Open Educational Resources: New Possibilities for Change and Sustainability
Bibliographic record
Abstract
In an attempt to understand the potential of OER for change and sustainability, this paper presents the results of an informal survey of active and inactive collections of online educational resources, emphasizing data related to collection longevity and the project attributes associated with it. Through an analysis of the results of this survey, in combination with other surveys of OER stakeholders and projects, the paper comes to an initial conclusion: Despite differences in priorities and emphasis, OER initiatives are in danger of running aground of the same sustainability challenges that have claimed numerous learning object collection or repository projects in the past. OER projects suffer from the same incompatibilities with existing institutional cultures and priorities that have dogged learning object initiatives, and they face the concomitant challenge of gaining access to the operational funding support that experience shows is necessary for their survival. However, through a review of one of the most successful of OER projects to date, the MIT Open Courseware Initiative, the paper ends by augmenting this significant caveat with a second, more hopeful conclusion: OER projects, unlike learning object initiatives, can accrue tangible benefits to educational institutions, such as student recruitment and marketing. Highlighting these benefits, it is argued, provides an opportunity to link OER initiatives to core institutional priorities. In addition to providing a possible route to financial sustainability, this characteristic of OER may help to foster the significant changes in practice and culture long sought by promoters of both learning objects and OERs.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.028 | 0.053 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".