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

MUSSO, M. F.; KYNDT, E. ; CASCALLAR, E. C.; DOCHY, F.

Education Research International

Hindawi Publishing Corporation

Lugar: New York; Año: 2012 vol. 12 p. 1 - 13

2090-4002

There is substantial research which has investigated the influences of working memory, attention, motivation, and learning strategies, on mathematical performance and self-regulation in general. However, these studies have only looked at the separate effects of these components. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them can contribute to the prediction of mathematical performance. With the emergence of new methodologies and technologies, including the use of modelling with predictive systems, it is now possible to study these effects and evaluate their impact with approaches which use a wide range of data, or student productions, to estimate student performance without the need of traditional testing (Boekaerts & Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables (gender, highest level of education of mother and father, occupation of parents, and secondary school from which the student graduated), associated with different levels of mathematical performance using Artificial Neural Networks (ANN). A total sample of approximately 800 entering university students of both genders, ages ranging between 18 and 25 was used. Three neural network models were developed to identify the lowest 30%, highest 30%, and middle 30% group of students, respectively, in terms of their estimated future performance in a mathematics test. Two of the models (identifying the top 30% and the low 30% groups) were able to reach 100% correct identification of all students in each of the two groups, using the corresponding ANN. The third model (middle 30% group) was able to reach 70% correct identification. These ANN models showed interesting differences in the pattern of relative predictive weight importance amongst those variables with the highest participation for the predictive model. For Low performers, basic cognitive variables were most important, while self-regulation and background variables were good predictors for High performers. Their impact on educational quality and improvement, as well as accountability is highlighted.