Conscious Self-regulation, Motivational Factors, and Personality Traits as Predictors of Students' Academic...

The theoretical basis of this study was the resource approach (Morosanova 2014, 2017), in which the conscious self-regulation of learning activity is understood as a meta-resource for students, allowing them to consciously and independently set learning goals and manage their achievement. This approach made it possible to create models of direct and mediate contributions of self-regulation and school engagement not only to academic performance, but also to other motivational and personal competencies.

Our study aimed to investigate the impact of conscious self-regulation, school engagement, motivation, and personality on academic achievement, while taking into account the effects of mediation.

A quantitative research design was applied, using data collected from more than 1524 students from the 5^th to 11^th grades in Russian schools and applying Structural Equation Modelling (SEM).

The results allowed us to construct a statistical model of predictors of students' academic achievement. The model was verified on the total sample, as well as samples differing in gender and age. The results show that conscious self-regulation is central to non-cognitive predictors of academic achievement. For the first time, a study has revealed and described the reciprocal relationship between self-regulation, academic motivation, school engagement, and academic performance. The resulting model demonstrates that behavioral and cognitive engagement make a significant contribution to academic performance, while emotional and social engagement do not find significant links with it, although they determine other areas of school life.

Our paper investigates the nature and strength of the effects of major non-cognitive predictors of academic achievement. The study results substantiated the resource role of conscious self-regulation not only for students' academic performance, but also for their academic motivation, school engagement, and attitude toward learning. The predictor model of academic achievement we developed will provide a foundation for combining existing heterogeneous concepts into a single integrated model and clarify the contradictions between them.