Mapping case-based learning research from 2014 to 2024: a bibliometric and network analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Case-based learning (CBL) is a globally recognized pedagogical approach known for fostering critical thinking, collaborative problem-solving, and active engagement among learners. Despite its implementation across multiple educational levels and disciplines worldwide, the global research landscape of CBL remains underexplored. To address this gap, a comprehensive overview is needed to map the evolution of CBL research, delineate its geographical and institutional hubs, and identify dominant thematic areas. This study aims to map global research on CBL. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, bibliometrics, and network analyses to evaluate 883 articles indexed in the Web of Science Core Collection between January 2014 and August 2024. Our findings indicate a relevant increase in annual publications over time. The main author keywords are CBL, medical education, and problem-based learning. The main Research Areas are Education & Educational Research, followed by General & Internal Medicine, and Healthcare Sciences & Services. The United States, China, and Canada are the most productive countries, while the University of California, the University of Toronto, and Harvard University are the top organizations contributing to the field. This study provides a general understanding of the global research landscape on CBL, offering important insights for future studies and fostering research collaboration between organizations around the world.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.098 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it