MOOCs: A systematic study of the published literature 2008-2012
Bibliographic record
Abstract
<p>Massive open online courses (MOOCs) are a recent addition to the range of online learning options. Since 2008, MOOCs have been run by a variety of public and elite universities, especially in North America. Many academics have taken interest in MOOCs recognising the potential to deliver education around the globe on an unprecedented scale; some of these academics are taking a research-oriented perspective and academic papers describing their research are starting to appear in the traditional media of peer reviewed publications. This paper presents a systematic review of the published MOOC literature (2008-2012): Forty-five peer reviewed papers are identified through journals, database searches, searching the Web, and chaining from known sources to form the base for this review. We believe this is the first effort to systematically review literature relating to MOOCs, a fairly recent but massively popular phenomenon with a global reach. The review categorises the literature into eight different areas of interest, introductory, concept, case studies, educational theory, technology, participant focussed, provider focussed, and other, while also providing quantitative analysis of publications according to publication type, year of publication, and contributors. Future research directions guided by gaps in the literature are explored.</p>
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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.064 | 0.047 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".