Prediction of course completion by students of a university in Brazil

• Autor(a) principal: Bolsoni-Silva,Alessandra Turini
• Data de Publicação: 2018
• Outros Autores: Barbosa,Rommel Melgaço, Brandão,Alessandra Salina, Loureiro,Sonia Regina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Psico-USF (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-82712018000300425
• topic: Prediction; support vector machine; classification; social skills; mental health

Abstract The conclusion of the undergraduate course by university students in the time predicted by the curriculum is desirable for young people and for society. The aim was to verify the reliability, sensitivity and specificity of a broad set of predictors for academic performance of university students, who completed the undergraduate course within the time predicted by the curricula, through data mining methodology, provided by the Support Vector Machines algorithm. A simple approach is proposed for the prediction of course completion by students in a university in Brazil. The dataset has 170 students who finished the course and 117 who did not finish. With the proposed methodology, it was possible to predict the course completion by students with an accuracy of 79.5% when using the 19 original variables. An accuracy of 75% was found using only 05 variables: Course, year of the course, gender, initial and final academic performance.