Müzik eğitimi araştırmalarının bilimsel haritası: WoS literatürünün bibliyometrik analizi (2000-2025)

Öz

Bu araştırma, 2000-2025 yılları arasında müzik eğitimi literatüründeki bilimsel üretimin entelektüel yapısını ve araştırma eğilimlerini bibliyometrik bir perspektifle incelemeyi amaçlamaktadır. Web of Science (core collection) veri tabanından elde edilen 8.851 makale, R-Studio Bibliometrix programı kullanılarak bibliyometrik analiz ve bilimsel haritalama yöntemleriyle çözümlenmiştir. Bulgular, literatürün son 25 yılda Price Yasası ile uyumlu şekilde üstel bir büyüme sergilediğini ve müzik eğitiminin bilimsel literatürde olgun bir disiplin haline geldiğini göstermektedir. Bradford Yasası’na göre literatürün %36,59’u IJME ve JRME gibi altı çekirdek dergide yoğunlaşmaktadır. Lotka Yasası analizi, yazar sürekliliğinin düşük olduğunu ve üretimin dar bir çekirdek grup tarafından domine edildiğini göstermektedir. Kelime analizi ve tematik haritalama eğitim, öğretmen ve müzik gibi kavramların “motor temalar” olarak öne çıktığını ve müzik eğitiminin pedagojik temeller üzerine inşa edildiğini göstermektedir. Bununla birlikte trend analizi sonucunda, son dönemde müzik eğitiminin geleneksel çerçeveden yapay zekâ, büyük veri ve sanal gerçeklik gibi teknoloji odaklı konulara doğru dijital dönüşüm sürecine girdiği saptanmıştır. Müzik eğitiminin disiplinler arası etkileşimi güçlenmekte, metodolojik olarak olgunlaşmakta ve küresel ölçekte kurumsallaşmış bir bilimsel disipline dönüşmektedir. Aynı zamanda toplumsal krizlerle teknolojik değişimlere hızla uyum sağlayabilen dinamik bir yapıya kavuştuğu sonucu ortaya çıkmaktadır.

Anahtar Kelimeler

• müzik eğitimi
• bibliyometrik analiz
• bilimsel haritalama
• dijital dönüşüm
• Web of Science

Destekleyen Kurum

Yazarlar, çalışmanın herhangi bir finansman almadığını beyan ederler

Etik Beyan

Bu araştırmada insan, hayvan ve hassas veriler kullanılmadığı için etik kurul onayı gerekmemektedir.

The scientific map of music education research: A bibliometric analysis of the WoS literature (2000-2025)

Öz

This study aims to examine the intellectual structure and research trends of scientific production in the music education literature between 2000 and 2025 through a bibliometric perspective. A total of 8,851 articles retrieved from the Web of Science (Core Collection) database were analyzed using bibliometric analysis and scientific mapping methods via the R-Studio Bibliometrix software. Findings indicate that the literature has exhibited exponential growth over the past 25 years in compliance with Price’s Law, suggesting that music education has established itself as a mature discipline within the scientific literature. According to Bradford’s Law, 36.59% of the literature is concentrated in six core journals, including IJME and JRME. Lotka’s Law analysis confirms low author persistence and reveals that production is dominated by a narrow core group. Keyword analysis and thematic mapping demonstrate that concepts such as education, teacher, and music emerge as motor themes, indicating that music education is built upon solid pedagogical foundations. Furthermore, trend analysis reveals that music education is rapidly evolving from a traditional framework toward a process of digital transformation, centered on technology-oriented topics such as artificial intelligence, big data, and virtual reality. Interdisciplinary synergy within music education is intensifying as the field reaches methodological maturity and transforms into a globally institutionalized scientific discipline. Simultaneously, the conclusion emerges that it has achieved a dynamic framework characterized by its capacity for swift adaptation to both social crises and technological transformations.

Anahtar Kelimeler

• music education
• bibliometric analysis
• scientific mapping
• digital transformation
• Web of Science

Destekleyen Kurum

The authors declare that the study received no funding.

Etik Beyan

Ethical committee approval is not required for this research as it does not involve the use of human or animal subjects or sensitive data.

Kaynakça

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