A Case Study of Applying the Classification Task for Students' Performance Prediction
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Data
2018-01-01
Autores
Guerra, M. S.
Asseiss Neto, H.
Oliveira, S. A. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee-inst Electrical Electronics Engineers Inc
Resumo
This paper presents a study involving the application of data mining techniques for extracting knowledge from the academic database of the Federal Institute of Mato Grosso do Sul (IFMS). The main goal is the prediction of students' performance on specific classes of the Internet Systems course. Extra students' information such as age and gender are also considered. Knowledge Discovery in Databases (KDD) is described and its steps are applied in this study. The classification task is used to generate decision trees that are tested on different datasets. The results show a success rate of 75.8% on the classification of new and unknown students based on the decision trees models generated.
Descrição
Palavras-chave
data mining, classification task, academic database
Como citar
Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 1, p. 172-177, 2018.