Matematik ve Fen Bilimleri Eğitimi Bölümü Koleksiyonu

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https://hdl.handle.net/11494/1789

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Estimation of radiation dose and lifetime risk from gross alpha/beta radioactivity in drinking water in Şırnak city, Türkiye
(SİVAS CUMHURİYET ÜNİVERSİTESİ, 2025) Damla, Nevzat; Yeşilkanat, Cafer Mert; Taşkın, Halim; Işık, Ümit; Tekin, Caner
The research aimed to evaluate the radiological quality of drinking water in Şırnak city, Türkiye. A gas proportional α/β counter (Berthold, LB 770 model) was employed to quantify the gross α and β activities in a total of seven drinking water samples. The findings revealed that the average gross α and β activities were 27 mBq/L (range: 19-35 mBq/L) and 117 mBq/L (range: 24-534 mBq/L), respectively. The age-dependent annual effective dose (AED) resulting from the ingestion of drinking water in Şırnak was meticulously estimated. The calculated average total AED values (alpha+beta) were 9.5 μSv/y for infants, 22.1 μSv/y for children, and 46.2 μSv/y for adults. The average lifetime risk of radiation-induced cancer (LTR) for the population was found to be 1.7×10⁻⁴. Overall, the study concluded that the radiological parameters in Şırnak city's drinking water were within the permissible limits recommended by the World Health Organization (WHO). These findings provide significant assurance that the drinking water in the region is safe for consumption, concerning radiation exposure. Furthermore, the results imply that existing water treatment and monitoring systems are effective in maintaining radiation levels within safe limits. The study highlights the importance of regular testing and evaluation of water quality to protect public health.
Accurate prediction of gamow-teller beta-decay matrix elements via machine learning: Implications for nuclear structure
(ELSEVIER, 2025) Yeşilkanat, Cafer Mert; Akkoyun, Serkan
Accurate prediction of Gamow-Teller (GT) beta decay matrix elements [M(GT)] is essential for elucidating complex nuclear structure phenomena and understanding astrophysical processes. In this study, we employed five advanced machine learning models (Cubist, Support Vector Regression, Extreme Gradient Boosting, Random Forest, and Bayesian Regularized Neural Networks) to predict GT beta decay matrix elements in sd-shell nuclei, using experimental data from NNDC/ENSDF, NUBASE2016, and AME2016. This study systematically compared the predictive performance of traditional theoretical approaches (including the USDB, IM-SRG, CCEI, and CEFT) to that of advanced machine learning models trained based on experimental observations. Our primary objective was to determine whether data-driven models could achieve higher predictive accuracy than computationally expensive theoretical models by learning the complex and nonlinear relationships among experimental parameters that reflect nuclear structure and decay dynamics. The results demonstrate that the Cubist model achieves a significantly lower RMSE (0.073 in the full parameter modeling approach and 0.112 in the reduced parameter modeling approach) and high coefficients of determination (R2 = 0.901 and 0.919, respectively), thereby outperforming traditional methods. Furthermore, SHapley Additive exPlanations (SHAP) analysis revealed that a minimal set of critical nuclear parameters predominantly governs GT decay dynamics, thereby enhancing model interpretability without compromising predictive accuracy. Complementing these findings, an online calculator was developed to facilitate rapid, highfidelity predictions of GT matrix elements. Overall, our study demonstrates that a data-driven approach outperforms established theoretical models. More importantly, by identifying the minimal set of physical observables that govern GT transitions, our work provides crucial insights into the underlying physics of nuclear structure and offers a new benchmark for refining future theoretical models and astrophysical calculations.
Interpretable machine learning for rapid prediction and calibration of HPGe detector efficiency: A physics-informed approach and online platform
(Springer Nature, 2025) Yeşilkanat, Cafer Mert; Çelik, Necati; Celik, Ahmet; Çevik, Uğur
This study introduces a novel, physics-informed, and calibration-friendly hybrid machine learning framework for the rapid and accurate prediction of the Full Energy Peak (FEP) efficiency in High-Purity Germanium (HPGe) detectors. To overcome the limitations of conventional “black-box” models, our two-stage approach first represents the FEP efficiency curve using a physically interpretable logarithmic polynomial. Subsequently, machine learning models were trained to predict the polynomial coefficients directly from the detector geometric parameters using a comprehensive dataset generated via Monte Carlo simulations. Among the various algorithms tested, the Generalized Linear Model yielded superior performance, achieving R2 values of 0.975–0.992 for the coefficients. While raw model predictions showed expected variability, a key feature of our framework (a single-point calibration protocol using one known efficiency value) dramatically improved accuracy, reducing the mean absolute percentage error by an average of 80% on the independent test data. The calibrated model was further validated using an experimental detector. The entire framework was deployed as a user-friendly online platform, enabling researchers to instantly generate and calibrate efficiency curves. Our work successfully harmonizes interpretability, speed, and accuracy, offering a powerful tool for the design, optimization, and routine calibration of HPGe detectors.
Future teachers’ views on their science teachers: A case of Turkey
(Peace and Conflict Studies, 2025) Karakaş, Mehmet
Here future elementary and middle school teachers reflect on their experiences with science teachers encountered throughout their education and highlight their characteristics. The aim of the study was to create a program where teachers were given the explicit opportunity to reflect on their experiences through journaling that promoted reflective thought. Participants were 173 non-science and 120 science major prospective teachers enrolled in a small university in northeastern Turkey. Data were collected using document analysis. Findings show that effective science teachers do experiments, use teaching materials, and show special interest in students; also, their positive personality affected students’ positive attitude towards science. Ineffective teachers were “boring” and using lecturing and didn’t take their students to laboratories, neither had they done some activity or use visual materials. Furthermore, ineffective science teachers were “angry, fear imposing, authoritarian, and discriminating between students.”
Micro/nanomechanical properties of top-seeded melt grown YBCO single crystals determined using depth sensing indentation
(Elsevier Ltd, 2025) Kölemen, Uğur; Yeşilkanat, Cafer Mert; Yılmaz, Fikret; Doğan, Fatih; Uzun, Orhan
Mechanical properties of the bulk YBa2Cu3O7-x (YBCO) single crystal superconductor, prepared by using the Top Seeded Melt Growth (TSMG) method, were determined. Property measurements were conducted by the nanoindentation method under various loads and the microindentation method at different temperatures. Hardness (H) and reduced elastic modulus (Er) values were calculated by using the Oliver–Pharr method. According to hf/hmaxvalues found from load- displacement curves, there is a sink in behavior around outer rim of the indents that was confirmed by atomic force microscopy (AFM) analysis. Nanoindentation analyses conducted on upper section, middle section and subsection revealed that all three regions exhibited similar mechanical characteristics. As a result of the microhardness tests, the thermal activation energy of the samples was calculated as 0.0796 eV. It is expected that the results of the mechanical properties obtained in the study will be useful for practical applications of TSMG YBCO superconductors such as flux-trapped magnets exposed to various mechanical stresses.
Sentiments, attitudes, and concerns about inclusion: Early years in teacher education programs
(Erzincan Üniversitesi, 2018) Cansız, Mustafa; Cansız, Nurcan
This study aims to examine preservice teachers' sentiments toward students with special needs,attitudes and concerns about inclusive education in terms of a number of demographic variables.These demographics included major area, grade level, gender, interaction with disabled people,training for inclusive education, self-confidence for teaching in inclusive classrooms, and teachingexperience in inclusive classrooms. Main data were collected only from first and second-yearpreservice teachers. The result indicated that although some findings matched those observed inearlier studies, others did not support the previous research. The possible reasons were discussedwith reference to teacher education programs implemented in Turkey.
The validity and reliability study of Turkish version of the sentiments, attitudes, and concerns about inclusive education scale
(KASTAMONU EĞİTİM FAKÜLTESİ, 2018) Cansız, Nurcan; Cansız, Mustafa
Bu çalışmanın amacı, Loreman, Earle, Sharma ve Forlin’in (2007) geliştirdikleri Sentiments, Attitudes, and Concerns about Inclusive Education (SACIE) ölçeğini Türkçeye uyarlamaktır. Türkçe formun yapı geçerliğini incelemek amacıyla 304 öğretmen adayından toplanan veriye Açımlayıcı Faktör Analizi uygulanmıştır. Sonrasında ölçek, 368 öğretmen adayına uygulanmış ve elde edilen veriye Doğrulayıcı Faktör Analizi yapılmıştır. Açımlayıcı faktör analizi ölçeğin Düşünce, Tutum ve Endişe olarak üç faktörlü yapısını ortaya koymuştur. Doğrulayıcı faktör analizi de toplamda 19 maddeden oluşan üç faktörlü ölçek yapısını desteklemiştir. Diğer taraftan ölçek, öğretmen adaylarının özyeterliklerini ortaya koymada makul bir yordama geçerliği göstermiştir. Ölçeğin Türkçeye uyarlanmasının önemi tartışılmıştır.
Enhancing preservice teachers’ observation and inference skills
(İnönü Üniversitesi, 2018) Cansız, Nurcan; Cansız, Mustafa
In this study, we aimed to investigate the change in third-grade preservice elementary teachers’ observation and inference skills. We also aimed to develop their ability to distinguish observation from inference. A total of 27 preservice elementary teachers participated in the study. Participants’ preinstruction and postinstruction observation and inference skills were explored through written statements about three different drawings. An instruction on science process skills within Science Teaching course was provided to the preservice elementary teachers. Analysis of their pre and postinstruction observation and inference statements showed that, at the beginning, preservice elementary teachers were not adequate in observation and mostly confused observation with inference. After participating in classroom discussions and activities, they improved in making observation and inference. They showed better enhancement in making observation than drawing inference. Implications were suggested in terms of elementary teacher education programs and further research.
Azometin yilür çekirdeği içeren yeni kiral bileşiklerin sentezi ve yapılarının aydınlatılması
(Cumhuriyet Üniversitesi, 2017) Gümüş, Mustafa Kemal
Bu çalışmada 14 adet kiral yapıda yeni dialkil (2-okso-2-{[(1S)-1-feniletil]amino}etil) ditiyoimidokarbonat ve (2-okso-2-{[(1R)-1-feniletil]amino}etil)ditiyoimidokarbonat bileşiği (4a-n) glisin tuzlarından (1) başlanılarak sentezlenmiştir. Bu bileşikler 1,3-dipolar siklokatılma tepkimelerinde 1,3-dipol reaktifi olarak kullanılan azometin yilür çekirdeği içermektedir. Ayrıca sentezlenen azometin yilür bileşikleri substituent olarak kiral bir amit fonksiyonu da içermektedir. Bu bileşiklerin yapıları farklı spektroskopik yöntemler (IR, 1H NMR, 13C NMR ve COSY NMR) kullanılarak karakterize edilmiştir.
Görme yetersizliği olan öğrencilerin öğrenmelerini destekleyici ihtiyaçlar
(Trakya Üniversitesi, 2017) Zorluoğlu, Seraceddin Levent; Sözbilir, Mustafa
Bu çalışmada görme yetersizliği olan öğrencilerin derse yönelik öğrenmelerini daha etkili hale getirmek amacıyla öğrenmeyi destekleyici ihtiyaçlar belirlenmiştir. Görme yetersizliği olan öğrencilerin öğrenmeye yönelik ihtiyaçlarının belirlenmesinde iç içe geçmiş tek durum deseni kullanılmıştır. Araştırmanın çalışma grubunu Erzurum İli Görme Engelliler Ortaokulu'nda öğrenim gören 6. sınıf görme yetersizliği olan öğrenciler oluşturmaktadır. Görme yetersizliği olan öğrencilerin eğitim ortamına, öğretime, öğrenime ve ölçme-değerlendirmeye yönelik ihtiyaçlar ders sırasında yapılan gözlemler, ders sonrası video kayıtların incelenmesi ve ünite sonunda yapılan görüşmeler sonucu belirlenmiştir. Verilerin detaylı incelenmesi sonucunda içerik analizine tabi tutulmuş ve ihtiyaç analizi belirlenmiştir. Elde edilen bulgulara göre görme yetersizliği olan öğrencilerin öğrenmelerini destekleyici ihtiyaçlar "eğitim-öğretim ortamı ihtiyaçları", "eğitim-öğretim ihtiyaçları" ve "değerlendirmeye yönelik ihtiyaçlar" olarak belirlenmiştir.
Logo programlama sürecinde matematik öğretmen adaylarının yaptıkları hatalar üzerine bir nitel çalışma
(Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 2017) Kul, Ümit; Birişçi, Salih
Bu çalışmada, ilköğretim matematik öğretmeni adaylarının Logo yazılımı kullanarak açı ve dönme kavramlarını içeren problemlerin çözümüne yönelik yaptıkları hataları ve olası yanılgıları tespit etmek amaçlanmıştır. Bu doğrultuda, 37 ilköğretim matematik öğretmeni adayına 10 saatlik Logo programlama dili eğitimi verilmiştir. Daha sonra adaylara Logo komutlarını kullanarak açı ve dönme kavramıyla ilgili performans gösterebilecekleri 8 sorudan oluşan açık uçlu bir sınav yapılmıştır. Bu çalışmada 8 soru içinden seçilen 5 soru üzerinde durulmaktadır. Çalışmanın verileri, nitel veri toplama yöntemlerinden doküman analizi ve klinik mülakat ile elde edilmiştir. Elde edilen bulgular neticesinde, öğretmen adaylarının kâğıt-kalem ortamında problemleri çözerken Logo programlama komutlarından daha çok geometrik kavramlara dayalı hatalar yaptıkları tespit edilmiştir. Bu hatalar şu üç noktada yoğunlaşmaktadır: dönme açısı, açı-kenar bağıntısı ve eksik kodlama. Logo’ da performans gösterebilecekleri problemleri çözerken bazı öğretmen adaylarının programlama becerilerinin sınırlı; diğerlerinin kabul edilebilir seviyede olduğu belirlenmiştir. Araştırma sonucunda, belirli geometrik kavramların öğretiminde Logo programlama dilinin sınırlı geri bildirim veren yapılandırmacı yaklaşımla uygulanması gerektiği belirlenmiştir.
Sınıf öğretmen adaylarının kesirler konusundaki pedagojik alan bilgileri
(Kastamonu Üniversitesi, 2015) Aksu, Zeki; Konyalıoğlu, Alper Cihan
Bu çalışmanın amacı sınıf öğretmen adaylarının kesirlerle işlemler konusundaki pedagojik alan bilgilerini Shulman (1986) tarafından ortaya konulan pedagojik alan bilgisi ve pedagojik alan bilgisi bileşenleri bağlamında araştırmaktır. Çalışmanın katılımcılarını, 2011-2012 eğitimöğretim yılı güz döneminde son sınıfta öğrenim gören 9 sınıf öğretmeni adayı oluşturmaktadır. Çalışmada nitel yaklaşım kullanılarak veriler açık uçlu sorular ve görüşmeler yardımıyla toplanmıştır. Öğretmen adaylarının, “öğrenciyi anlama” ve “gösterim temsilleri ve yöntemi” bilgisi bakımdan yeterli olmadıkları tespit edilmiştir. Özellikle, gösterim temsilleri ve model kullanımı konusunda büyük eksiklikler olduğu görülmüştür.
Examination of problems in middle school mathematics textbooks in relation to the PISA mathematical literacy framework
(Kafkas Üniversitesi, Eğitim Fakültesi, 2025) Çelik Demirci, Sedef; Kul, Ümit; Korkmaz, Samet
This research examines the extent to which mathematics problems in middle school textbooks for grades 5 through 8 align with the PISA mathematical literacy framework. The analysis emphasizes key dimensions, including content, context, cognitive processes, proficiency levels, and problem types. Utilizing document and descriptive analyses, the research evaluates textbooks published by Türkiye’s Ministry of National Education for the 2023–2024 academic year. Results reveal significant imbalances in the distribution of content areas, with “change and relationships” dominating, while “quantity” is underrepresented. Contextual analysis shows a predominance of “personal” contexts, with limited occupational, social, and scientific scenarios, which restrict students’ engagement with real-world applications. Regarding mathematical processes, the emphasis is on procedural tasks, while higher-order cognitive skills are insufficiently represented. Furthermore, the majority of problems correspond to PISA proficiency levels 2 and 3, with minimal representation of levels 5 and 6, highlighting a scarcity of tasks designed to foster advanced mathematical competencies. These findings underscore the necessity for a more equitable integration of content areas, a broader spectrum of real-life contexts, and tasks targeting higher proficiency levels. Recommendations propose a redesign of textbooks to incorporate a wider range of cognitive demands and contextual scenarios, with the aim of enhancing students’ mathematical literacy and preparedness for international assessments such as PISA.
Mapping the critical current density distribution in bulk YBCO superconductors using sequential Gaussian simulation
(Elsevier Ltd, 2025) Çakır, Bakiye; Yeşi̇lkanat, Cafer Mert; Duman, Şeyda; Aydıner, Alev
Bulk Y123 samples with different compositions were produced by both the Top-Seeded Melt-Growth (TSMG), with a NdBCO/YBCO/MgO film (300 nm NdBCO with 20 nm YBCO buffer layer on MgO substrate) seed, and Melt-Powder-Melting-Growth (MPMG) methods. Each sample was cut into 21 small specimens after the annealing process. Magnetization measurements of the 15 specimens selected from the symmetrical regions of each cutting sample were performed. All specimens' superconducting transition temperature (Tc) values were determined to be around 92 K from the magnetization-temperature graphs. Based on the extended Bean model, the specimens' critical current densities (Jc) were calculated from the magnetization hysteresis loops. Intermediate value estimation was made with Sequential Gaussian Simulation (SGS) for the unmeasured samples using the Jc values of the measured samples. The Jc distribution maps were obtained throughout each sample for different applied magnetic fields, and the results were compared.
Spatial distribution modeling of radiometric analysis and radiation dose estimations in drinking water and soil samples from Siirt city in Türkiye
(Taylor and Francis Ltd., 2023) Damla, Nevzat; Altun, Ahmet; Yeşilkanat, Cafer Mert; Taşkın, Halim; Kara, Ayhan; Işık, Ümit
In this study, the spatial distribution model has been aimed to characterize the radiometric parameters in drinking water and soil samples of Siirt City in Türkiye using the geostatistical method. The gross α and β measurements in the water samples and radiometric measurements in the soil samples were performed using a gas proportional α/β counter (Berthold, LB 770 model) and a gamma spectroscopy system (HPGe-detector), respectively. The spatial distribution maps, covering the whole region using the ordinary kriging method, were created visually. The gross α and β activities in the water samples varied from 9 to 40 and from 21 to 252 mBq L−1. The corresponding arithmetic average of the annual effective dose of gross α and β of drinking water was estimated to be 5.3 and 52.3 μSv y−1, respectively. In soil samples, the arithmetic average values of 238U, 232Th, 40K, and 137Cs radionuclides were 17 ± 6, 20 ± 7, 445 ± 166, and 4 ± 6 Bq kg−1, respectively. The arithmetic average absorbed dose rate (D), annual effective dose (AED), and excess lifetime cancer risk (ELCR) values in soil samples were calculated as 38 nGy h−1, 0.05 mSv y−1, and 0.165 × 10−3, respectively, for soil samples. The radiometric parameters of the sample were lower than those of the guideline levels recommended in the literature. Furthermore, the interpolation maps were evaluated in terms of the soil structure of the region.
Implementing digital storytelling in statistics classrooms: Influences on aggregate reasoning
(Elsevier Ltd, 2023) Batur Öztürk, Aslıhan; Çakıroğlu, Ünal
This paper reports on a study aiming at examining the effect of the digital storytelling approach on the aggregate reasoning of high school students. A pretest-posttest quasi-experimental design was implemented on 50 10th-grade students for five weeks to reveal the effectiveness of digital storytelling in mathematics courses regarding aggregate reasoning. Results of statistical analysis showed a significant improvement in aggregate reasoning in favor of the students in the digital storytelling group. It was also noted that aggregate reasoning was specifically enhanced to analyze data and interpret results rather than the other two components (formulate question and collect data) in the experimental group. The results support the idea that the use of digital storytelling can be an effective instructional tool for statistics education and provide implications for course designers to provide better teaching of aggregate reasoning.
Characterizing mathematical discourse according to teacher and student interactions: The core of mathematical discourse
(Duzce University, Faculty of Education, 2023) Çelik Demirci, Sedef; Baki, Adnan
The discussion on the development of mathematical discourse plays a key role in the determination of the in-classroom interactions in mathematics learning and instruction. The present study aims to present a theoretical framework for the nature of mathematical discourse that addresses the teacher and student interaction in the classroom. Previous studies attempted to discuss the theoretical structure in mathematical discourse with the embedded theory approach. The findings revealed the core of mathematical discourse that reflected the structure of mathematical discourse based on open, axial and selective codes determined based on the embedded theory approach. The external structure of this core reflects the types of in-classroom interaction, while the internal structure reflects the development of the mathematical discourse. The external structure included four types of interaction: teacher, teacher-class, teacher-student, and student-student. The internal structure includes mathematical discourse movements associated with three stages: motivation, explanation of mathematical ideas, and achievement of mathematical ideas. The external structure of mathematical discourse core revealed the general state of in-classroom interaction core, and the internal structure revealed the specific mathematical discourse based on the mathematical content. It could be suggested that the discourse movements in the mathematical discourse core determined in the present study would provide guidelines for mathematical communications. The study also includes recommendations for future studies on the employment of this general and specific theoretical mathematical discourse framework.
Predicting science achievement scores with machine learning algorithms: a case study of OECD PISA 2015–2018 data
(Springer Science and Business Media Deutschland GmbH, 2023) Açışlı Çelik, Sibel; Yeşilkanat, Cafer Mert
In this study, the performance of machine learning methods was examined in terms of predicting the science education achievement scores of the students who took the exam for the next term, PISA 2018, and the science average scores of the countries, using PISA 2015 data. The research sample consists of a total of 67,329 students who took the PISA 2015 exam from 13 randomly selected countries (Brazil, Chinese Taipei, Dominican Republic, Estonia, Finland, Hungary, Italy, Japan, Lithuania, Luxembourg, Peru, Singapore, Türkiye). In this study, multiple linear regression, support vector regression, random forest, and extreme gradient boosting (XGBoost) machine learning algorithms were used. For the machine learning process, a randomly determined part from the PISA-2015 data of each country researched was divided as training data and the remaining part as testing data to evaluate model performance. As a result of the research, it was determined that the XGBoost algorithm showed the best performance in estimating both PISA-2015 test data and PISA-2018 science academic achievement scores in all researched countries. Furthermore, it was determined that the highest PISA-2018 science achievement scores of the students who participated in the exam, estimated by this algorithm, were in Luxembourg (r = 0.600, RMSE = 75.06, MAE = 59.97), while the lowest were in Finland (r = 0.467, RMSE = 79.38, MAE = 63.24). In addition, the average PISA-2018 science scores of the countries were estimated with the XGBoost algorithm, and the average science scores calculated for all the countries studied were estimated with very high accuracy.
Applications of different machine learning methods on nuclear charge radius estimations
(Institute of Physics, 2023) Bayram, Tuncay; Yeşilkanat, Cafer Mert; Akkoyun, Serkan
Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, machine learning (ML) can be considered as a different approach. In this study, ML techniques were performed using the experimental charge radius of 933 atomic nuclei (A ≥ 40 and Z ≥ 20) available in the literature. In the calculations in which eight different approaches were discussed, the obtained outcomes were compared with the experimental data, and the success of each ML approach in estimating the charge radius was revealed. As a result of the study, it was seen that the Cubist model approach was more successful than the others. It has also been observed that ML methods do not miss the different behavior in the magic numbers region.
Predictors of technology integration self-efficacy beliefs of preservice teachers
(Anadolu University, Faculty of Communication Sciences, 2019) Birişçi, Salih; Kul, Ümit
This correlational study aimed to investigate the prediction levels of technopedagogical education competency for technology integration self-efficacy beliefs of pre-service teachers. The study group comprised 174 pre-service teachers at the Faculty of Education of a university located in the Eastern Black Sea region of Turkey. Both “Technopedagogical Education Competency Scale” and “Technology Integration Self-Efficacy Perception Scale” were administered as data collection tools. The results of the study showed that pre-service teachers had high levels of technology integration self-efficacy beliefs, with a high-level positive correlation with technopedagogical education competency. In addition, the dimensions of technopedagogical education competency such as ethics, design, exertion and proficiency were revealed as the predictors of technology integration self-efficacy; moreover, predictive effects of exertion and proficiency dimensions are insignificant. The findings obtained from the present study are thought to be helpful for the development of pre-service teachers' technology integration self-efficacy beliefs.

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