Predicting primary students' mathematical competence: The role of cognitive, attitudinal, and methodological factors...

International assessments indicate a decline in mathematical performance in Spain, underscoring the need to research factors related to math achievement and to implement innovative teaching methods in classrooms that foster reasoning and a positive student attitude. This research aims to study the relationship between cognitive, attitudinal, and methodological factors and general mathematical performance, as well as analyze the explanatory weight of these factors on the variance in general mathematical competence. The study included 237 5th and 6th-grade primary education students, divided into groups based on their classroom teaching methodology: Algorithm Based on Numbers method (ABN) (102 students) and non-ABN (135 students), with confirmed socioeconomic equivalence between groups. Results have revealed significant correlations between mathematical competence and the studied factors. The general regression model explained around 49% of the variance, highlighting negative predisposition towards mathematics as the strongest predictor. Predictive models differed between groups: the ABN students showed a more explanatory model (59% of the variance), emphasizing the influence of processing speed and fluid intelligence. The non-ABN group, however, revealed working memory as the cognitive factor with the greatest weight. Ultimately, this research suggests that the ABN methodology could foster more efficient learning strategies less dependent on working memory, while also highlighting the crucial influence of predisposition on mathematical performance.