Artículo

Analyzing the evolution of machine learning integration in educational research: a bibliometric perspective

Resumen

Machine learning, a subset of artificial intelligence, has experienced rapid advancements and applications across various domains. In education, its integration holds great potential to revolutionize teaching, learning, and educational outcomes. Despite the growing interest, there needs to be more comprehensive bibliometric analyses that track the trajectory of machine learning’s integration into educational research. This study addresses this gap by providing a nuanced perspective derived from bibliometric insights. Using a dataset from 1986 to 2022, consisting of 449 documents from 145 sources retrieved from the Web of Science (WoS), the research employs network analysis to unveil collaborative clusters and identify influential authors. A temporal analysis of annual research output sheds light on evolving trends, while a thematic content analysis explores prevalent research themes through keyword frequency. The findings reveal that co-authorship network analysis exposes distinct clusters and influential figures shaping the landscape of machine learning in educational research. Scientific production over time reveals a significant surge in research output, indicating the field’s maturation. The co-occurrence analysis emphasizes a collective focus on student-centric outcomes and technology integration, with terms like “online” and “analytics” prevailing. This study provides a nuanced understanding of the collaborative and thematic fabric characterizing machine learning in educational research. The implications derived from the findings guide strategic collaborations, emphasizing the importance of cross-disciplinary engagement. Recommendations include investing in technological infrastructure and prioritizing student-centric research. The study contributes foundational insights to inform future endeavors in this ever-evolving field.
Bao, Haoran (59488973200); Nikolaeva, Anna (59489398100); Xia, Jun (59148841500); Ma, Feng (59489188000)
Evolution Trends and Future Prospects in Artificial Marine Reef Research: A 28-Year Bibliometric Analysis
2025
10.3390/su17010184
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214475018&doi=10.3390%2fsu17010184&partnerID=40&md5=de023fd8f5c61e9bce1810c36393d7db
Department of Marine and Fisheries Business Administration, Pukyong National University, Busan, 48513, South Korea; School of Economics and Management, Nanjing Forestry University, Nanjing, 210037, China; Department of Marine Design Convergence Engineering, Pukyong National University, Busan, 48513, South Korea; Department of Environmental Design, Dongseo University, Busan, 47011, South Korea; College of Network Communication, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, 312000, China
All Open Access; Gold Open Access
Scopus
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