Opening Doors to Open Digital Practice for Educators: The Open Page Project
Bibliographic record
Abstract
This paper outlines the design and purpose of an open educational resource (OER) project focused on developing digital literacies and open educational practice (OEP) within a Canadian Faculty of Education. Called The Open Page, the project features a Tool Parade of videos and podcasts created with and by Bachelor of Education (B.Ed.) students). Designed to enable students to build critical and participatory digital literacies with common classroom tools, and to encourage the development of OEP, the project assesses classroom uses of specific educational technology platforms. It also engaged student creators in analysis of various platforms' implications for student data and for differentiated learning. Featured on the University of Windsor Faculty of Education's website, The Open Page and its Tool Parade of OER offer professional development resources for faculty and practicing teachers and contributes to a common conversation about digital learning between educators at all levels. This paper will overview The Open Page and its creation, and the ways in which it represents an effort to focus pre-service teachers on the participatory and production capacities of the web for digital learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".