Bibliographic record
Abstract
<p class="3">Quality Assurance (QA) concepts and applications in Higher Education (HE) emerge from evolving meanings related to HE’s dynamic relationship with social, economic, cultural, and technological developments. The latter has been redefined by the growth spurred by the forms distance and online education acquired during the last decades. Creating a roadmap with clearly articulated meanings of quality and consistent key actions fills a need for the involved communities to reground the research, policy-making, and the related discourse. Our current work consists of a thorough meta-analysis on all available research in every identified pertinent field. It is a qualitative review of the concepts, definitions, and approaches about quality in general, but also specifically, in e-learning in HE, as they have globally appeared in peer-reviewed journals, government reports, and web pages. As we left no stone unturned in enquiring regarding the meanings, uses, evolution, and applicability of the revealed variables it is our hope that the roadmap we provide here will guide future research and support policy-making in the field. The present study is part of the research project<em> e-learning Quality Assurance Design Standards in Higher Education</em> (e-QADeSHE), which was funded by Laureate International Universities as the winning research project for the <em>International David Wilson Award for Excellence in Teaching and Learning</em> (2015 edition).</p>
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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 | 0.037 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".