Cultivating Literacy Engagement in Multilingual and Multicultural Learning Spaces
Bibliographic record
Abstract
Classrooms are changing rapidly in response to Canada’s linguistically and culturally diverse demographic profile. Learning in the 21st century calls for inquiry into different ways of designing curriculum, teaching, conducting assessments and educational research than is found in K-12 education today. In this doctoral research, I examined the impact of a curricular innovation to challenge the monolingual and monocultural norms of literacy practices and to be responsive to the linguistic and cultural landscape of 21st century classrooms. Using design based principles and a mixed methods approach to data collection and analysis, I collaborated with 11 university pre-service teachers to bridge theory and practice to inform early literacy practices for 28 students who are learning English, i.e., English Language Learners (ELLs). The primary question framing this study was, “How can educators cultivate literacy engagement to support English language development?” The findings of this study show the positive impact the designed literacy intervention had on supporting the linguistic and cultural needs of the young ELLs as well as how the implementation of the literacy intervention supported the pre-service teachers’ emerging practice. Through the design based research process, I created design principles to inform early literacy practices for young ELLs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".