What happened to OpenCourseWare?: a discussion of the open education movement’s shift from course content to textbooks
Bibliographic record
Abstract
The open education movement is now more than twenty years old, if counting from the launch of MIT’s OpenCourseWare (OCW) in 2001. Though OCW was instrumental in jumpstarting higher education’s enthusiasm for open educational resources (OER), provincial funding bodies have doubled-down on open textbooks, and this focus is reflected in the language used by provincial funding bodies, as well as the impressive, and growing, number of openly licensed textbooks. The focus on textbooks is understandable given the intense interest from students. Several provincial funding bodies, including BC Campus, OpenEd Manitoba, and eCampus Ontario have saved students millions of dollars by encouraging open textbook adoption in post-secondary. Furthermore, open textbooks (and other learning objects) are arguably easier to host and distribute than entire courses. The tradeoff is that this lopsided focus on open textbooks means that a few institutions (most notably MIT, TU Delft and others) dominate the OCW space. In this presentation, the author will argue the reasons why OCW investment is inconsistent and highlight the challenges that face OCW creation and adoption. The author will also argue that refocusing on OCW investment has the potential to bolster institutional reputation and satisfy a public that is hungry for high-quality open content.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".