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

Abstract

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.