Bibliographic record
Abstract
This study seeks to understand how to use formal learning activities to effectively support the development of open education literacies among K-12 teachers. Considering pre- and post-surveys from K-12 teachers (n = 80) who participated in a three-day institute, this study considers whether participants entered institutes with false confidence or misconceptions related to open education, whether participant knowledge grew as a result of participation, whether takeaways matched expectations, whether time teaching (i.e., teacher veterancy) impacted participant data, and what specific evaluation items influenced participants’ overall evaluations of the institutes. Results indicated that 1) participants entered the institutes with misconceptions or false confidence in several areas (e.g., copyright, fair use), 2) the institute was effective for helping to improve participant knowledge in open education areas, 3) takeaways did not match expectations, 4) time teaching did not influence participant evaluations, expectations, or knowledge, and 5) three specific evaluation items significantly influenced overall evaluations of the institute: learning activities, instructor, and website / online resources. Researchers conclude that this type of approach is valuable for improving K-12 teacher open education literacies, that various misconceptions must be overcome to support large-scale development of open education literacies in K-12, and that open education advocates should recognize that all teachers, irrespective of time teaching, want to innovate, utilize open resources, and share in an open manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".