Transformative Curriculum Design through Open Educational Resource Creation
Bibliographic record
Abstract
At their best, open educational resources (OER) are known to foster equity, accessibility, and flexibility for users and include multiple perspectives from a collaborative community for creators and contributors (Hylén, 2006). Therefore, when planning our university’s first offering of a week-long course (re)design workshop (based on Saroyan & Amundsen, 2004), recognizing we had been primarily using materials from other post-secondary institutions, we opted to create resources specific to our university through a one-day facilitated writing sprint. Moving beyond offering information simply for course design, we decided to create an OER that encompasses three main areas of curriculum planning and design: composition, mapping, and alignment of learning outcomes; choice and alignment of instructional strategies and learning activities; and alignment of outcomes assessment at all levels. During the OER content development stage, the group also recognized the opportunity to position the OER, named CRICKET, as a community building tool, focusing on learning re-design. The site not only hosts curriculum planning and design information, it also features an OER authoring tool that invites participants to share their work with their peers. Through our OER creation process, we determined that OER have the potential to transform not only how information is disseminated and used, but also how it is created.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".