Clarifying the Connections Among Giftedness, Metacognition, Self-Regulation, and Self-Regulated Learning: Implications for Theory and Practice
Bibliographic record
Abstract
The concept of giftedness has historically been shaped by theories of IQ, creativity, and expertise (including early conceptions of metacognition). These theories focus within the mind of the individual learner. Social, emotional, and motivational qualities of giftedness were treated as add-ons, not part of the core construct. This created misalignment with the social construction of knowledge—a position widely supported in gifted education practice. Newer, broader conceptions of metacognitive, self-regulated, and self-regulated learning processes have garnered interest. However, because these theories borrowed language from each other and earlier theories, assigning new meanings to old constructs, confusion arose about how to distinguish each of these three theories from each other or apply them to instruction. This article distinguishes among metacognition, self-regulation, and self-regulated learning, relating each to notions of giftedness, highlighting implications for practice, and especially highlighting self-regulated learning as a valuable contributor to understanding giftedness and designing instruction in gifted education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.038 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".