Ranking Meets Distance Education
Bibliographic record
Abstract
University ranking systems are being implemented with the aim of assessing and comparing higher education institutions at a global level. Despite their being increasingly used, rankings are often strongly criticized for their social and economic implications, as well as for limitations in their technical implementation. One of these limitations is that they do not consider the specific characteristics of online education. This study used a participatory approach to define a set of criteria and indicators suitable to reflect the specific nature of distance education. This endeavour will help evaluate and rank online higher education institutions more appropriately than in current practice, where indicators are devised for traditional universities. To this end, several stakeholders and informants were involved in a Delphi study in an attempt to reach the broader higher education institutions (HEI) community. According to the study participants, apart from students’ achievements and general quantitative measures of HEI performance, which are quite common in traditional ranking systems, teaching and student learning experience turned out to be the most important criteria. Student support, teacher support, technological infrastructure, research and organization were deemed middle ground criteria, while sustainability and reputation were regarded as the least important criteria.
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 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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".