Publications

A Comparative Study of Regression and Classification Algorithms for Modelling Students' Academic Performance

Authors: Pedro Strecht, Luís Cruz, Carlos Soares, João Mendes-Moreira and Rui Abreu

Published in: Educational Data Mining 2015.

Abstract: Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is tackled as a classification task while the latter as a regression task. Separate models are trained for each course. The experiments were carried out using administrate data from the University of Porto, concerning approximately 700 courses. The algorithms with best results overall in classification were decision trees and SVM while in regression they were SVM, Random Forest, and AdaBoost.R2. However, in the classification setting, the algorithms are finding useful patterns, while, in regression, the models obtained are not able to beat a simple baseline.

@inproceedings{strecht2015comparative, author = {Strecht, Pedro and Cruz, Luis and Soares, Carlos and Mendes-Moreira, Joao and Abreu, Rui}, booktitle = {Educational Data Mining 2015}, title = {A Comparative Study of Regression and Classification Algorithms for Modelling Students' Academic Performance}, year = {2015}}

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