Bibliographic record
Abstract
Previous studies have mainly investigated major massive open online course (MOOC) platforms such as Coursera, edX, and Udemy. This study used 21 metrics to explore 35 MOOC platforms from across the world. Five Web analytics tools were used to analyze these MOOC platforms using data from MOOC platform directories and exploration of platform sites. The findings revealed that many universities, companies, and organizations have cooperated with the platforms and provided MOOCs through them. Major global platforms have offered thousands of MOOCs while regional platforms were more likely to have offered dozens. Some large platforms had millions of registered users while others registered just thousands. The major global platforms were established in the US to offer MOOCs mainly in English, though they offered MOOCs in other languages as well. The regional platforms offered MOOCs mainly in local languages, and to some extent in English and other languages. Some platforms engaged users for long periods while others failed to keep users after they viewed the first page of the platform. On average, a visitor stayed on a platform for 8 minutes visited 7.2 pages per visit. Major global platforms attracted users from all over the world, while regional platforms mainly attracted users from countries where the regional platform language was spoken. Some platforms had very few accessibility and contrast errors while other platforms performed poorly. Most platforms were mobile-friendly. However, administrators of almost all MOOC platforms should take actions to increase the speed of their platform. Other recommendations include undertaking marketing campaigns to increase the number of partners, the number of MOOCs offered, and the platforms’ visibility.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".