Open For All: The OERu’s Next Generation Digital Learning Ecosystem
Bibliographic record
Abstract
This paper describes the functionality, scalability, and cost of implementing and maintaining a suite of open source technologies, which have supported hundreds of thousands of learners in the past year, on an information technology infrastructure budget of less than US$10,000 per year. In addition, it reviews pedagogical opportunities offered by a fully open digital learning ecosystem, as well as benefits for learners and educators alike. The Open Education Resource universitas (OERu) is an international consortium made up of 36 publicly funded institutions and the OER Foundation. The OERu currently offers first-year postsecondary courses through OER-based micro-courses with pathways to gain stackable micro-credentials, convertible to academic credit toward recognised university qualifications. The OERu, adhering to open principles (Wiley, 2014b), has created an open source Next Generation Digital Learning Ecosystem (NGDLE) to meet the needs of learners, consortium partners, and OERu collaborators. The NGDLE—a distributed, loosely coupled component model, consisting entirely of free and open source software (FOSS)—is a global computing infrastructure created to reach learners wherever they are. All OERu services are hosted on commodity FOSS infrastructure, conferring significant advantages and creating opportunities for institutions adopting any of these services to enhance education opportunities at minimal cost. The NGDLE can also increase technological autonomy and resilience while providing exceptional learning opportunities and agency for learners and educators alike.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".