Performance Evaluation for Educators in Higher Education from Bibliometric Analysis Views
Keywords:
performance evaluation; educator; bibliometric analysis; Higher Education Assessment; Sustainable Development Goals (SDGs)Abstract
Educator performance evaluation in higher education is an evolving process that is shaped by pedagogical advances and institutional needs. This study uses bibliometric analysis to explore research trends and emerging themes in educator performance evaluation. Using Scopus data, a comprehensive analysis of 1,173 publications from 2000 to 2024 was conducted, examining the evolution from traditional student evaluation to competency-based frameworks that integrate artificial intelligence and data-driven methodologies. Key findings indicate a shift toward technology-enhanced evaluation, integrating AI-driven analytics and real-time feedback. The study highlights key contributors, including leading countries, institutions and authors, providing insights into global research collaborations. The analysis identified five dominant research clusters: technology-driven evaluation systems, competency-based faculty evaluation, policy frameworks, faculty evaluation in medical education, and post-pandemic teaching evaluation. The increasing use of machine learning, data mining and performance indicators reflects a shift toward structured and objective evaluation methods. However, challenges such as implicit bias, inconsistent rubrics and an overreliance on quantitative metrics persist. Findings from this study offer valuable insights for policymakers, administrators, and educators seeking to refine evaluation frameworks in alignment with emerging trends. The research underlines the necessity for a hybrid evaluation method that balances quantitative performance measurements with qualitative evaluation strategies, ensuring a thorough understanding of teaching efficacy. For higher education evaluation systems to be more equitable, dependable, and inclusive, future studies should investigate different approaches, interdisciplinary evaluation frameworks, and the moral ramifications of AI-driven performance evaluations.
https://doi.org/10.26803/ijlter.24.5.15
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