Abstract
This meta-analytic study estimated the correlations among metacognition, intelligence, and academic performance. Metacognition is higher order cognition and one of the most significant predictors of academic performance. The purpose of this study was to examine the degree to which metacognition predicted academic performance when controlling for intelligence. The analysis of 149 samples from 118 articles revealed that, overall, metacognition weakly correlated with both academic performance and intelligence, and that these relationships were moderated by the type of measurement of metacognition. Furthermore, it was found that metacognition predicted academic performance when controlling for intelligence. Our findings indicate the importance of metacognition in educational practice and provide guidance for assessing metacognition in future research.
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Notes
In this study, we did not include metacognitive experiences because, despite their reflection of metacognitive monitoring, metacognitive experiences include affective and/or motivational components (e.g., feeling of satisfaction). Affective and/or motivational components are of significance in self-regulated learning (see Efklides 2006, 2011). However, inclusion of such components broadens the concept of metacognition in the current research (i.e., cognition of cognition), and such components may relate to academic performance via different learning processes from what is tested in the present research on a cognitive process rather than an affective process, which requires more careful theoretical speculation.
Other concepts of intelligence may also be related to academic performance and/or metacognition via different learning processes. For example, emotional intelligence may facilitate academic performance as well as metacognition via academic social interaction (e.g., group or peer-learning) because social skills or perspective taking that reflect aspects or outcomes of emotional intelligence are essential to collaborative learning (Johnson and Johnson 1990) and, consequently, such collaborative learning can promote both one’s own and peers’ metacognition (e.g., Miller and Hadwin 2015). However, this kind of learning process is more than we can handle in this research and, consequently, we concentrate on the concept of the classical theory of intelligence (g) presented above.
The research concerning metacognition and intelligence has been intensively studied by metacognition (or metacognitive skill) researchers; therefore, we focused on the terms “metacognition” and “metacognitive skills” for this search list. We also searched by applying broader metacognition terms, including “self-regulated learning” and “self-regulated learning strategies”; however, the search results were almost the same and the obtained studies for integrating effect size would be identical to our main search. We also tested the combination of metacognition terms, intelligence terms, and achievement terms to find studies that reported all three correlations at once. There were only a few such studies and all were included in our main search.
We asked three corresponding authors in total, and one author kindly replied.
Studies that used the Learning and Study Strategies Inventory were excluded. Although it measures various learning strategies reflecting metacognitive activities, most items and subscales on the inventory are considered to assess cognition and other strategies, rather than metacognition per se, which causes difficulty in differentiating between metacognition and other factors. For other scales, we selected those designed to assess metacognition (i.e., knowledge or activities) consistent with our inclusion criteria.
We also created a forest plot; however, we have not presented it here because the study sample of the analysis were large and consequently, the visibility of the figure was poor. Detailed information may be obtained from the corresponding author.
The child category also significantly differed from the secondary school category (p < .05).
We also tested the model in which interview and accuracy categories were entered in the above mentioned model because those categories were relatively large effect sizes for off-line methods. The measurement of metacognition was still significant, but both performance domain and overall developmental stage were non-significant.
The first analysis that included the full study samples caused a problem and failed to converge. Therefore, we omitted the study that was considered to have caused the problem (Veenman and Elshout 1999; Experiment 1, Sample 1) from the analysis; consequently, the overall sample size for the analysis were (k = 148, N = 369,039).
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Further Reading
The studies marked with an asterisk represent the studies included in the meta-analysis.
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Ohtani, K., Hisasaka, T. Beyond intelligence: a meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition Learning 13, 179–212 (2018). https://doi.org/10.1007/s11409-018-9183-8
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DOI: https://doi.org/10.1007/s11409-018-9183-8