Data-driven approach unveils key trends in research talent evaluation at Chinese universities
Higher Education Press
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Researchers from Beihang University have conducted a comprehensive bibliometric analysis to identify evolving trends and challenges in evaluating research talent at Chinese universities. Their study, published in Frontiers of Computer Science on 15 December 2025, reveals a significant shift from theoretical policy discussions to practical, multidimensional evaluation frameworks, driven by China’s “Double World-Class” initiative. These findings provide critical insights for universities aiming to modernize talent assessment systems and reduce overreliance on traditional metrics like paper publications.
Current evaluation systems often struggle with outdated criteria, poor expert selection, and inconsistent standards. By applying data mining techniques—such as co-occurrence analysis and clustering—to 1,696 academic articles (2014–2024), the team uncovered five key research clusters, including “Educational Evaluation and Reform” and “Talent Cultivation.” Post-2020, research has increasingly focused on integrating industry-education collaboration and aligning career development with institutional goals, reflecting national efforts to build world-class universities. This data-driven approach addresses gaps in traditional methods, offering actionable strategies to enhance fairness, innovation, and societal impact in talent evaluation.
The team utilized advanced tools like Bicomb and CiteSpace to analyze keyword co-occurrence and cluster patterns in Chinese core journals. By merging synonyms and filtering high-frequency terms, they mapped the evolution of research priorities, validated by strong intra-cluster similarity metrics (ISim ≈ 0.07). Visualizations, including co-occurrence matrices and strategic coordinate diagrams, highlight the growing emphasis on interdisciplinary education and teacher development.
The study urges universities to adopt dynamic evaluation systems that reflect diverse contributions, from academic achievements to societal impact. As China accelerates its “Double World-Class” project, these insights could reshape global higher education practices, fostering innovation and equitable talent recognition.
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Credit
HIGHER EDUCATON PRESS
Journal
Frontiers of Computer Science
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Bibliometric analysis of research talent evaluation in Chinese universities: data mining approach
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