Effects of Metacognition on Performance in Mathematics and Language- Multiple Mediation of Hope and General Self-Efficacy
Bibliographic record
Abstract
This study examined (a) students’ reported use of metacognitive knowledge (declarative, procedural, conditional) and metacognitive regulation (planning, monitoring, information management, evaluation) when they are doing school work or homework, and the effect of metacognition on school performance in language and mathematics and (b) the role of hope (agency thinking, pathway thinking) in general self-efficacy, in the impact of general self-efficacy on metacognition, and in the effect of metacognition on school performance. One hundred and sixty-five 5th and 6th grade students (83 boys, 82 girls), randomly selected from 10 state primary schools of various regions of Greece, participated in the study. Data gathered at the second school term of the total three terms. The results revealed that: (a) the reported frequency of use of metacognitive knowledge (mainly, conditional) and metacognitive regulation (mainly, monitoring) was at a moderate extent, (b) hope (predominately, pathway thinking) was a positive formulator of general self-efficacy and of its impact on metacognition, but the influential role of the two constructs differed between and within the components of metacognition, (c) the three sets of predictors had complementary and positive effects on school performance but their relative power in influencing it varied between mathematics and language and within each school subject, with agency thinking being the most powerful predictor and (d) general self-efficacy mediated the impact of metacognition on school performance, while hope had direct impact on school performance beyond that of metacognition and general self-efficacy. The findings are discussed for their practical applications in education and future research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".