Bibliographic record
Abstract
In this chapter, the authors challenge the traditional differentiation between metacognitive monitoring and control in text-based self-regulated learning (SRL). Building on Pieshl (2009), the authors presented a case for conceptualizing and measuring calibration as the interaction between metacognitive monitoring and control under the assumption that learners adjust metacognitive judgments as they monitor and control their learning both within and between trials. To this end they describe three separate but related measures of calibration – assessment, internal, and strategic calibration – to address such questions as what kind of test will be given; how will I perform on such a test; and what can I do to improve my performance, respectively. Each type of calibration is mutually exclusive; however, overall calibration accuracy relies on the hierarchical interplay among all three types. Finally, they provide examples of how trace data for each type of calibration may be collected in a multimedia-learning environment.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.008 | 0.027 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".