Expanding K-12 Learning Opportunities Through Open Educational Practices
Bibliographic record
Abstract
In current K-12 contexts, there is great potential for research that examines the expansion of learning beyond formal learning environments, as well as inquiry about how digital networks can enable all learners to access people, content and ideas that were previously inaccessible. Open learning networks, which include formal, non-formal and informal learning environments, can afford transformed learning opportunities for K-12 students. This design based research expands upon the Building Futures program in which grade 10 students complete their core subject courses and career and technology studies courses in the process of building a house. This year, the students’ social innovation project focused on how to connect students with local government to promote student voice and choice in the community. The present research analyzed how the open learning design process supported the expansion of learning from the classroom to outside networks in both informal and non-formal ways. Using the open learning design intervention (OLDI) framework as a guide, the research team analyzed the extent to which open educational practice expanded learning opportunities for K-12 learners, the student and teacher perspectives of open educational practice experiences, and how the OLDI framework supported teachers in designing for expanded open learning experiences.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".