Quality Assurance for Online Higher Education Programmes: Design and Validation of an Integrative Assessment Model Applicable to Spanish Universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The quality assurance of online Higher Education online programmes is one of the great challenges faced by Spanish universities. Regular assessment of these programmes is essential in order to take actions to improve their quality. The said assessment should be complex and include all of the components of the programme, as well as its planning and implementation stages and its effects. The purpose of this paper is to present a model designed to assess the quality of online Higher Education online programmes that includes the assessment of the quality of the programme itself, as well as its continuous assessment. In order to design the model, the author conducted a bibliographical analysis of different standards, models, and guides developed in Spain and other countries to assess online education. The model was validated by 23 international online education experts. The results of the validation were triangulated with specialized literature, thus allowing the author to make decisions regarding whether to change the model by keeping, reformulating, or removing a dimension or indicator. As a result, two variables, fourteen dimensions, and 81 indicators were obtained. In order to verify the utility of the model it was applied in the assessment of four online programmes. The model guides the persons in charge of the implementation of online programmes and allows to conduct a more comprehensive assessment of the programme in order to discover its strengths and weaknesses, and opportunities for its improvement. The model can be also applied by online programme designers as a guideline for creating other, high quality programmes.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it