I think therefore I learn: metacognition is a better predictor of school readiness than executive functions
Bibliographic record
Abstract
Previous research suggests that metacognition (the knowledge and skills related to knowledge acquisition) and executive functions (skills needed to plan and execute goals) are possible predictors of academic performance, including math and reading abilities. This study sought to clarify the relationship between school readiness and these abilities. A visual identification task was used to measure preschool children's metacognitive skills, specifically their ability to monitor their confidence on their answers (explicit) and ability to ask for a clue only when necessary (implicit). Response time to answering was also measured to obtain a non-verbal implicit measure of metacognition. Executive functions were measured using the Flanker and Dimensional Change Card Sorting (DCCS) tasks from the NIH toolbox. It was hypothesized that both metacognition and executive functions would predict school readiness and that implicit metacognitive skills would be more highly related to school readiness than explicit skills. A hierarchical linear regression was run with age and sex as control variables, and with executive function and metacognition (implicit and explicit) as predictors. Results indicated that both implicit and explicit metacognition remained significant predictors of school readiness scores beyond age and sex. In addition, we found correlations between explicit metacognition and executive functions and a relationship between response time and explicit metacognitive skill. Results highlight the importance of early metacognitive abilities beyond other cognitive skills and the importance of being able to effectively use metacognitive strategies from a young age. The implications relating to academic abilities are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".