From Isolation to Innovation: Narrative Self-Study of Teachers Adopting Digital Pedagogies in Remote Canadian Regions
Bibliographic record
Abstract
Background. Teachers in remote Canadian regions have historically faced challenges related to geographic isolation, limited access to professional development, and infrastructural disparities. The COVID-19 pandemic accelerated the demand for digital pedagogies, forcing educators in these contexts to rapidly adopt unfamiliar technologies and reconfigure their instructional practices. Purpose. This study investigates how teachers in remote areas navigated this transition through a narrative self-study lens. Method. Using qualitative methodology, five educators from rural provinces in Northern Canada engaged in self-reflective journaling and peer dialogue over a nine-month period. Thematic analysis of the narratives revealed key tensions between professional isolation and digital empowerment, as well as shifts in teacher identity, agency, and pedagogical innovation. Results. Participants described initial resistance, technological uncertainty, and emotional fatigue, which gradually evolved into adaptive strategies, collaborative learning, and renewed professional purpose. The findings highlight how digital transformation, though initially disruptive, served as a catalyst for reflective growth and community-building in marginalized teaching environments. Conclusion. The study concludes that narrative self-study can be a powerful tool for supporting teacher resilience, agency, and innovation, especially in geographically and technologically constrained settings.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".