Relation of Metacognitive Monitoring and Control Processes across the Life-Span
Bibliographic record
Abstract
The two studies presented here were conducted to explore the relationship between metacognitive monitoring and control processes across the life-span. Monitoring processes often guide control processes (goal-oriented learning), yet more recent work also documents that control processes can also be based on feedback from monitoring processes (data-oriented learning). Study 1 provided first evidence for data-oriented learning in older adults and in a life-span perspective. Participants of four age groups (third-grade children, adolescents, younger and older adults) were able to adapt their Judgments-Of-Learning (JOLs) based on their Study Time (ST). Effects were most pronounced for younger and older adults. Study 2 investigated the flexible interplay between goal- and data-oriented learning within one learning task for the first time in older adults and from a life-span perspective. Adolescents and younger adults were able to switch between models while elementary children and older adults hat greater difficulties to do so. Possible causes for developmental trends are discussed. In sum, the integration of both goal- and data-oriented learning within one task seems to be a complex process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".