Teachers working together to foster self- regulated learning through reading by students in an elementary school located in a disadvantaged area
Bibliographic record
Abstract
A key question for the educational research community concerns how teachers can adapt theories and practices supported by the scientific literature in ways that meet the particular needs of students in classrooms. The research reported here takes up that question by investigating how a team of teachers in an elementary school in a disadvantaged area built from research and theory to enact, try out, and refine classroom practices supportive of self-regulated LTR in their subject-area classrooms in light of their students’ needs. Our goals were to (1) describe what classroom activities participating teachers planned and implemented to integrate attention to the curriculum with support for self-regulated LTR; (2) evaluate the match between teachers’ practices and recommendations from the literature, as represented in the framework on which they were drawing; and (3) trace how practices enacted by teachers could be related to the perceptions and learning processes of their 123 Grade 5 and 6 students. A key finding was that teachers were successful in embedding activities into classrooms that engaged students in self-regulation, particularly use of cognitive strategies. However, we also found that the practices enacted did not sufficiently push students towards self-conscious, deliberate self-direction of learning. Implications are drawn about how classrooms practices can be constructed to take into account qualities of LTR contexts and supports necessary to foster success in school by young, at-risk learners. Discussion also focuses on benefits and challenges when teachers work together to adapt practices for use in subject area classrooms in ways that are responsive to student needs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".