Bilgi Üretiminin Nicelleştirilmesi: Türkiye Temel Bilimlerinde Tez Üretimine Yönelik Doğal Dil İşleme (NLP) Tabanlı Analizler ve Tahmin Modelleri

Yıl 2025, Cilt: 37 Sayı: 4, 287 - 300, 23.12.2025

Şeyma Bozkurt Uzan , Nesrin Manav , Şevval Arabacıoğlu

https://doi.org/10.7240/jeps.1673780

Öz

Günümüz dünyasında temel bilimler alanındaki araştırmalar büyük ölçüde akademik kurumlarda yürütülmektedir. Lisansüstü tezler bu sürecin temel taşlarından biri olup, bireylerin akademik yolculuğunun ilk adımlarını ve bilimsel bilgiye katkılarını temsil eder. Bu çalışma, 1960-2024 yılları arasında Türkiye’de temel bilimler alanında yazılmış lisansüstü tezlerin kapsamlı bir bibliyometrik analizini sunmaktadır. Araştırmada zaman içinde tez sayısındaki eğilimler, yıllara, tez türlerine ve üniversitelere göre dağılımları incelenmiş; ayrıca kelime bulutu aracılığıyla tekrar eden anahtar kelimeler ve araştırma temaları görselleştirilmiştir. Veriler, Ulusal Tez Merkezi’nden Instant Data Scraper programı kullanılarak toplanmış ve toplamda 68.883 tez analiz edilmiştir. Kullanılan istatistiksel yöntemler arasında betimsel istatistikler, zaman serisi tahmin modelleri ve doğal dil işleme (NLP) teknikleri yer almaktadır. Sonuçlar, yıllar içinde tez üretiminde istikrarlı bir artış olduğunu göstermekte; Gazi Üniversitesi ve Orta Doğu Teknik Üniversitesi’nin (ODTÜ) bu alandaki önemli katkılarını ortaya koymaktadır. Kimya ve biyoloji alanlarında güçlü bir faaliyet gözlenirken, matematik ve kuramsal bilimler nispeten düşük temsil edilmiştir. Tahmin modelleri, tez üretiminde artışın devam edeceğini, ancak bu artışın dışsal etkenlerden etkilenecek şekilde değişkenlik göstereceğini öngörmektedir. Bu bulgular, Türkiye’nin lisansüstü araştırma yapısına dair değerli içgörüler sunmakta ve gelecekteki akademik ve politik gelişmelere yön verebilecek niteliktedir.

Anahtar Kelimeler

Doğal Dil İşleme (NLP) , Zaman Serisi Tahmini , Lisansüstü Tezler , Bibliyometrik Analiz , Türkiye'de Temel Bilimler

Quantifying Knowledge Production: NLP-Driven Insights and Forecasting Models for Thesis Output in Turkey’s Basic Sciences

Öz

In the contemporary world, research in the field of basic sciences is predominantly conducted within academic institutions. Graduate theses play a pivotal role in this process, as they not only represent the initial steps on an individual's academic journey but also their contributions to scientific knowledge. This study presents a comprehensive bibliometric analysis of graduate theses written in Türkiye in the basic sciences from 1960 to 2024. The research aims to investigate the trends in the number of theses over time, their distribution by years, types, and universities, as well as visualizing recurring keywords and research themes through a word cloud. By using the Instant Data Scraper program to gather data from the National Thesis Center, a total of 68,883 theses were analyzed. The statistical methods employed include descriptive statistics, time-series forecasting, and natural language processing (NLP) techniques. The results indicate a steady increase in thesis production over the years, with significant contributions from Gazi University and Middle East Technical University (METU). While chemistry and biology show strong activity, mathematics and theoretical sciences are underrepresented. Forecasting models predict continued growth in thesis production, with variations reflecting external influences. These results provide valuable insights into Türkiye’s graduate research landscape and can guide future academic and policy development.

Anahtar Kelimeler

Natural Language Processing (NLP) , Time Series Forecasting , Graduate Theses , Bibliometric Analysis , Basic Sciences in Türkiye

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