2. Data mining applied to school dropout case: bachelor of computing at universidad del zulia-npf

Abstract

The aim of this study was to obtain patterns on students who could not finish their university studies, in the bachelor of computing at LUZ-NP; by applying data mining. A field-descriptive research was conducted based on Crisp-DM computing methodology with Weka support. Sample data was collected from students between first and third term, teachers from first and fifth term during the period I-2012; as well as the reports from control study’s office between 2018 and 2011. A computing model was built to predict student’s dropout. C4.5 decision and k neighbors’ technique were applied. Results demonstrated not too much previous knowledge in the logic and mathematical areas, reduced economical resources to buy computing equipments, lack of concentration on studies and a few hours dedicated to study.