Quality Assurance for Online Higher Education Programmes: Design and Validation of an Integrative Assessment Model Applicable to Spanish Universities
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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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".