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Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Scientometric Exploration of Rhizomatic Learning in Theory and Practice

Exploración cienciométrica del aprendizaje rizomático en teoría y práctica
Authors: Castro-Rojas, Yenny Lisbeth; Pérez, Johan Hernán; Pedraza-Jiménez, Yamile;

Scientometric Exploration of Rhizomatic Learning in Theory and Practice

Abstract

RESUMENIntroducción: El aprendizaje rizomático (RL) es un enfoque educativo innovador y no lineal, basado en la teoría del rizoma de Gilman (1989). A diferencia de los modelos tradicionales, el RL es descentralizado y permite conexiones multidireccionales, lo que favorece la interconectividad y la adaptabilidad en la construcción del conocimiento. Aunque su potencial ha sido ampliamente reconocido, el RL aún representa un campo emergente que requiere mayor respaldo científico desde diversas perspectivas. Este artículo tiene como objetivo realizar un análisis cienciométrico para mapear la producción científica relacionada con el RL e identificar las tendencias predominantes en el área. Metodología: Para ello, se utilizaron datos bibliométricos provenientes de Web of Science (WoS) y Scopus, organizando el estudio en dos partes: un mapeo general de la producción y la aplicación del algoritmo Tree of Science (ToS). Resultados: Los resultados muestran un crecimiento constante del interés en RL, con una tasa de incremento del 21.65% desde 2024, destacando Estados Unidos y Reino Unido como los países con mayor número de publicaciones. Discusión: Se identificaron tres tendencias principales: la integración del aprendizaje en las artes y el patrimonio cultural, estrategias pedagógicas innovadoras en el aula, y la exploración de experiencias culturales en la educación inicial. Conclusión: Este trabajo contribuye a la conceptualización y visibilidad del RL como motor de innovación educativa.

ABSTRACTIntroduction: Rhizomatic Learning (RL) is an innovative and non-linear educational approach, based on the rhizome theory proposed by Gilman (1989). Unlike traditional teaching models, RL is decentralized and allows for multidirectional connections, promoting interconnectivity and adaptability in the construction of knowledge. Although its potential has been widely acknowledged, RL remains an emerging field that requires further scientific support from various perspectives. This article aims to conduct a scientometric analysis to map the scientific production related to RL and identify prevailing trends in the field. Methodology: To achieve this, bibliometric data from Web of Science (WoS) and Scopus were used, structuring the study into two parts: a general mapping of the scientific output and the application of the Tree of Science (ToS) algorithm. Results: Results show a steady growth in interest in RL, with a 21.65% increase in production since 2024, with the United States and the United Kingdom leading in the number of publications. Discussions: Three main trends were identified: the integration of learning in the arts and cultural heritage, innovative pedagogical strategies in the classroom, and the exploration of cultural experiences in early childhood education. Conclusion: This study contributes to the conceptualization and visibility of RL as a driver of educational innovation.

Keywords

Pedagogical Practices, Educational Innovation, Rhizomatic Learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green