Investigating Teacher-Student Interactions That Foster Self-Regulated Learning
Bibliographic record
Abstract
This article describes the use of qualitative methods to study young children's engagement in self-regulated learning. In particular, it describes how fine-grained analyses of running records have enabled us to characterize what teachers say and do to foster young children's metacognitive, intrinsically motivated, and strategic behavior during reading and writing activities in their classrooms. This article argues that in-class observations followed by semistructured, retrospective interviews ameliorate many of the difficulties researchers have experienced in past studies of young children's motivation and self-regulation. The observations and interviews provide evidence of children in kindergarten through Grade 3 engaging in self-regulatory behaviors, such as planning, monitoring, problem-solving, and evaluating, during complex reading and writing tasks. Also, they reveal variance in young children's motivational profiles that is more consistent with older students than has heretofore been assumed. Moreover, the in situ investigations of young children's self-regulated learning offer important insights into the nature and degree of support young children require to be successfully self-regulating.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".