A Comparative Study of Ensemble Methods for Students' Performance Modeling

performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate program. To this end, five ensemble techniques based on four representative learning algorithms, namely Adaboost, Bagging, Random Forest and Rotation Forest have been used to construct and combine different number of ensembles. These four algorithms have been compared for the same number (ten) of base classifiers and the Rotation Forest is found to be the best ensemble classifiers for predicting the student performance at the initial stages of the degree program.

[1]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[2]  Pedro M. Domingos.  Using Partitioning to Speed Up Specific-to-General Rule Induction , 1996 .

[3]  S. Anupama Kumar.  EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT'S ACADEMIC PERFORMANCE , 2011 .

[4]  Sotiris B. Kotsiantis,et al.  A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education , 2010, Knowl. Based Syst..

[5]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Qasem A. Al-Radaideh,et al.  Mining Student Data Using Decision Trees , 2006 .

[9]  Zachary A. Pardos,et al.  Ensembling predictions of student knowledge within intelligent tutoring systems , 2011, UMAP'11.

[10]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[11]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[12]  Hanan Ayad,et al.  Improving student success using predictive models and data visualisations , 2012 .

[13]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[14]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[15]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[16]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[17]  Mykola Pechenizkiy,et al.  Predicting Students Drop Out: A Case Study , 2009, EDM.

[18]  Hannu Toivonen,et al.  Predicting and preventing student failure - using the k-nearest neighbour method to predict student performance in an online course environment , 2010, Int. J. Learn. Technol..

[19]  Hany M. Harb,et al.  Adaboost ensemble with simple genetic Algorithm for student prediction model , 2013 .

[20]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[21]  Dimitrios Kalles,et al.  ANALYZING STUDENT PERFORMANCE IN DISTANCE LEARNING WITH GENETIC ALGORITHMS AND DECISION TREES , 2006, Appl. Artif. Intell..

[22]  Wilhelmiina Hämäläinen,et al.  Comparison of Machine Learning Methods for Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.

[23]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.