Dimensions of self-regulated learning and academic achievement in college students

The purpose of this study was to first investigate the factor structure of a data set, which included the measures of: (a) executive functions, (b) metacognitive strategies, (c) time management, and (d) academic self-efficacy in a sample of undergraduate students (N = 45) . A second purpose was to explore whether there were differences between low-achieving (n = 21) and high-achieving college students (n = 24) in terms of the scores on the underlying factors identified in the factor structure that presumably will align with the measures of executive functions, metacognitive strategies, time management, and academic self-efficacy or some type of combined variables. The results from Exploratory Factor Analysis showed that 3 factors were retained from 11 measures that represent executive functions, metacognitive strategies, time management, and academic self-efficacy. Six self-report-measures, which basically represent executive functions, time management strategies, and self-efficacy loaded on Factor 1, and this factor was labeled as Perceived Self-Regulation (PSR). Three measures, which basically represent metacognitive strategies, loaded for Factor 2, and this factor was labeled as Metacognitive Knowledge Strategies (MKS). Also, two direct measures, which represent executive functions, loaded on Factor 3, and this factor were labeled as Executive Control Processes (ECP). Results from the independent sample t-tests showed that there were mean differences in the scores for the three factors, which identified in factor analysis (i.e., PSR, MKS, and ECP), between the high-achieving group and the low-achieving group in favor of the high-achieving group. Finally, Research Question 3 addressed the degree to which an individual’s membership (i.e., high- and low-achieving groups) could be correctly classified by the scores of the three factors scores by determining the contribution of each factor to predict individual’s membership while controlling for the other factors, and this was assessed through Hierarchical Binary Logistic Regression. Logistic Regression analysis showed that ECP appears to have a direct, and strong, effect on (or contribution to) the discrimination between the high- and low-achieving groups. Second, the contribution of MKS to the identification of high- and low-achieving group membership appears to be entirely mediated by the PSR factor; however, the PSR has a direct, moderate relationship to group membership