Bibliographic record
Abstract
MOOCs (Massive Open Online Courses) have been around since 2008, when 2,300 students took part in a course called “Connectivism and Connective Knowledge” organized by University of Manitoba, Canada. The year 2012 was widely recognized as “The year of the MOOC”, because several MOOC initiatives gained a world-wide popularity. Nowadays, many experts consider MOOCs a “revolution in education”. However, other experts think is too soon to make such a claim since MOOCs still have to prove they are here to stay. With the spread of MOOCs, different providers have appeared, such as Coursera, Udacity and edX. In addition, some popular LMS (Learning Management Systems), such as Moodle or Sakai, have also been used to provide MOOCs. Besides, a new breed of LMS has appeared in recent months with the aim of providing specific tools to create MOOCs: OpenMOOC and Google CourseBuilder being two of them. The growing interest of MOOCs has led to the emergence of different forms of use. In some cases, such as xMOOCs, the initial concept has been distorted. In other cases, such as SPOCs (Small Private Online Courses), it has become possible to use MOOCs in alternative contexts which they were originally created. The aim of this paper is to clarify the enormous confusion that currently exists around the MOOCs. On one hand, in this paper we present different MOOC taxonomies that currently exist. On the other hand, we present several barriers for deploying MOOCs promises: language, cost, internet access, and web accessibility.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".