It is autonomous, not controlled motivation that counts: Linear and curvilinear relations of autonomous and controlled motivation to school grades. (June 2021)

• Record Type: Journal Article
• Authors: Mouratidis, Athanasios; Michou, Aikaterini; Sayil, Melike; Altan, Servet
• Is Part Of: Learning and instruction. Volume 73(2021)
• Publication Date: 2021-06
• Subjects: Self-determination theory -- Academic achievement -- Adolescents -- Polynomial regression -- Response surface analysis; Learning -- Periodicals; Teaching -- Periodicals; Apprentissage -- Périodiques; Enseignement -- Périodiques; Learning; Teaching; Periodicals; Electronic journals; 370.1
• DOI: 10.1016/j.learninstruc.2020.101433
• Languages: English
• ISSNs: 0959-4752

Abstract

Abstract: Can controlled motivation contribute to desired educational outcomes such as academic achievement over and above autonomous motivation? No, According to Self-Determination Theory. Yet, some recent findings have shown that controlled motivation may not fully undermine motivated behavior when autonomous motivation remains high. In this study, we tested this possibility through two different samples of more than 3000 Turkish adolescent students. Through polynomial regression and response surface analyses we found only slim evidence that high controlled motivation can predict higher grades. Instead, a consistent finding that emerged was that higher grades were expected when high levels of autonomous motivation coincided with low levels of controlled motivation rather than high levels of controlled motivation. These findings highlight the usefulness of polynomial regressions and response surface analyses to examine pertinent questions which challenge the view that controlled motivation may not be as much detrimental as self-determination theory claims to be. Highlights: Autonomous motivation predicted higher grades. When controlled motivation was high, autonomous one predicted no higher grades. Higher autonomous and lower controlled motivation predicted even higher grades. Polynomial regressions and surface analyses can address advanced research questions.