Examining Classroom Contexts in Support of Culturally Diverse Learners’ Engagement: An Integration of Self-Regulated Learning and Culturally Responsive Pedagogical Practices.
Bibliographic record
Abstract
Research shows that culturally diverse students are often disengaged in multicultural classrooms. To address this challenge, literatures on self-regulated learning (SRL) and culturally responsive teaching (CRT) both document practices that foster engagement, although from different perspectives. This study examined how classroom teachers at schools that enrol students from diverse cultural communities on the West Coast of Canada built on a Culturally Responsive Self-Regulated Learning Framework to design complex tasks that integrated SRL pedagogical practices (SLPPs) and culturally-responsive pedagogical practices (CRPPs) to support student engagement. Two elementary school teachers and their 43 students (i.e., grades 4 and 5) participated in this study. We used a multiple, parallel case study design that embedded mixed methods approaches to examine how the teachers integrated SRLPPs and CRPPs into complex tasks; how culturally diverse students engaged in each teacher’s task; and how students’ experiences of engagement were related to their teachers' practices. We generated evidence through video-taped classroom observations, records of classroom practices, students’ work samples, a student self-report, and teacher interviews. Overall findings showed: (1) that teachers were able to build on the CR-SRL framework to guide their design of an CR-SRL complex task; (2) benefits to students’ engagement when those practices were present; and (3) dynamic learner-context interactions in that student engagement was situated in features of the complex task that were present on a given day. We close by highlighting implications of these findings, limitations, and future directions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".