Research Article
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Year 2025, Volume: 12 Issue: 1, 174 - 194

Abstract

Project Number

1919B012308984

References

  • Blum, W. (1996): Anwendungsbezüge im Mathematikunterricht – Trends und Perspektiven. – In: Kadunz, G. et al. (Eds): Trends und Perspektiven. Schriftenreihe Didaktik der Mathematik, Vol. 23. – Wien: Hölder-Pichler-Tempsky, p. 15–38.
  • Blum, W. (2011). Can modeling be taught and learned? Some answers from empirical research. In: G. Kaiser, W. Blum, R. Borromeo Ferri & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling (ICTMA 14) (pp. 15-30). Dordrecht: Springer.
  • Blum, W., & Leiss, D. (2007). How do students and teachers deal with modeling problems. In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modeling (pp. 222-231). Chichester, England: Horwood.
  • Borromeo Ferri, R. (2006). Theoretical and empirical differentiations of phases in the modelling process. Zentralblatt für Didaktik der Mathematik, 38(2), 86–95.
  • Borromeo Ferri, R. (2007). Modelling problems from a cognitive perspective. In: C.R. Haines, P.L. Galbraith, W. Blum & S. Khan (Eds.), Mathematical modelling: Education, engineering, and economics (pp. 260-270). Chichester: Horwood.
  • Borromeo Ferri, R. (2014). Mathematical modeling – The teachers’ responsibility. In A. Sanfratello & B. Dickman (Eds.), Proceedings of conference on mathematical modeling at Teachers College of Columbia University (pp. 26–31). New York.
  • Borromeo Ferri, R. (2018). Learning how to teach mathematical modeling in school and teacher education. Springer.
  • Common Core State Standards Initiative [CCSSI]. (2010). Common core state standards for mathematics. Washington, DC: Author. Retrieved from https://www.thecorestandards.org/
  • Colajanni, G., Gobbi, A., Picchi, M., Raffaele, A., & Taranto, E. (2023). An operations research-based teaching unit for grade 10: The ROAR experience, part I. INFORMS Transactions on Education, 23(2), 104–120.
  • Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Thousand Oaks, CA: Sage.
  • Dündar Karaman, R. (2022). Amerika birleşik devletleri ortaokul matematik öğretim programı. In M. Tastepe & S. Alkan (Ed), Karşılaştırmalı Matematik Öğretim Programları (p. 79–109). Nobel Akademik Yayıncılık.
  • English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3, 3. doi:10.1186/s40594-016-0036-1
  • English, L. D. (2021). Mathematical and interdisciplinary modeling in optimizing young children’s learning. In J. Suh, M. Wickstrom, & L. D. English (Eds.), Exploring mathematical modeling with young learners (pp. 3-24). Springer.
  • English, L. D. (2023). Ways of thinking in STEM-based problem solving. ZDM - Mathematics Education, 55(7), 1219-1230. doi:10.1007/s11858-023-01474-7
  • Erbaş, A. K., Kertil, M., Çetinkaya, B., Çakıroğlu, E., Alacacı, C., & Baş, S. (2014). Matematik eğitiminde matematiksel modelleme: Temel kavramlar ve farklı yaklaşımlar. Kuram ve Uygulamada Eğitim Bilimleri, 14(4), 1–21.
  • Ferrarello, D., Gionfriddo, M., Grasso, F., & Mammana, M. F. (2022). Graph theory and combinatorial calculus: An early approach to enhance robust understanding. ZDM–Mathematics Education, 54(4), 847–864. doi:10.1007/s11858-022-01407-w
  • Geiger, V. (2011). Factors affecting teachers’ adoption of innovative practices with technology and mathematical modeling. In G. Kaiser, W. Blum, R. Borromeo, & F. G. Stillman (Eds.), Trends in teaching and learning of mathematical modeling (pp. 305-314). New York, NY: Springer.
  • Gravemeijer, K., & Stephan, M. (2002). Emergent models as an instructional design heuristic. In K. Gravemeijer, R. Lehrer, B. Oers, & L. Verschaffel (Eds.), Symbolizing, modeling and tool use in mathematics education (pp. 145–169). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Greefrath, G., Siller, H. S., Vorhölter, K., & Kaiser, G. (2022). Mathematical modelling and discrete mathematics: opportunities for modern mathematics teaching. ZDM–Mathematics Education, 54(4), 865–879. doi:10.1007/s11858-022-01339-5
  • Hitt, F. (Ed.) (2002). Representations and mathematical visualization. PME-NA Working Group (1998-2002). Mexico City: Cinvestav-IPN.
  • Kaiser, G. (1996): Realitätsbezüge im Mathematikunterricht – Ein Überblick über die aktuelle und historische Diskussion. – Graumann, G. et al. (Eds): Materialien für einen realitätsbezogenen Mathematikunterricht. – Bad Salzdetfurth: Franzbecker, p. 66 – 84.
  • Kaiser, G., & Schwarz, B. (2010). Authentic modelling problems in mathematics education – Examples and experiences. Journal für Mathematikdidaktik, 31(1–2), 51. doi:10.1007/s13138-010-0001-3
  • Kaiser, G., Bracke, M., Göttlich, S., & Kaland, C. (2013). Authentic complex modelling problems in mathematics education. In Damlamian, A., Rodrigues, J. F., & Sträßer, R. (Eds.), Educational interfaces between mathematics and industry: report on an ICMI-ICIAM-study (pp. 287-297). Springer.
  • Karaman Dündar, R. (2023). Matematiksel modelleme. In Erdoğan, F. (Ed.), Matematik ve fen bilimleri eğitiminde yeni yaklaşımlar (pp. 109-122). Efe Akademi.
  • Lehmann, T. H. (2024). Mathematical modelling as a vehicle for eliciting algorithmic thinking. Educational Studies in Mathematics, 115(2), 151–176. doi:10.1007/s10649-023-10275-4
  • Lesh, R., & Doerr, H. M. (2003). Foundations of a models and modeling perspective on mathematics teaching, learning, and problem solving. In R. Lesh, & H. M. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching (pp. 3-33). Mahwah, NJ: Lawrence Erlbaum.
  • Lesh, R., & Lehrer, R. (2003). Models and modeling perspectives on the development of students and teachers. Mathematical Thinking and Learning, 5(2-3), 109–129. doi:10.1080/10986065.2003.9679996
  • Ludwig, M. & Xu, B. (2010). A comparative study of modelling competencies among Chinese and German students. Journal für Mathematik-Didaktik, 31(1), 77–97. doi:10.1007/s13138-010-0005-z
  • Maaß, K. (2007). Modelling in class: What do we want the students to learn? In C. Haines, P. Galbraith, W. Blum, S. Khan, & Mathematical Modelling (Eds.), Education, engineering and economics (pp. 65–78). Chichester: Horwood Publishing.
  • MoNE (MEB) (2018). Matematik Dersi Öğretim Programı. Retrieved from http://mufredat.meb.gov.tr/Dosyalar/201813017165445-MATEMAT%C4%B0K%20%C3%96%C4%9ERET%C4%B0M%20PROGRAMI%202018v.pdf
  • Merriam, S. B. (1998). Qualitative research and case study applications in education. San Francisco: Jossey-Bass.
  • National Council of Teachers of Mathematics (NCTM) (1989). Curriculum and evaluation standards for school mathematics. Reston: VA.
  • National Council of Teachers of Mathematics (NCTM) (2000). Principles and Standards for School Mathematics. Reston: Va.
  • Niss, M. (2004). Mathematical competencies and the learning of mathematics: The Danish KOM project. In A. Gagtsis & Papastavridis (Eds.), 3rd Mediterranean conference on mathematical education (pp. 115–124), 3–5 January 2003, Athens. Athens: The Hellenic Mathematical Society, 2003.
  • Niss, M., & Blum, W. (2020). The learning and teaching of mathematical modelling. Routledge.
  • Raffaele, A., & Gobbi, A. (2021). Teaching operations research before university: A focus on grades 9–12. Operations Research Forum, 2(1), 13. doi:10.1007/s43069-021-00054-3
  • Sandefur, J., Lockwood, E., Hart, E., & Greefrath, G. (2022). Teaching and learning discrete mathematics. ZDM–Mathematics Education, 54(4), 753–775. doi:10.1007/s11858-022-01399-7
  • Schuster, A. (2004). About traveling salesmen and telephone networks—Combinatorial optimization problems at high school. ZDM, 36, 77–81.
  • Sokolowski, A. (2015). The Effect of Math Modeling on Student's Emerging Understanding. IAFOR Journal of Education, 3(2), 142–156.
  • Stillman, G. (2019). State of the art on modelling in mathematics education: Lines of inquiry. In G. Stillman & J. Brown (Eds.), Lines of Inquiry of Mathematical Modelling Research in Education (pp. 1–19). Springer.
  • Stillman, G., Brown, J. & Galbraith, P. (2010). Identifying challenges within transition phases of mathematical modeling activities at year 9. In: R. Lesh, P.L. Galbraith, C.R. Haines & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 385–398). NewYork: Springer.
  • Taranto, E., Colajanni, G., Gobbi, A., Picchi, M., & Raffaele, A. (2024). Fostering students' modelling and problem-solving skills through Operations Research, digital technologies, and collaborative learning. International Journal of Mathematical Education in Science and Technology, 55(8), 1957–1998. doi:10.1080/0020739X.2022.2115421
  • Villegas, J. L., Castro, E., & Gutierrez, J. (2009). Representation in problem solving: A case study with optimization problems. Electronic Journal of Research in Educational Psychology, 7(1), 279-308.
  • Yin, R. (2009). Case study research: Design and methods. California: Sage.

Middle School Students' Approaches to Optimization: Insights into Mathematical Modeling and Real-World Problem Solving

Year 2025, Volume: 12 Issue: 1, 174 - 194

Abstract

This study investigates middle school students' perspectives on optimization problems in mathematics, focusing on their problem-solving processes and learning experiences. Optimization involves finding the best solution under specific constraints or maximizing/minimizing an objective function, a concept closely related to mathematical modeling. This process is designed to develop essential skills, such as logical reasoning, prediction, argumentation, and critical thinking, by framing real-world situations as mathematical challenges. Although existing research on students' problem-solving with optimization problems primarily involves high school or university students, recent studies emphasize the necessity of introducing optimization concepts earlier in education.
This study administered four optimization problems to 16 middle school students to explore their experiences and opinions. Data were collected through observations, feedback forms, and individual interviews and analyzed using descriptive and content analysis methods. The findings reveal that students limited prior exposure to optimization problems significantly contributes to their challenges. While students generally understand the optimization problems, their performance varies notably, especially in assumption-making and mathematical calculations.
The study underscores the need for systematically integrating optimization problems into the middle school curriculum to enhance students' problem-solving skills and critical thinking. It suggests incorporating mathematical modeling with optimization tasks could improve students' abstraction and problem-solving abilities. Future research could investigate the effects of optimization problems on students' problem-solving skills and mathematical understanding.
ÖZ
Bu çalışma, ortaokul öğrencilerinin matematikteki optimizasyon problemlerine yönelik görüşlerini ve çözüm süreçlerini incelemeyi amaçlamaktadır. Optimizasyon, belirli kısıtlamalar altında en iyi çözümü bulmayı veya bir amaç fonksiyonunu maksimize/minimize etmeyi içeren bir süreç olup, matematiksel modelleme ile doğrudan ilişkilidir. Bu süreç, öğrencilere mantıksal akıl yürütme, tahmin etme, muhakeme yapma ve eleştirel düşünme gibi temel becerileri kazandırır. Optimizasyon problemleri, gerçek dünya durumlarını matematiksel modelleme problemleri olarak ele alır ve bu bağlamda önemli bir eğitimsel değer taşır. Mevcut literatürde optimizasyon problemleri genellikle lise veya üniversite seviyesinde incelenmiş olup, ortaöğretim düzeylerinde bu kavramların daha detaylı öğretilmesi gerektiği önerilmiştir.
Bu bağlamda, bu çalışmada dört optimizasyon problemi 16 ortaokul öğrencisine uygulanmış ve öğrencilerin bu problemleri çözme süreçleri ile görüşleri derinlemesine incelenmiştir. Veriler, gözlemler, geri bildirim formları ve birebir görüşmeler yoluyla toplanmış ve betimsel ile içerik analizi yöntemleri kullanılarak değerlendirilmiştir. Bulgular, öğrencilerin optimizasyon problemlerine yönelik sınırlı ön bilgiye sahip olmalarının, çözüm süreçlerinde karşılaştıkları zorlukları önemli ölçüde artırdığını göstermektedir. Öğrenciler, optimizasyon problemlerini genel olarak anlamalarına rağmen, performansları özellikle varsayım yapma ve matematiksel hesaplamalar aşamalarında değişkenlik göstermektedir.
Çalışmanın sonuçları, optimizasyon problemlerinin ortaokul müfredatına sistematik bir şekilde entegre edilmesinin önemini vurgulamaktadır. Matematiksel modelleme ile optimizasyon problemlerinin entegrasyonunun, öğrencilerin soyutlama becerilerini ve problem çözme yeteneklerini artırabileceği öngörülmektedir. Gelecekteki araştırmalar, optimizasyon problemlerinin öğrencilerin problem çözme becerileri ve matematiksel anlayışları üzerindeki etkilerini inceleyebilir.

Ethical Statement

Etik Kurul Onay Belgesi sisteme yüklenmiştir.

Supporting Institution

TÜBİTAK

Project Number

1919B012308984

Thanks

This study is based upon the “Optimization Concepts from the Perspective of Middle School Students” project supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK; Project No: 1919B012308984). Any opinions, findings, conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of TÜBİTAK.

References

  • Blum, W. (1996): Anwendungsbezüge im Mathematikunterricht – Trends und Perspektiven. – In: Kadunz, G. et al. (Eds): Trends und Perspektiven. Schriftenreihe Didaktik der Mathematik, Vol. 23. – Wien: Hölder-Pichler-Tempsky, p. 15–38.
  • Blum, W. (2011). Can modeling be taught and learned? Some answers from empirical research. In: G. Kaiser, W. Blum, R. Borromeo Ferri & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling (ICTMA 14) (pp. 15-30). Dordrecht: Springer.
  • Blum, W., & Leiss, D. (2007). How do students and teachers deal with modeling problems. In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modeling (pp. 222-231). Chichester, England: Horwood.
  • Borromeo Ferri, R. (2006). Theoretical and empirical differentiations of phases in the modelling process. Zentralblatt für Didaktik der Mathematik, 38(2), 86–95.
  • Borromeo Ferri, R. (2007). Modelling problems from a cognitive perspective. In: C.R. Haines, P.L. Galbraith, W. Blum & S. Khan (Eds.), Mathematical modelling: Education, engineering, and economics (pp. 260-270). Chichester: Horwood.
  • Borromeo Ferri, R. (2014). Mathematical modeling – The teachers’ responsibility. In A. Sanfratello & B. Dickman (Eds.), Proceedings of conference on mathematical modeling at Teachers College of Columbia University (pp. 26–31). New York.
  • Borromeo Ferri, R. (2018). Learning how to teach mathematical modeling in school and teacher education. Springer.
  • Common Core State Standards Initiative [CCSSI]. (2010). Common core state standards for mathematics. Washington, DC: Author. Retrieved from https://www.thecorestandards.org/
  • Colajanni, G., Gobbi, A., Picchi, M., Raffaele, A., & Taranto, E. (2023). An operations research-based teaching unit for grade 10: The ROAR experience, part I. INFORMS Transactions on Education, 23(2), 104–120.
  • Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Thousand Oaks, CA: Sage.
  • Dündar Karaman, R. (2022). Amerika birleşik devletleri ortaokul matematik öğretim programı. In M. Tastepe & S. Alkan (Ed), Karşılaştırmalı Matematik Öğretim Programları (p. 79–109). Nobel Akademik Yayıncılık.
  • English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3, 3. doi:10.1186/s40594-016-0036-1
  • English, L. D. (2021). Mathematical and interdisciplinary modeling in optimizing young children’s learning. In J. Suh, M. Wickstrom, & L. D. English (Eds.), Exploring mathematical modeling with young learners (pp. 3-24). Springer.
  • English, L. D. (2023). Ways of thinking in STEM-based problem solving. ZDM - Mathematics Education, 55(7), 1219-1230. doi:10.1007/s11858-023-01474-7
  • Erbaş, A. K., Kertil, M., Çetinkaya, B., Çakıroğlu, E., Alacacı, C., & Baş, S. (2014). Matematik eğitiminde matematiksel modelleme: Temel kavramlar ve farklı yaklaşımlar. Kuram ve Uygulamada Eğitim Bilimleri, 14(4), 1–21.
  • Ferrarello, D., Gionfriddo, M., Grasso, F., & Mammana, M. F. (2022). Graph theory and combinatorial calculus: An early approach to enhance robust understanding. ZDM–Mathematics Education, 54(4), 847–864. doi:10.1007/s11858-022-01407-w
  • Geiger, V. (2011). Factors affecting teachers’ adoption of innovative practices with technology and mathematical modeling. In G. Kaiser, W. Blum, R. Borromeo, & F. G. Stillman (Eds.), Trends in teaching and learning of mathematical modeling (pp. 305-314). New York, NY: Springer.
  • Gravemeijer, K., & Stephan, M. (2002). Emergent models as an instructional design heuristic. In K. Gravemeijer, R. Lehrer, B. Oers, & L. Verschaffel (Eds.), Symbolizing, modeling and tool use in mathematics education (pp. 145–169). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Greefrath, G., Siller, H. S., Vorhölter, K., & Kaiser, G. (2022). Mathematical modelling and discrete mathematics: opportunities for modern mathematics teaching. ZDM–Mathematics Education, 54(4), 865–879. doi:10.1007/s11858-022-01339-5
  • Hitt, F. (Ed.) (2002). Representations and mathematical visualization. PME-NA Working Group (1998-2002). Mexico City: Cinvestav-IPN.
  • Kaiser, G. (1996): Realitätsbezüge im Mathematikunterricht – Ein Überblick über die aktuelle und historische Diskussion. – Graumann, G. et al. (Eds): Materialien für einen realitätsbezogenen Mathematikunterricht. – Bad Salzdetfurth: Franzbecker, p. 66 – 84.
  • Kaiser, G., & Schwarz, B. (2010). Authentic modelling problems in mathematics education – Examples and experiences. Journal für Mathematikdidaktik, 31(1–2), 51. doi:10.1007/s13138-010-0001-3
  • Kaiser, G., Bracke, M., Göttlich, S., & Kaland, C. (2013). Authentic complex modelling problems in mathematics education. In Damlamian, A., Rodrigues, J. F., & Sträßer, R. (Eds.), Educational interfaces between mathematics and industry: report on an ICMI-ICIAM-study (pp. 287-297). Springer.
  • Karaman Dündar, R. (2023). Matematiksel modelleme. In Erdoğan, F. (Ed.), Matematik ve fen bilimleri eğitiminde yeni yaklaşımlar (pp. 109-122). Efe Akademi.
  • Lehmann, T. H. (2024). Mathematical modelling as a vehicle for eliciting algorithmic thinking. Educational Studies in Mathematics, 115(2), 151–176. doi:10.1007/s10649-023-10275-4
  • Lesh, R., & Doerr, H. M. (2003). Foundations of a models and modeling perspective on mathematics teaching, learning, and problem solving. In R. Lesh, & H. M. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching (pp. 3-33). Mahwah, NJ: Lawrence Erlbaum.
  • Lesh, R., & Lehrer, R. (2003). Models and modeling perspectives on the development of students and teachers. Mathematical Thinking and Learning, 5(2-3), 109–129. doi:10.1080/10986065.2003.9679996
  • Ludwig, M. & Xu, B. (2010). A comparative study of modelling competencies among Chinese and German students. Journal für Mathematik-Didaktik, 31(1), 77–97. doi:10.1007/s13138-010-0005-z
  • Maaß, K. (2007). Modelling in class: What do we want the students to learn? In C. Haines, P. Galbraith, W. Blum, S. Khan, & Mathematical Modelling (Eds.), Education, engineering and economics (pp. 65–78). Chichester: Horwood Publishing.
  • MoNE (MEB) (2018). Matematik Dersi Öğretim Programı. Retrieved from http://mufredat.meb.gov.tr/Dosyalar/201813017165445-MATEMAT%C4%B0K%20%C3%96%C4%9ERET%C4%B0M%20PROGRAMI%202018v.pdf
  • Merriam, S. B. (1998). Qualitative research and case study applications in education. San Francisco: Jossey-Bass.
  • National Council of Teachers of Mathematics (NCTM) (1989). Curriculum and evaluation standards for school mathematics. Reston: VA.
  • National Council of Teachers of Mathematics (NCTM) (2000). Principles and Standards for School Mathematics. Reston: Va.
  • Niss, M. (2004). Mathematical competencies and the learning of mathematics: The Danish KOM project. In A. Gagtsis & Papastavridis (Eds.), 3rd Mediterranean conference on mathematical education (pp. 115–124), 3–5 January 2003, Athens. Athens: The Hellenic Mathematical Society, 2003.
  • Niss, M., & Blum, W. (2020). The learning and teaching of mathematical modelling. Routledge.
  • Raffaele, A., & Gobbi, A. (2021). Teaching operations research before university: A focus on grades 9–12. Operations Research Forum, 2(1), 13. doi:10.1007/s43069-021-00054-3
  • Sandefur, J., Lockwood, E., Hart, E., & Greefrath, G. (2022). Teaching and learning discrete mathematics. ZDM–Mathematics Education, 54(4), 753–775. doi:10.1007/s11858-022-01399-7
  • Schuster, A. (2004). About traveling salesmen and telephone networks—Combinatorial optimization problems at high school. ZDM, 36, 77–81.
  • Sokolowski, A. (2015). The Effect of Math Modeling on Student's Emerging Understanding. IAFOR Journal of Education, 3(2), 142–156.
  • Stillman, G. (2019). State of the art on modelling in mathematics education: Lines of inquiry. In G. Stillman & J. Brown (Eds.), Lines of Inquiry of Mathematical Modelling Research in Education (pp. 1–19). Springer.
  • Stillman, G., Brown, J. & Galbraith, P. (2010). Identifying challenges within transition phases of mathematical modeling activities at year 9. In: R. Lesh, P.L. Galbraith, C.R. Haines & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 385–398). NewYork: Springer.
  • Taranto, E., Colajanni, G., Gobbi, A., Picchi, M., & Raffaele, A. (2024). Fostering students' modelling and problem-solving skills through Operations Research, digital technologies, and collaborative learning. International Journal of Mathematical Education in Science and Technology, 55(8), 1957–1998. doi:10.1080/0020739X.2022.2115421
  • Villegas, J. L., Castro, E., & Gutierrez, J. (2009). Representation in problem solving: A case study with optimization problems. Electronic Journal of Research in Educational Psychology, 7(1), 279-308.
  • Yin, R. (2009). Case study research: Design and methods. California: Sage.
There are 44 citations in total.

Details

Primary Language English
Subjects Mathematics Education
Journal Section Articles
Authors

Rüveyda Karaman Dündar 0000-0002-8903-9627

İleyda Arslan 0009-0009-6708-9760

İlayda Mengi 0009-0006-2670-9423

Project Number 1919B012308984
Early Pub Date April 14, 2025
Publication Date
Submission Date August 25, 2024
Acceptance Date March 19, 2025
Published in Issue Year 2025 Volume: 12 Issue: 1

Cite

APA Karaman Dündar, R., Arslan, İ., & Mengi, İ. (2025). Middle School Students’ Approaches to Optimization: Insights into Mathematical Modeling and Real-World Problem Solving. E-Kafkas Journal of Educational Research, 12(1), 174-194. https://doi.org/10.30900/kafkasegt.1538533

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