Trends in Massive Open Online Courses (MOOCs) Research Over the Past Ten Years (2015–2024): A Bibliometric Analysis
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of research trends in Massive Open Online Courses (MOOCs) from 2015 to 2024. Using data from the Scilit database, we analyzed 707 peer-reviewed articles to identify patterns in research output, citation networks, and international collaboration. The analysis employed VOSviewer and Scimago Graphica tools to examine publication trends, author collaborations, citation patterns, and geographical distribution of MOOC research. Results reveal a steady increase in research output from 2015 to 2021, with self-regulated learning and learner engagement emerging as dominant themes. The United States and the United Kingdom lead in publications and citations, while emerging contributions from countries like Malaysia and China indicate growing global interest. Citation analysis identified key influential papers, with the most cited work focusing on self-regulated learning strategies (547 citations). Co-authorship analysis revealed 86 collaboration clusters, highlighting both the collaborative nature of MOOC research and opportunities for increased international cooperation. Keyword analysis evolved from fundamental implementation concerns to sophisticated applications of artificial intelligence and personalized learning. The findings suggest a maturing field emphasizing technological integration, learner support systems, and cross-cultural adaptations. This study provides valuable insights for researchers, educators, and policymakers involved in MOOC development and implementation while identifying emerging trends and future research directions in online education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.084 | 0.110 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".