Creating equitable classroom communities through self-regulated learning: an example from one primary classroom
Bibliographic record
Abstract
Educators within most developed nations strive to develop inclusive teaching practices within their classrooms to empower and engage students from diverse backgrounds. Despite their efforts, these practices are often difficult to develop and implement. This article bridges commonalities between multiple inclusive pedagogical frameworks, highlighting self-regulated learning as an inclusive educational framework. From this stance, a discussion is provided linking teachers’ implementation of practices that promote self-regulated learning to the fulfilment of students’ self-determined motivational needs of autonomy, competence and relatedness. Within this framework, a detailed description is presented of how one primary teacher in Surrey, British Columbia, Canada employed self-regulated learning strategies to foster students’ self-determined motivation for engaging in writing activities. A detailed examination of how self-regulated learning contributed to students’ fulfilment of motivational needs is presented. Additionally, the article illustrates how these strategies supported learners with varied academic abilities, offering practical insights into adaptable, responsive classroom practice. The article then concludes with a description of how teachers’ development and implementation of self-regulated learning–promoting practices fosters an inclusive learning environment for all students, encouraging sustained engagement and meaningful participation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".