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

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Pedro Strecht (Author) FEUP; Luís Cruz (Author) FEUP; Carlos Soares (Author) FEUP; João Mendes-Moreira (Author) FEUP; Rui Abreu (Author) FEUP

Conference proceedings International

Title: Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015

Pages: 392-395

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00K-NJ2

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific