Matematik Öğretmen Adayları İçin Matematiksel Zihniyet Ölçeği: Geçerlik ve Güvenirlik Çalışması
Yıl 2026,
Cilt: 46 Sayı: 1
,
1
-
17
,
27.04.2026
Neslihan Demirci
,
Rıdvan Ezentaş
,
Çiğdem Arslan
Öz
Bireylerin öğrenme süreçlerini etkileyen temel unsurlardan biri, zekâ ve yeteneklerinin geliştirilebilir olduğuna dair sahip oldukları zihniyet yapısıdır. Özellikle matematik gibi zorlayıcı disiplinlerde bu zihniyet; öğrencilerin tutum, başarı ve motivasyonlarını şekillendiren kritik bir değişkendir. Bu araştırmanın amacı, Megawanti ve arkadaşları (2024) tarafından geliştirilen ve yazarlarından temin edilen geniş kapsamlı madde havuzu temel alınarak hazırlanan “Matematiksel Zihniyet Ölçeği”nin Türkçeye uyarlanması, geçerlik ve güvenirlik çalışmalarının gerçekleştirilmesidir. Araştırma, Türkiye’deki altı farklı üniversitenin ilköğretim ve ortaöğretim matematik öğretmenliği programlarında öğrenim gören 337 öğretmen adayı ile yürütülmüştür. Ölçeğin yapı geçerliğini test etmek amacıyla yapılan analizler sonucunda; öğretmen adaylarının zihniyet algılarını detaylı bir şekilde yansıtan 8 faktör ve 25 maddeden oluşan modelin iyi uyum sergilediği saptanmıştır. Ölçeğin bütününe ilişkin Cronbach Alfa iç tutarlık katsayısı 0.86 iken, alt boyutlara ait bileşik güvenirlik (CR) değerlerinin 0.603 ile 0.808 arasında değiştiği belirlenmiştir. Elde edilen bulgular, Türkçeye uyarlanan bu ölçeğin, matematik öğretmen adaylarının çok boyutlu matematiksel zihniyet yapılarını belirlemede geçerli ve güvenilir bir ölçme aracı olduğunu ortaya koymaktadır.
Kaynakça
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Bui, P., Pongsakdi, N., McMullen, J., Lehtinen, E., & Hannula-Sormunen, M. M. (2023). A systematic review of mindset interventions in mathematics classrooms: What works and what does not? Educational Research Review, 40, 100554. https://doi.org/10.1016/j.edurev.2023.100554
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Chapman, S., & Mitchell, M. (2018). Mindset for math. The Learning Professional, 39(5), 60-64.
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Chen, S., Ding, Y., & Liu, X. (2023). Development of the growth mindset scale: Evidence of structural validity, measurement model, direct and indirect effects in Chinese samples. Current Psychology, 42(3), 1712-1726. https://doi.org/10.1007/s12144-021-01532-x
-
Çiftçi, S. K., & Yıldız, P. (2020). Matematik inancı ölçeği: Yapı geçerliği ve güvenirlik analizleri. Mehmet Akif Ersoy University Journal of Education Faculty, 56, 121-138.
-
Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
-
Daly, I., Bourgaize, J., & Vernitski, A. (2019). Mathematical mindsets increase student motivation: Evidence from the EEG. Trends in Neuroscience and Education, 15, 18-28. https://doi.org/10.1016/j.tine.2019.02.005
-
Delice, A., Erden, S., Yılmaz, K., & Sevimli, E. (2016). Matematik İnanç Ölçeği’nin Türkçe’ye uyarlanması sürecinde geçerlik ve güvenirlik çalışması. Kastamonu Eğitim Dergisi, 24(2), 737-754.
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Denzine, G. M., & Kowalski, G. J. (2002). Confirmatory factor analysis of the assessment for living and learning scale: A cross-validation investigation. Measurement and Evaluation in Counseling and Development, 35(1), 14–26. https://doi.org/10.1080/07481756.2002.12069044
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Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41 (10), 1040–1048. https://doi.org/10.1037/0003-066x.41.10.1040
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Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Psychology Press. https://doi.org/10.4324/9781315783048
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Dweck C. S. (2014) Mindsets and math/science achievement. Carnegie Corporation of New York.
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Erden, B., & Yıldız, S. (2023). Gelişim odaklı zihniyet inançları ölçeği geliştirme çalışması ve psikometrik özellikleri. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 23(1), 98-117. https://doi.org/10.17240/aibuefd.2023..-1162857
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Mathematical Mindset Scale for Prospective Mathematics Teachers: A Validity and Reliability Study
Yıl 2026,
Cilt: 46 Sayı: 1
,
1
-
17
,
27.04.2026
Neslihan Demirci
,
Rıdvan Ezentaş
,
Çiğdem Arslan
Öz
One of the fundamental factors influencing individuals’ learning processes is their mindset regarding the malleability of intelligence and abilities. Particularly in challenging disciplines such as mathematics, mindset constitutes a critical variable that shapes students’ attitudes, achievement, and motivation. The purpose of this study is to adapt the Mathematical Mindset Scale developed by Megawanti et al. (2024) and constructed from a comprehensive item pool provided by the original authors into Turkish and to examine its validity and reliability. The study was conducted with 337 prospective mathematics teachers enrolled in primary and secondary mathematics teacher education programs at six different universities in Türkiye. As a result of the analyses to test the construct validity of the scale, an eight-factor model comprising 25 items that comprehensively reflects prospective teachers’ mindset perceptions was found to demonstrate good model fit. The overall Cronbach’s alpha internal consistency coefficient of the scale was 0.86, while the composite reliability (CR) values for the subscales ranged from 0.603 to 0.808. The findings indicate that the Turkish version of the scale is a valid and reliable tool for assessing the multidimensional structures of prospective mathematics teachers' mathematical mindsets.
Kaynakça
-
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139–161. https://doi.org/10.1016/0167-8116(95)00038-0
-
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191. https://doi.org/10.1097/00007632-200012150-00014
-
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi/10.1037/0033-2909.88.3.588
-
Beyaztaş, D. İ., & Hymer, B. (2018). An analysis of Turkish students’ perception of intelligence from primary school to university. Gifted Education International, 34(1), 19-35. https://doi.org/10.1177/0261429416649041
-
Boaler, J. (2013, March). Ability and mathematics: The mindset revolution that is reshaping education. The Forum, 55(1), 143. https://doi.org/10.2304/forum.2013.55.1.143
-
Boaler, J. (2015). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. John Wiley & Sons. https://doi.org/10.1080/14794802.2016.1237374
-
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.
-
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage. https://doi.org/10.1177/0049124192021002005
-
Bui, P., Pongsakdi, N., McMullen, J., Lehtinen, E., & Hannula-Sormunen, M. M. (2023). A systematic review of mindset interventions in mathematics classrooms: What works and what does not? Educational Research Review, 40, 100554. https://doi.org/10.1016/j.edurev.2023.100554
-
Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri. Pegem Akademi. https://doi.org/10.14527/9789944919289
-
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications and programming (2. Ed.). Routledge.
-
Can, A. (2020). SPSS ile bilimsel araştırma sürecinde nicel veri analizi (9.Baskı). Pegem Akademi.
-
Canbazoğlu-Bilici, S. (2019). Örnekleme yöntemi. H. Özmen ve O. Karamustafaoğlu (Edlr.), Eğitimde araştırma yöntemleri içinde (2. Baskı, s. 56-78). Pegem Akademi. https://doi.org/10.14527/9786052417867.04
-
Chapman, S., & Mitchell, M. (2018). Mindset for math. The Learning Professional, 39(5), 60-64.
-
Chen, S., Ding, Y., & Liu, X. (2023). Development of the growth mindset scale: Evidence of structural validity, measurement model, direct and indirect effects in Chinese samples. Current Psychology, 42(3), 1712-1726. https://doi.org/10.1007/s12144-021-01532-x
-
Çiftçi, S. K., & Yıldız, P. (2020). Matematik inancı ölçeği: Yapı geçerliği ve güvenirlik analizleri. Mehmet Akif Ersoy University Journal of Education Faculty, 56, 121-138.
-
Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
-
Daly, I., Bourgaize, J., & Vernitski, A. (2019). Mathematical mindsets increase student motivation: Evidence from the EEG. Trends in Neuroscience and Education, 15, 18-28. https://doi.org/10.1016/j.tine.2019.02.005
-
Delice, A., Erden, S., Yılmaz, K., & Sevimli, E. (2016). Matematik İnanç Ölçeği’nin Türkçe’ye uyarlanması sürecinde geçerlik ve güvenirlik çalışması. Kastamonu Eğitim Dergisi, 24(2), 737-754.
-
Denzine, G. M., & Kowalski, G. J. (2002). Confirmatory factor analysis of the assessment for living and learning scale: A cross-validation investigation. Measurement and Evaluation in Counseling and Development, 35(1), 14–26. https://doi.org/10.1080/07481756.2002.12069044
-
Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41 (10), 1040–1048. https://doi.org/10.1037/0003-066x.41.10.1040
-
Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Psychology Press. https://doi.org/10.4324/9781315783048
-
Dweck C. S. (2014) Mindsets and math/science achievement. Carnegie Corporation of New York.
-
Erden, B., & Yıldız, S. (2023). Gelişim odaklı zihniyet inançları ölçeği geliştirme çalışması ve psikometrik özellikleri. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 23(1), 98-117. https://doi.org/10.17240/aibuefd.2023..-1162857
-
Erkuş, A. (2003). Psikometri üzerine yazılar. Türk Psikologlar Derneği Yayınları No 24.
-
Field, A. (2009). Discovering statistics using SPSS (3rd Edition). Sage Publications.
-
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
-
Fraenkel, J., Wallen, N., & Hyun, H. (1993). How to design and evaluate research in education 10th ed. McGraw-Hill Education.
-
Goldin, G. A. (2002). Affect, meta-affect, and mathematical belief structures, In G. Leder, E. Pehkonen, & G. Törner (Eds.), Beliefs: A hidden variable in mathematics education? (pp. 59 72). Kluwer Academic Publishers. https://doi.org/10.1007/0-306-47958-3_4
-
Green, S. B., Salkind, N. J., & Akey, T. M. (1997). Using SPSS for Windows: Analyzing and understanding data. Prentice Hall, Inc.
-
Hacıömeroğlu, G. (2011). Matematiksel problem çözmeye ilişkin inanç ölçeğinin Türkçe’ye uyarlama çalışması. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, 17, 119-132.
-
Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-13.
-
Hambleton, R. K., Merenda, P. F., & Spielberger, C. D. (2004). Issues, designs, and technical guidelines for adapting tests into multiple languages and cultures. In R. K. Hambleton, P. F. Merenda, & C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 15-50). Psychology Press. https://doi.org/10.4324/9781410611758
-
Hart, L. E. (1989). Describing the affective domain: Saying what we mean. In affect and mathematical problem solving: A new perspective (pp. 37-45). Springer. https://doi.org/10.1007/978-1-4612-3614-6_3
-
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit ındexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
-
Ignacio, N. G., Nieto, L. J. B., & Barona, E. G. (2006). The affective domain in mathematics learning. International Electronic Journal of Mathematics Education, 1(1), 16-32. https://doi.org/10.29333/iejme/169
-
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