Bridging Digital Equity and Cultural Responsivity in Elementary Schools: The Role of Family-School Partnerships
Bibliographic record
Abstract
The COVID-19 pandemic triggered a rapid shift toward remote learning, revolutionizing family-school relationships. The pandemic brought digital inequalities into sharp relief coupled with new possibilities for culturally responsive teaching (CRT). Within the context of our selected schools, families emerged as crucial partners for supporting student engagement and cultural connection during virtual learning in elementary schools in Canada. The present qualitative case study explores how seven elementary school staff members in Eastern Canada navigated the intersection between CRT, digital access, and family-school partnerships during pandemic-driven remote teaching. Based on our semi-structured interviews with the school staff and through a Family-School Partnership theoretical lens, we found that family involvement was a key strength for CRT implementation, as parents and guardians served as cultural liaisons facilitating students' learning at home. The teachers adapted their teaching practices through the integration of cultural knowledge at home, the use of multilingual tools, and involving families with culturally responsive classroom practices. However, there were challenges because there was disparate access to technology, poor internet connections, and varying digital literacy among family members. These inequalities had a disproportionate impact on students from minority communities, particularly Indigenous, immigrant, and low-income communities. Our findings highlight educators' innovation and adaptability toward facilitating culturally responsive digital pedagogy but also notes an opportunity to reinforce institution-level professional development support for culturally responsive digital pedagogy. Based on our findings, we emphasize the need for sustained investment in digital infrastructure/resources, targeted teacher training, and adaptive family engagement models for ensuring equal access and culturally responsive practice for future hybrid or remote learning environments. This research contributes to broader discourse on educational reform emerging from a pandemic era through its demonstration of how school-family relationships grounded in trust can serve as a foundation for culturally relevant, inclusive, and equitable learning. With schools moving toward digital and blended learning, overarching policies should cover strengthening school-home relationships and sustainable efforts to bridge the digital divides for diverse learners.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.031 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".