Examining culturally diverse learners’ motivation and engagement processes as situated in the context of a complex task
Bibliographic record
Abstract
Student learning processes, including motivation and engagement, have been identified as malleable and situated in context. We have limited understanding about how to enhance motivation and engagement processes for culturally diverse learners in today’s multicultural classrooms. To support thinking about that challenge, this work built on research on both culturally responsive teaching (CRT) and self-regulated learning (SRL), each of which identifies pedagogical practices that enhance student engagement and motivation. This study examined how students at a culturally diverse independent elementary school in the West Coast of Canada participated in classroom context that integrated CRT and SRL-promoting practices. Specifically, this study examined culturally diverse learners’ engagement and motivation during a complex learning task. Data collected included classroom observations, practice records and documents, students’ work samples, and student interviews and student surveys. The results demonstrated: (1) above medium levels of engagement and motivation, among participants, that varied across specific contexts; and (2) associations between culturally diverse learners’ engagement and motivation; and complex learning context such as CRT and SRL-promoting practices. Implications for future research on culturally diverse students’ engagement as well as designing a complex task that integrated a culturally responsive teaching and self-regulated learning pedagogical practices to support engagement and motivation 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".