Research Status and Development Trends of Experiential Learning in Music Education: A Bibliometric Analysis
Bibliographic record
Abstract
A global commitment has been observed toward the incorporation of experiential methodologies into education. Experiential learning strategies have been increasingly integrated into classrooms across various educational levels, and their use has been recognized as a core competency for educators (Council of Europe, 2018). The primary aim of this study is to analyze academic research concerning the application of experiential learning in music education. A sample was obtained from the Web of Science Core Collection, encompassing publications from January 1, 2014, to July 20, 2025. Multiple bibliometric tools, including GraphPad Prism v8.0.2, CiteSpace (6.2.4R), and VOSviewer (1.6.18), were employed to examine publication trends, relevant journals and authors, geographic distribution, keywords, and emerging research themes. The study analyzed 817 relevant publications spanning 76 countries and regions, 1,140 institutions, and 2,865 authors. The analysis demonstrated that (1) publication volume has shown a consistent upward trajectory, accelerating post-2019 with a peak anticipated in 2024; (2) the United States has led in both publication count (310, 37.94%) and citation frequency (5,111), followed by China; (3) the Journal of Chemical Education has been identified as the most prolific journal; and (4) research focal points have shifted from foundational topics such as categorization and integrated learning development to contemporary themes including active learning and concept drift. Looking ahead, two prominent areas of focus are anticipated to involve the integration of artificial intelligence and neural networks in music education, and the amalgamation of experiential learning with innovative pedagogical strategies.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.133 | 0.070 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".