A meta-analysis of the postgraduate thesis in the field of life science class
2019
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Danışman: Doç. Dr. Menderes Ünal
Özet (EN)
This study aims to investigate the distribution of the master's and doctoral theses about Life Science course according to the graduate level, the university, the department and the year they are prepared, the selected keywords, the research method used, data collection and data analysis techniques. In this study, post-graduate theses which contain the word of "Life Science" and conducted between 1988 and 2018 in the database of National Thesis Center of Higher Education (YOK) constituted the data source. In the study, meta-analysis method was used to combine independent studies and the data of the study to make more general interpretations. According to the findings of the study, most of the graduate theses examined were mostly at the graduate level, conducted at Gazi University, made in the field of primary school department, mostly used "Life Science" category as a keyword, and mostly preferred scanning model as data collection methods and techniques. In addition, questionnaire was the mostly utilized data collection tool and t-test was used more than any other data analysis techniques.
Yazar
Fadime Dağıstan
Bu Yayına Nasıl Atıf Yapılır
Fadime Dağıstan (Master Thesis). A meta-analysis of the postgraduate thesis in the field of life science class, 2019, Kırşehir Ahi Evran University.
Anahtar Kelimeler
Lisans
Tüm Hakları Saklıdır
Bu eser belirtilen lisans koşulları altında paylaşılmaktadır.
Kırşehir Ahi Evran University tezlerinden daha fazlası
- In teaching of mathematical concept, the effect of storyline method on attitude and success(2013)
- Jean-Jacques Rousseau's thoughts on life science and education in his Work "emile"(2019)
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- H. 1301 (M. 1884) in light of the yearbook dated hejaz province during the reign of Abdulhamid II (History and geography, social-cultural, economic, administrative-military structure)(2020)
- Proteinurin in diabetic patients effect on mortality(2022)
- Wind energy forecasting methods: A case study of the long short term memory model (LSTM)(2024)
