GPS4Success: Early Identification of Students at Risk of Success/Failure in the 1st Cycle of Basic Education

Project acronym: 
Project start date: 
Sunday, April 1, 2018
Project end date: 
Sunday, April 1, 2018
Main researcher: 
External researchers: 
Jorge Morais, INESC-TEC and Universidade Aberta
Research team: 
Proponent institution: 
CIPES - Centre for Research in Higher Education Policies
Participating institutions: 
Nova School of Business and Economics, Universidade Nova de Lisboa CONFAP – Confederação Nacional das Associações de Pais ANDAEP - Associação Nacional de Diretores de Agrupamentos e Escolas Públicas ANP - Associação Nacional de Professores Câmara Municipal de Cascais
Project description: 
At present, educational institutions are challenged to define strategic plans aimed at promoting success and preventing failure. These intervention plans entail relevant evidence to support decision making. Though, early identification of students continues almost exclusively through the feedback given by teachers and therefore this intervention often comes at a late stage in the learning process. The main aim of this research study is to contribute to the knowledge of success and failure issue in the 1st cycle of elementary education, adopting a longitudinal perspective. It is important to know who the students are and how their performances are influenced by their personal and family features and the way they interact with the school context. In this project we try not only to identify the factors that best explain the school failure, but also propose an approach based on Data Mining techniques, in order to facilitate the recognition of these students at risk. The result is to clearly identify predictive profiles of success and failure, validated not only empirically, but also conceptually and to create an efficient decision support system that allows school managers to identify risks and opportunities to intervene intentionally and early in the promotion of success and prevention of school failure.
Project keywords: 
Sucess, Faillure, Dropout, Key indicators, Data Minning
Financing amount: 
Funding entity: 

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