PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Comparing classication methods for predicting distance students' performance
Diego García-Saiz and Marta Zorrilla
In: 2nd Workshop on Applications Pattern Analysis, 19-21 Oct 2011, Castro Urdiales, Spain.


Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, dierent machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them dierent from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the dierent classication techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9095
Deposited By:Diego García-Saiz
Deposited On:21 February 2012