Into the Open: Shared Stories of Open Educational Practices in Teacher Education
Bibliographic record
Abstract
Navigating through the Faculty of Education as a teacher educator in Canada is complex and complicated. Research literature calls for an intentional focus on media and digital literacies, and technological competencies, in teacher education. Program directions are confounded by technological trends emerging in kindergarten to grade twelve education and higher education. This post-intentional phenomenological research study examined moments, materials, and insights from the stories shared by participants as they revealed media and digital skills, fluencies, competencies, and literacies in their open educational practice. This research provides insights into how teacher educators seize opportunities to work through complex matters while applying technology resources. It is becoming ever more important to share expertise as practitioners, researchers, and theorists in the field of education by making explicit what is often tacit and unspoken, and when sharing knowledge, reflections, and actions. By actively thinking-out-loud through blogs, social media, and open scholarly publications, educators can openly share details of what, how, and why they do what they do. Research findings reveal the importance of media and digital literacies in the dimensions of communication, creativity, connections, and criticality within an open educational practice as a teacher educator.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.055 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| 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".