A Case Study of Applying the Classification Task for Students' Performance Prediction

被引：3

作者：

Guerra, M. S. ^{[1
]}

Asseiss Neto, H. ^{[1
]}

Oliveira, S. A. ^{[2
]}

机构：

[1] Inst Fed Mato Grosso Do Sul, Tres Lagoas, MS, Brazil

[2] Univ Estadual Paulista, Ilha Solteira, SP, Brazil

来源：

IEEE LATIN AMERICA TRANSACTIONS | 2018年 / 16卷 / 01期

关键词：

data mining; classification task; academic database;

D O I：

10.1109/TLA.2018.8291470

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：172 / 177

页数：6

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