Emerging Research Trends in Experiential Learning in Higher Education: A Scopus Database Bibliometric Analysis
Bibliographic record
Abstract
This study aims to map global research developments on experiential learning (EL) in higher education by employing a bibliometric approach. Experiential learning has gained increasing attention as a transformative pedagogy that connects theoretical understanding with real-world application. Research data were collected from the Scopus database for the years 2023–2025 and analyzed using VOSviewer and Biblioshiny to examine publication trends, citation patterns, author productivity, and thematic clusters. The findings reveal a sharp and consistent increase in EL-related publications worldwide, with the United States, Canada, and the United Kingdom emerging as the most productive countries, while Southeast Asia—particularly Indonesia—shows relatively limited contributions. The analysis also identifies several emerging thematic clusters, including the integration of EL with immersive technologies such as artificial intelligence, virtual reality, and the metaverse, as well as applications in service learning, entrepreneurship education, and authentic assessment. The novelty of this study lies in its focused examination of the 2023–2025 publication period, capturing the latest global trends shaped by digital transformation and sustainability agendas. Practically, the results highlight the need for stronger institutional and policy support to systematically embed EL within higher education curricula. Overall, this study contributes to strengthening evidence-based, student-centered learning and informs future research opportunities in experiential education.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.196 | 0.297 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".