Bibliographic record
Abstract
This article discusses the need to innovate education due to global changes to keep its status as a human right and public good and introduces Open Education as a theory to fulfil these requirements. A systematic literature review confirms the hypothesis that a holistic quality framework for Open Education does not exist. For its development, a brief history and definition of Open Education are provided first. It is argued that Open Education improves learning quality through the facilitation of innovative learning designs and processes. Therefore, sources of learning quality and dimensions of quality development are discussed. To support the improvement of the learning quality and design of Open Education, the Reference Process Model of ISO/IEC 40180 (former ISO/IEC 19796-1) is introduced and modified for Open Education. Adapting the three quality dimensions and applying the macro, meso, and micro levels, the OpenEd Quality Framework is developed. This framework combines and integrates the different quality perspectives in a holistic approach that is mapping them to the learning design, processes, and results. Finally, this article illustrates potential adaptations and benefits of the OpenEd Quality Framework. The OpenEd Quality Framework can be used in combination with other tools to address the complexity of and to increase the quality and impact of Open Education. To summarize, the OpenEd Quality Framework serves to facilitate and foster future improvement of the learning design and quality of Open Education.
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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".