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The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis

Abstract

This research synthesis explores how academic achievement relates to two main components of self-regulated learning for students in elementary and secondary school. Two meta-analyses integrated previous findings on (1) the defining metacognitive processes of self-regulated learning and (2) students’ use of cognitive strategies. Overall correlations were small (metacognitive processes, r = 0.20; cognitive strategies, r = 0.11), but there was systematic variation around both of them. Five moderator analyses were conducted to explain this variation. Average correlations significantly differed based on the specific process or strategy, academic subject, grade level, type of self-regulated learning measure, and type of achievement measure. Follow-up tests explored the nature of these differences and largely support the hypotheses. Theoretical, methodological, and practical implications of these findings are discussed.

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Notes

  1. Documents that did not include an abstract were automatically retained. Sections describing the methodology and results in these documents were read in their entirety.

  2. Scales often included more than one construct, such as combining cognitive strategies and metacognitive processes or combining metacognitive processes and metacognitive knowledge. Correlations with these scales were initially excluded from either meta-analysis given their conceptual confounding. However, if the description of the measure led us to believe that these constructs could be disentangled into subscales, we attempted to contact the first author for correlations with subscale scores.

  3. In each categorical code, there was always an option of “Other” along with an opportunity to specify why the study was not best described by any of the predefined categories. This “Other” category was included for two reasons. First, the coding categories should be comprehensive and mutually exclusive, which including “Other” satisfies (Cooper 2010). Second, the predefined categories in our coding procedure were not meant to be exhaustive. By including “Other”, the coder was not forced to choose among categories that may not describe the information well.

  4. When students in a sample belonged to more than one category of a moderator variable, the sample was classified by all applicable categories. However, belonging to more than one category often led to the exclusion of a sample from that moderator analysis. For example, students in a sample were recruited from math and science classes, yet only an overall correlation between elaboration and exam grades was reported. This correlation would not contribute to the average for math or science in the moderator analysis by academic subject.

  5. Most often, correlations were calculated from means, standard deviations, and subgroup sizes when participants were grouped by their achievement level. For example, if a sample was separated into high- and low-achieving students, the mean and standard deviation of each group’s score on a self-regulated learning measure could be used to calculate the correlation between it and whatever measure of achievement was used to group the students. When correlations were calculated from means and standard deviations, they were often reported in the context of self-regulated learning interventions. In these cases, we calculated correlations from statistics at baseline or for the control group after an intervention. When both were provided, baseline statistics were used because they incorporated all students in a study. Some correlations were calculated by comparing the cognitive or metacognitive strategy use mean and standard deviation for students with a learning disability to those for normal- or high-achieving students without a learning disability. Correlations were only calculated in this case when students’ learning disability was defined as a discrepancy between their academic performance and intelligence. If an academic performance measure was not part of the diagnostic criterion, the group of students with a learning disability was not used in the calculation. When descriptive statistics were provided for more than two groups, the two most extreme were used to calculate the correlation (e.g., high- and low-achieving students when average-achieving students were also included in the sample). Doing so should maximize the variation between groups.

    If neither correlations nor subgroup means, standard deviations, and sample sizes were reported, we attempted to calculate correlations from relevant inferential statistics. When converting an analysis of variance to a correlation, studies were excluded if any of the groups contributing to relevant F ratios were not defined by their achievement level (e.g., students with high artistic ability). In these cases, the size of the F ratio, and thus the strength of the correlation, could be influenced by this group. Therefore, the resulting correlation would not entirely reflect a relation with academic performance. A non-parametric (e.g., Chi square) test was only converted to a correlation when the interpretation of both self-regulated learning and academic performance variables would make substantive sense as continuous. Only one nonparametric test appeared among the studies that qualified for the meta-analysis, and the substantive interpretation did indeed make sense as continuous rather than count data.

    Authors reporting other data from which a correlation could likely be calculated, including path coefficients from multiple regression or structural equation models, were contacted to request zero-order correlations. In total, 52 emails were sent of which 3 were responded to with the requested correlations.

    Preliminary tests and all analyses were conducted after correlations were transformed into standardized z-scores. This Fisher transformation of r values stabilizes their variance and normalizes the sampling distribution used for significance testing. A normal sampling distribution of z scores ensures that the upper and lower limit of a confidence interval will be of equal distance from the parameter estimate around which it is constructed. However, this may not be the case if the sampling distribution remains skewed, as it is for correlations (Cohen, Cohen, West, & Aiken, 2002; Cooper, 2010). After confidence intervals were calculated, their values and average effect sizes were transformed back into correlations.

  6. This shifting unit of analysis approach has two main advantages. First, it retains as much information as possible from each study while minimizing violations of the assumption that statistical tests are independent. Second, several correlations from a small sample would not have undue impact on an average relative to a larger sample with fewer correlations. Instead, each of these samples would contribute one correlation to the average weighted by its sample size (Cooper 2010).

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Acknowledgments

The authors would like to thank Drs. Jeffrey A. Greene, Lisa Linnenbrink-Garcia, and Rick H. Hoyle for their helpful comments on the draft of this manuscript.

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Dent, A.L., Koenka, A.C. The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis. Educ Psychol Rev 28, 425–474 (2016). https://doi.org/10.1007/s10648-015-9320-8

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Keywords

  • Self-regulated learning
  • Academic achievement
  • Meta-analysis