Goals, Efficacy and Metacognitive Self-Regulation A Path Analysis

The purpose of this study was to illustrate the relationship between self-efficacy, task value, goal orientations, metacognitive self-regulation, self-regulation and learning strategies and to investigate the unique contribution of each on the variability in students’ total scores of 12 exams. Our study revealed that students’ self-efficacy, task-value, self-regulation, and elaboration are significantly positively correlated with total scores. Path analysis demonstrated that self-efficacy was the strongest predictor of total score and positively predicted mastery goals, but negatively predicted avoidant goals. The study reveals positive direct effect of mastery goals on metacognitive self-regulation. In addition, positive direct effects of metacognitive self-regulation on deep learning strategies and on self-regulatory strategies are found. However, some expected direct effects were not represented with significant parameters in the model. Performance-approach goals were not a significant predictor of other variables in the model. Also, there were no significant direct effects of mastery goals nor metacognitive self-regulation and deep learning strategies on total scores which were discussed here. 

The purpose of this study was to illustrate the relationship between self-efficacy, task value, goal orientations, metacognitive self-regulation, self-regulation and learning strategies and to investigate the unique contribution of each on the variability in students’ total scores of 12 exams. Our study revealed that students’ self-efficacy, task-value, self-regulation, and elaboration are significantly positively correlated with total scores. Path analysis demonstrated that self-efficacy was the strongest predictor of total score and positively predicted mastery goals, but negatively predicted avoidant goals. The study reveals positive direct effect of mastery goals on metacognitive self-regulation. In addition, positive direct effects of metacognitive self-regulation on deep learning strategies and on self-regulatory strategies are found. However, some expected direct effects were not represented with significant parameters in the model. Performance-approach goals were not a significant predictor of other variables in the model. Also, there were no significant direct effects of mastery goals nor metacognitive self-regulation and deep learning strategies on total scores which were discussed here.