Bibliographic record
Abstract
The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners’ sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues’ studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners’ choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing “error variance” as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".