Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement

Abstract

To improve mathematics education and achievement, research needs to identify factors that support and motivate students to learn and achieve in math. The purpose of this study was to test, using structural equations, a model with a sample of 1412 high-school students where autonomy would predict autonomous motivation, which in turn, has a positive effect on effort regulation and deep-processing, and both variables would predict math achievement. Results confirmed all hypothesized paths, except deep-processing unexpectedly did not predict math achievement. Findings suggest that when students feel that their schoolwork is purposeful and interesting, and that the classroom environment and teachers are responsive and supportive, they will be autonomously motivated to engage in self-regulated learning. Autonomous motivation propels students to engage in deep-processing of information and to persist and exert effort in their studies even when the school subject or studying becomes boring or taxing. Self-regulation of effort ultimately results in enhanced mathematics achievement.