Junior Teachers’ and Students’ Perspectives of Culturally Responsive Pedagogy Through Texts, Technology, and Collaboration
Bibliographic record
Abstract
With the rich cultural diversity of Ontario’s classrooms, educators must work to ensure their teaching practices support and represent their students. This study sought to gather teachers’ perspectives of culturally responsive pedagogy (CRP), resources available, and how teachers can be better supported to be culturally responsive to their learners. It also aimed to engage students using texts, technology, and collaboration to help them understand diversity and inclusion. This research was built upon the ideas of Ladson-Billings (1994), Freire (2005), and Gay (2018). The study employed the generic qualitative research method to collect data on a project with two educators and three Junior students over a period of 12 weeks. Data collection included field notes, interviews, planning sessions, and one-on-one interactions with the researcher. Results indicated that educators have a good understanding of CRP but lack access to current, representative resources. Further, findings indicate a shift in teaching practices and student learning when culturally responsive practices are used. Students also possessed a good understanding of diversity and inclusion when engaged in culturally responsive texts and technology. Junior educators and students indicated a positive classroom experience when learners were represented in classroom materials and lessons. Lastly, educators are willing to learn new strategies and resources that are culturally responsive, but professional development workshops are not always accessible and applicable to their classrooms. Overall, this research suggests implications for practice, research, and theory that can all be used to effectively support Ontario educators in using CRP within their classrooms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".