SUSTAINABLE INFRASTRUCTURE: DEVELOPING A RE-USABLE PRODUCTION TOOLKIT FOR EFFICIENT ONLINE COURSE DESIGN
Bibliographic record
Abstract
Distance education has come a long since the days of mailed correspondence, with little interaction and a timely delay. As technology continues to more robustly offer opportunities for instructors and learners to be distributed but still interactive, more options for course structures emerge. Open online course development for distance education is a time consuming process that requires deep thought about personal pedagogical beliefs and an exploration of the technological tools available (for both development and for use in the course). This paper details the development of an online open course that features not only open access to course content and files, but also to the supporting resources that were used by the team involved in developing the course. These resources build what the authors are calling a "production toolkit," which can be put into action by any individual or team embarking on the development of their own course. This paper details the background of the project, an overview of the pedagogy that underpins the project, the tools used to produce the course, and the design decisions used during production. Though specific examples are used from the course, the design principles and resources presented can be used across projects.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".