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August 9, 2026InformationOpen Access

Machine Learning Framework for Cross-Ranking Analysis and Estimation of University Positions in Global Rankings

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Authors

NKNursultan KuldeyevEAEmil AndekinVSVassiliy Serbin

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Overview

Randomized trial evaluates university rankings, revealing complementary assessments among QS, THE, and ARWU.

Key Points

  • The aim is to develop a methodology for comparing university rankings across different global ranking systems.
  • Data harmonization for university names, countries, years, and rankings for 2022–2025.
  • Model performance evaluated using leave-one-year-out cross-validation with linear regression, Random Forest, and XGBoost.
  • Ranking agreement assessed using Spearman distance, overlap of ranked lists, and normalized inverse-rank metric.
  • Random Forest achieved the lowest mean absolute error (MAE = 24.80 ± 3.26 ranking positions).
  • Linear regression preserved relative ordering best, with Spearman’s ρ = 0.794 ± 0.062.
  • Prediction accuracy decreases with ranking depth, noted in analyses of TOP-50, TOP-100, and TOP-200 subsets.

Cite This Study

Kuldeyev et al. (2026) studied this question.

synapsesocial.com/papers/6a782d7b2e1896536c840a19https://doi.org/10.3390/info17080758
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