Moving to open educational resources at Athabasca University: A case study
Bibliographic record
Abstract
Since the birth of the World Wide Web, educators have been exchanging ideas and sharing resources online. They are all aware of the turmoil in higher education created by freely available content, including some hopeful developments charted in this issue. Interest has grown steadily over the past decade in making a university-level education openly available to students around the globe who would otherwise be overlooked, and recommendations for how to do this are well documented (e.g., UNESCO, 2002; OECD, 2007). Initiatives in the United States (Thille, 2012), Canada (Stacey, 2011b), Africa (OER Africa, n.d.), and the United Kingdom (JISC, 2012) are easily accessed and case studies abound (e.g., Barrett, Grover, Janowski, van Lavieren, Ojo, & Schmidt, 2009). Supporting the widespread availability of OER is a goal that Athabasca University (AU) has embraced through association with the Commonwealth of Learning and by becoming a charter member of the OER University (OERu, 2011). The use of OER in AU programs has strategic local implications that go beyond the five reasons for institutions to engage in OER projects described by Hylén (2006). Recently at AU explorations have begun into the potential of using OER in course design and production.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".