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Dergiler / Alphanumeric Journal / 2026 / Volume: 14 Issue: 1

A comparative machine learning analysis for forecasting university satisfaction scores in Türkiye using TUMA, YÖK, and URAP data (2016–2025)

Hüseyin Başaran(GAZİANTEP ÜNİVERSİTESİ)0009-0005-3641-4760
Yunus Eroğlu(GAZİANTEP ÜNİVERSİTESİ)0000-0002-8354-6783
Suleyman Mete(GAZİANTEP ÜNİVERSİTESİ)0000-0001-7631-5584
Sayfa
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Yayın Tarihi
2026-07-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Thus, it was concluded that institutions need to use different models for their satisfaction score predictions, depending on the level of satisfaction and the type of university.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Assessing university satisfaction levels is crucial for achieving expected performance from universities.
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Temel Bulgular & Veri:In the application stage, Turkiye University Satisfaction Survey (TUMA), Council of Higher Education (YÖK) Statistics, and University Ranking by Academic Performance (URAP) data were employed.
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Sonuç & Katkı:Thus, it was concluded that institutions need to use different models for their satisfaction score predictions, depending on the level of satisfaction and the type of university.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2016-2025 Verileri
⚙️Değişkenler:Forecasting · Machine Learning · University Satisfaction

Abstract

Assessing university satisfaction levels is crucial for achieving expected performance from universities. In this regard, this study investigates the forecasting of satisfaction scores at universities in Türkiye. In the application stage, Turkiye University Satisfaction Survey (TUMA), Council of Higher Education (YÖK) Statistics, and University Ranking by Academic Performance (URAP) data were employed. This data covers the period between 2016-2025. Linear Regression, Random Forest, Gradient Boosting, Tree, Neural Network, and Support Vector Machine (SVM) machine learning algorithms were applied to predict satisfaction scores. The trials for each model were carried out using Orange version 3.39 on samples taken according to different satisfaction levels and university types. After that, the model performances were compared and the best trial results show that model performances differ according to institution and satisfaction level type. Thus, it was concluded that institutions need to use different models for their satisfaction score predictions, depending on the level of satisfaction and the type of university.

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Bu Makaleye Atıf Yap

Başaran, H., Eroğlu, Y., Mete, S. (2026). A comparative machine learning analysis for forecasting university satisfaction scores in Türkiye using TUMA, YÖK, and URAP data (2016–2025). Alphanumeric Journal, Volume: 14 Issue: 1. https://doi.org/10.17093/alphanumeric.1886703

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