Growth and Collaboration in Massive Open Online Courses: A Bibliometric Analysis
Bibliographic record
Abstract
Massive open online courses (MOOCs) are an important approach for achieving UNESCO’s aim of open and accessible education. However, there are concerns regarding fragmentation or bias of MOOCs toward certain disciplines or countries. This study sought to: (a) examine how MOOCs research has evolved and is distributed, (b) determine what key areas are discussed in MOOCs research, and (c) identify the major players in MOOCs research and their collaborations. This study conducted a bibliometric analysis of 3,118 scholarly works related to MOOCs as recorded in the Scopus database in July, 2019. Specifically, we analyzed the evolution of MOOCs research by examining (a) published studies, (b) source titles, (c) types of sources and documents, as well as (d) the languages in which the documents were written in. We further analyzed the key areas of MOOCs research by looking into common subject areas, keywords used most often, and title analysis. Finally, we sought to increase our understanding of the major players in MOOCs research and their collaborations by examining (a) which countries contributed most to MOOCs research, (b) the main institutions involved, as well as (c) authorship and citation analysis. Findings indicated that in their early development starting in 2009, MOOCs caught the attention of scholars from both the East and the West, and the number of publications grew consistently over the 10 years after that. MOOCs research has been well distributed but has yet to adequately encourage inclusiveness. There has been healthy cross-country collaboration, but there is a gap in MOOCs research originating from certain countries as compared to the rest of the world. Our findings provide important input towards improving the inclusivity and global reach of MOOCs.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.135 | 0.171 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".