Inclusive Digital Education on Open Platforms: A Case Study of the Complexity of the Future of Education
Bibliographic record
Abstract
Open education platforms can be a valuable bridge supporting inclusive education.This article reports an international open education program conducted within the context of COVID-19.The guiding question was: What challenges lie ahead in the future of education, allowing open platforms to facilitate an inclusive digital education that considers special educational, contextual, and diverse learning needs?A case study involved 959 participants in five webinars.The results reported: (a) challenges facing open platforms for inclusive education, (b) current open practices for inclusion, (c) production of open educational resources for inclusion, (d) processes necessary for the production of open platforms, and (e) institutional requirements for inclusive digital education.The study is interest to academic, scientific, governmental, and societal communities and designers, computer developers, and decision-makers interested in educational practices promoting digital equity and inclusive education.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".