - HERTZ WILTON DE CASTRO LINS
Interno - 1204045 - JOSE ADAILTON DA SILVA
Externa ao Programa - 2087603 - ROSIRES MAGALI BEZERRA DE BARROS - UFRNExterna à Instituição - TALITA HELENA MONTEIRO DE MOURA