Table of Contents Author Guidelines Submit a Manuscript
Education Research International
Volume 2012 (2012), Article ID 250719, 13 pages
Research Article

Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors

1Centre for Research on Teaching and Training, Katholieke Universiteit Leuven, Belgium
2Universidad Argentina de la Empresa, Buenos Aires, Argentina
3Assessment Group International, Washington, DC, USA
4Assessment Group International, Brussels, Belgium

Received 7 May 2012; Accepted 26 September 2012

Academic Editor: Monique Boekaerts

Copyright © 2012 Mariel Musso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing (Boekaerts and Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted.