Digital Literacy in Canadian Classrooms: A Systematic Review of Teachers’ Professional Development Programs
Bibliographic record
Abstract
Recognizing the role of teachers in promoting students' digital literacy, it is essential to explore the support they receive in becoming digitally competent educators. This systematic review examined the incorporation of digital literacy in professional development programs for teachers and teacher candidates in Canada from 2012 to 2023, guided by Belshaw’s 8Cs model (Cultural, Cognitive, Constructive, Communicative, Confident, Creative, Critical, and Civic). Specifically, this research addresses the following question: What were the focus areas and key elements of digital literacy emphasized in professional development programs for Canadian teachers and teacher candidates between 2012 and 2023? The research team conducted a search across eight academic databases, focusing on peer-reviewed publications. Forty-two relevant publications were identified, aligning with the following requirements: focus on teachers or teacher candidates, Canadian context, and emphasis on digital literacy professional development. These publications were analyzed according to teachers’ career stage (preservice or in-service), grade level, subject(s) taught, adopted digital literacy framework, and Belshaw’s 8Cs elements. Findings highlight a holistic integration of digital literacy in professional development programs, yet reveal gaps in the creative, cultural, and civic elements of digital literacy. The study underscores the need for strategies to empower Canadian educators in these three areas.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.030 | 0.047 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".