Digital Literacies: a comparative analysis of in-service teacher education in Brazil and Canada
Bibliographic record
Abstract
In the constant flux of global migratory patterns, shifting borders, transliteracies and cross-cultural changes, digital technologies add certain complexities to an increasingly intricate and evolving educational landscape. In-service teachers grapple with digital literacy challenges in their appreciation of technology, changing teacher-student paradigms, and their own personal pedagogical philosophies. Within the context of the Brazil-Canada Knowledge Exchange Project and using a case study approach, this paper focuses on a comparative analysis of digital literacies amongst Canadian in-service teachers and their Brazilian counterparts. It further elaborates digital literacy concepts and considerations for mutually inclusive collaborations in multi-spherical global/local contexts. Supported by a bibliographic research and empirical data from two different case studies in Brazil and Canada, the paper examines in-service teacher professional development with a specific focus on technology education. The main findings suggest that pre-service and in-service teacher education should include digital literacies as part of their programs. They also suggest that these programs could take place through a critical framework, which can aid such practices so that technology education can be viewed as part of evolving social practices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".