OPEN EDUCATIONAL RESOURCES IN UNDERGRADUATE OPEN EDUCATIONAL RESOURCES IN UNDERGRADUATE CHALLENGES
Bibliographic record
Abstract
Open Education Resources are pedagogical resources which are available under open licences for reuse and remixing. These resources support collaborative development of education material, the ongoing evolution and improvement of the material and easy access to both educators and students. Several initiatives exist for OER in Canada, and resources specifically targeting engineering are beginning to emerge. At the moment, those efforts are fragmented. In line with the mission to the Canadian Engineering Education Association (CEEA) OER SIG, this paper presents an overview of current Canadian and international Engineering OER initiatives. Based on the findings, several challenges and opportunities pertaining to engineering OER are identified and recommendations are provided for engineering instructors and institutions who wish to increase the use of OER in engineering programs. For instructors, this could be adapting OER where available. For those looking to develop OER, there may be grants and resources at the institutional and provincial levels to support this. For institutions, this may be supporting instructors in using or developing OER through grants or recognition.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".