Fostering Cultural Responsiveness Online: Elementary Educators’ Experiences During the COVID-19 Shift to Online Education
Bibliographic record
Abstract
The COVID-19 pandemic created a swift shift to online learning, challenging elementary educators to sustain Culturally Responsive Teaching (CRT) practices in virtual environments. This qualitative case study explored how elementary school staff in an eastern Canadian province experienced fostering CRT during this transition. Using Cultural Historical Activity Theory (CHAT) as a theoretical framework, the study analyzed the systemic contradictions educators navigated in online settings—including the digital divide, the complexities of building virtual relationships, and the challenges of maintaining cultural relevance for diverse learners. Through semi-structured interviews with seven educators from varied school contexts, the research captured how participants addressed technological inequities, engaged families and communities, and adapted their approaches to sustain culturally responsive practices. These findings highlight the urgent need for professional development and institutional support to strengthen CRT practices in digital and blended learning environments. The study underscores the importance of reimagining culturally responsive pedagogy beyond traditional classroom settings and calls for further research into sustaining equity-driven teaching practices across evolving educational landscapes.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".