Predicting Long-Term Growth in Students' Mathematics Achievement: The Unique Contributions of Motivation and Cognitive Strategies

Authors

Errata

This article is corrected by:

  1. Errata: Corrigendum: Correction to Supporting Information Volume 87, Issue 5, 1646, Article first published online: 4 July 2016

  • This study was supported by an Alexander von Humboldt Foundation fellowship (to K. Murayama) and four grants from the German Research Foundation (DFG; to R. Pekrun, Project for the Analysis of Learning and Achievement in Mathematics, PALMA; PE 320/11-1, PE 320/11-2, PE 320/11-3, PE 320/11-4).

  • [Correction made after online publication on December 20, 2012: In the supporting information of this article, the authors described the proportion of participants who completed the assessments. These numbers were miscalculated and are now correct. The revised data does not influence the reported results.]

Abstract

This research examined how motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (deep and surface strategies), and intelligence jointly predict long-term growth in students' mathematics achievement over 5 years. Using longitudinal data from six annual waves (Grades 5 through 10; Mage = 11.7 years at baseline; N = 3,530), latent growth curve modeling was employed to analyze growth in achievement. Results showed that the initial level of achievement was strongly related to intelligence, with motivation and cognitive strategies explaining additional variance. In contrast, intelligence had no relation with the growth of achievement over years, whereas motivation and learning strategies were predictors of growth. These findings highlight the importance of motivation and learning strategies in facilitating adolescents' development of mathematical competencies.

Ancillary