Student Dropout Predictive Model Using Data Mining Techniques

Data mining allows discover hidden information in large amounts of data, which is very difficult to visualize with traditional process. This topic of computer science permits manipulation and classification of huge amounts of data. C4.5 and ID3 decision tree, for example, have been proven to be efficient for specific prediction cases. This article shows the construction of a predictive model of student dropout, characterizing students at the University Simón Bolívar in order to predict the probability that a student drop out his/her an academic program, by means of two data mining techniques and comparison of results. To create the model was used WEKA that allows multiple and efficient tools for data processing.

[1]  Sadie E. Roth.  A Model to Predict Ohio University Student Attrition from Admissions and Involvement Data , 2008 .

[2]  R Alkhasawneh,et al.  Modeling student retention in science and engineering disciplines using neural networks , 2011, 2011 IEEE Global Engineering Education Conference (EDUCON).

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[4]  Deanna Kelley-Winstead,et al.  New Directions in Education Research: Using Data Mining Techniques to Explore Predictors of Grade Retention , 2010 .

[5]  Martha Patricia,et al.  Modelos predictivos y explicativos del rendimiento académico universitario: caso de una institución privada en México , 2012 .

[6]  Saurabh Pal,et al.  Data Mining: A prediction for performance improvement using classification , 2012, ArXiv.

[7]  Z. Kovacic,et al.  Predicting student success by mining enrolment data. , 2012 .

[8]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[9]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[10]  Nguyen Thai Nghe,et al.  A comparative analysis of techniques for predicting academic performance , 2007, 2007 37th Annual Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports.

[11]  Michell Angelo Ferruccio,et al.  Minería de Datos , 2004 .

[12]  Ricardo Timarán Pereira.  Una Lectura sobre deserción universitaria en estudiantes de pregrado desde la perspectiva de la minería de datos , 2010 .

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[14]  Surjeet Kumar Yadav,et al.  Mining Education Data to Predict Student's Retention: A comparative Study , 2012, ArXiv.