Mapping Research Trends in the Community of Inquiry Framework within Online Courses: A Bibliometric Analysis
Bibliographic record
Abstract
The Community of Inquiry (CoI) framework has become a foundational model for understanding online learning environments, focusing on cognitive, social, and teaching presence. This study provides a comprehensive bibliometric analysis of research trends in the CoI framework within online courses from 2003 to 2023. The analysis identifies key contributors, top institutions, and prominent research terms, offering insights into the evolution of CoI research over the last two decades. Results indicate a significant increase in research activity, particularly following the COVID-19 pandemic, with the United States and Canada leading contributions. However, there is a growing global interest in CoI research, with emerging contributions from countries such as China, Turkey, and South Africa. Key institutions, including Purdue University and the University of Calgary, and influential authors such as Peter J. Shea and Jennifer C. Richardson, have played pivotal roles in advancing the field. The analysis also reveals the broad application of the CoI framework in fully online, blended, and hybrid learning environments, emphasizing its versatility. Despite limitations related to database coverage and keyword reliance, this study provides valuable insights into the current state of CoI research and highlights areas for future exploration. The findings underscore the importance of the CoI framework in enhancing the quality of online education and its adaptability across diverse educational contexts.
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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.137 | 0.165 |
| 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) | 0.001 | 0.000 |
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".