Developing and Implementing a Culturally Responsive Self-Regulated Learning Framework: Exploring How Teachers Could Empower Culturally Diverse Learners in Inclusive Classroom Environments
Bibliographic record
Abstract
This study investigates the development, and integration, of a “Culturally Responsive Self-Regulated Learning (CR-SRL) Framework” to guide teachers in creating supportive classroom environments for culturally diverse learners. Literatures on culturally responsive teaching and self-regulated learning both suggest principles and practices for designing inclusive classrooms; however, from different perspectives. To pull those frameworks together, and building on sociocultural and situative perspectives, I conducted a theoretical analysis to conceptualize an integrated CR-SRL Framework. Using a multiple case study design, I field-tested how the framework might be useful by building practices collaboratively with three elementary classroom teachers. Data included classroom observations, document reviews (i.e., teachers’ lesson plans and assignment instructions), and teacher interviews. The findings from a qualitative analysis suggest that the CR-SRL framework helped the teachers in integrating CR-SRL practices. However, there were variations in the ways the teachers designed and implemented the framework based on their prior practices and learning experiences. Nevertheless, the teachers experienced both benefits and challenges as they were working to design and implement the framework. This paper concludes by discussing how educators might be supported to take up a CR-SRL framework to meet the needs of culturally diverse learners. Implications for theory and research, teacher education, and professional development are discussed.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".