Research Article
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Investigating the Role of Modeling Practices on Mathematical Literacy

Year 2023, , 180 - 189, 30.01.2023
https://doi.org/10.14686/buefad.1027353

Abstract

The mathematical literacy or competence notion in PISA deals with the capacity of students to analyze, reason and communicate efficiently as they pose, formulate, solve and interpret mathematical problems in a variety of situations. The best way to improve mathematics literacy is that students have the necessary mathematical knowledge and different problem solving strategies, know when and how to use these strategies, and work with activities that involve different contexts of interest. When considered in this respect, teachers have an important role in the development of students' mathematical literacy. The aim of this study was to investigate the mathematics literacy status of Mathematics Teacher Candidates through PISA questions. The mathematical literacy status of pre-service teachers was examined within the scope of conceptual, operational and contextual questions and in terms of gender, academic grade point average and mathematical modeling. At the same time, semi-structured interviews have examined the difficulties that pre-service teachers experienced. Fully mixed concurrent equal status design was used. The participants were 113 pre-service mathematics teachers. Independent samples t-test and covariance analysis was used for the comparisons. Qualitative data analysis was conducted with content analysis. The research results, together with the suggestions, reveal important points for future studies.

References

  • An, S., Kulm, G., & Wu, Z. (2004) The pedagogical content knowledge of middle school mathematics teachers in China and the U.S. Journal of Mathematics Teacher Education, 7,145-172.
  • Aydın, U., & Özgeldi, M. (2019). The PISA tasks: Unveiling prospective elementary mathematics teachers’ difficulties with contextual, conceptual, and procedural knowledge. Scandinavian Journal of Educational Research, 63(1), 105-123.
  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2001). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
  • Chiu, M. M., & Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18, 321–336.
  • De Jong, T., & Ferguson-Hessler, M. G. (1996). Types and qualities of knowledge. Educational psychologist, 31(2), 105-113.
  • Groth, R. E., & Bergner, J. A. (2006). Preservice elementary teachers’ conceptual and procedural knowledge of mean, median and mode. Mathematical Thinking and Learning, 8(1), 37–63. doi:10.1207/ s15327833mtl0801_3.
  • Hiebert, J., & Lefevre, J. (1986). Conceptual and procedural knowledge in mathematics: An introductory analysis. In J. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 1–27). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Research Paradigm Whose Time Has Come. Educational researcher, 33(14), 14-26. doi:10.3102/0013189X033007014
  • Kilpatrick, J. (2001). Understanding mathematical literacy: The contribution of research. Educational Studies in Mathematics, 47(1), 101-116.
  • Kriegbaum, K., Jansen, M., & Spinath, B. (2015). Motivation: A predictor of PISA’s mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences, 43, 140–148.
  • Maab, K. (2005). Barriers and opportunities for the integration of modelling in mathematic classes-results of an empirical study. Teaching Mathematics and its Applications, 2 (3), 1-16.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Movshovitz-Hadar, N., Zaslavsky, O., & Inbar, S. (1987). An empirical classification model for errors in high school mathematics. Journal for Research in Mathematics Education, 18(1), 3–14. doi:10.2307/749532.
  • Niss, M., & Jablonka, E. (2020). Mathematical literacy. Encyclopedia of mathematics education, 548-553.
  • OECD (2014). PISA 2012 Results: What Students Know and Can Do (Volume I: Student Performance in Mathematics, Reading and Science. OECD Publishing.
  • Olande, O. (2014). Graphical artefacts: Taxonomy of students’ response to test items. Educational Studies in Mathematics, 85, 53–74.
  • Pollak, H. (1979). the interaction between mathematics and other school subjects. UNESCO, New Trends in Mathematics Teaching IV. Paris.
  • Radatz, H. (1980). Students’ errors in the mathematical learning process: a survey. For the Learning of Mathematics, 1(1), 16–20.
  • Rittle-Johnson, B., & Koedinger, K. R. (2005). Designing knowledge scaffolds to support mathematical problem solving. Cognition and Instruction, 23, 313–349.
  • Sáenz, C. (2009). The role of contextual, conceptual and procedural knowledge in activating mathematical competencies (PISA). Educational Studies in Mathematics, 71, 123–143.
  • Steen, L. A., Turner, R., & Burkhardt, H. (2007). Developing mathematical literacy. In W. Blum, P.L. Galbraith, H.W. Henn and M. Niss (Eds), Modelling and Applications in Mathematics Education (pp. 285-294). US: Springer.
  • Swan, M., Turner, R. ve Yoon, C. (2006). The Roles of Modelling in Learning Mathematics. (eds: W. Blum, P. Galbraith, H.-W. Henn ve M. Niss), Modelling and Applications in Mathematics Education The 14. ICMI Study (s. 275- 284). New York: Springer.
  • Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. Handbook of mixed methods in social and behavioral research, 1, 13-50.
  • Widjaja, W. (2011) Towards mathematical literacy in the 21st century: perspectives from Indonesia. Southeast Asian mathematics education journal, 1(1),75-84.

Modelleme Uygulamalarının Matematik Okuryazarlığı Üzerindeki Rolünün İncelenmesi

Year 2023, , 180 - 189, 30.01.2023
https://doi.org/10.14686/buefad.1027353

Abstract

Uluslararası Öğrenci Değerlendirme Programı’nı (PISA) yürüten Ekonomik İşbirliği ve Kalkınma Teşkilatı (OECD) tarafından yapılan matematik okuryazarlığının tanımı; matematiğin dünyada oynadığı rolü anlama, sağlam temellere dayanan matematiksel hükümler verme, yapıcı-ilgili düşünce üreten kişiler olarak bireysel yaşamların gereksinimlerini karşılarken matematiği kullanma ve matematikle meşgul olma kapasitesi şeklindedir. Matematik okuryazarlığını geliştirmenin en iyi yolu öğrencilerin gerekli matematiksel bilgiye ve farklı problem çözme stratejilerine sahip olmaları, bu stratejileri ne zaman ve nasıl kullanacaklarını bilmeleri ve ilgilerini çeken farklı bağlamları barındıran etkinliklerle çalışmaları olarak ifade edilmektedir. Bu anlamda öğretmenler, öğrencilerin matematiksel okuryazarlığının gelişmesinde önemli bir role sahiptirler. Bu tespitten yola çıkılarak çalışmada Ortaokul Matematik Öğretmeni Adaylarının matematik okuryazarlık durumlarının PISA soruları üzerinden incelenmesi amaçlanmıştır. Öğretmen adaylarının matematiksel okuryazarlık durumları kavramsal, işlemsel ve bağlamsal sorular kapsamında cinsiyet, akademik not ortalaması ve matematiksel modelleme değişkenleri ile incelenmiştir. Aynı zamanda yarı yapılandırılmış görüşmelerde öğretmen adaylarının yaşadıkları zorluklar ele alınmıştır. Çalışmada tamamen karma eşzamanlı eşit statülü tasarım kullanılmıştır. Katılımcılar 113 matematik öğretmeni adayıdır. Karşılaştırmalar, bağımsız örneklem t testi ve kovaryans analizi ile gerçekleştirilmiştir. Nitel veriler ise içerik analizine tabi tutulmuştur. Araştırma sonuçları, önerilerle birlikte ileriki çalışmalar için önemli noktaları ortaya koymaktadır.

References

  • An, S., Kulm, G., & Wu, Z. (2004) The pedagogical content knowledge of middle school mathematics teachers in China and the U.S. Journal of Mathematics Teacher Education, 7,145-172.
  • Aydın, U., & Özgeldi, M. (2019). The PISA tasks: Unveiling prospective elementary mathematics teachers’ difficulties with contextual, conceptual, and procedural knowledge. Scandinavian Journal of Educational Research, 63(1), 105-123.
  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2001). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
  • Chiu, M. M., & Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18, 321–336.
  • De Jong, T., & Ferguson-Hessler, M. G. (1996). Types and qualities of knowledge. Educational psychologist, 31(2), 105-113.
  • Groth, R. E., & Bergner, J. A. (2006). Preservice elementary teachers’ conceptual and procedural knowledge of mean, median and mode. Mathematical Thinking and Learning, 8(1), 37–63. doi:10.1207/ s15327833mtl0801_3.
  • Hiebert, J., & Lefevre, J. (1986). Conceptual and procedural knowledge in mathematics: An introductory analysis. In J. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 1–27). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Research Paradigm Whose Time Has Come. Educational researcher, 33(14), 14-26. doi:10.3102/0013189X033007014
  • Kilpatrick, J. (2001). Understanding mathematical literacy: The contribution of research. Educational Studies in Mathematics, 47(1), 101-116.
  • Kriegbaum, K., Jansen, M., & Spinath, B. (2015). Motivation: A predictor of PISA’s mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences, 43, 140–148.
  • Maab, K. (2005). Barriers and opportunities for the integration of modelling in mathematic classes-results of an empirical study. Teaching Mathematics and its Applications, 2 (3), 1-16.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Movshovitz-Hadar, N., Zaslavsky, O., & Inbar, S. (1987). An empirical classification model for errors in high school mathematics. Journal for Research in Mathematics Education, 18(1), 3–14. doi:10.2307/749532.
  • Niss, M., & Jablonka, E. (2020). Mathematical literacy. Encyclopedia of mathematics education, 548-553.
  • OECD (2014). PISA 2012 Results: What Students Know and Can Do (Volume I: Student Performance in Mathematics, Reading and Science. OECD Publishing.
  • Olande, O. (2014). Graphical artefacts: Taxonomy of students’ response to test items. Educational Studies in Mathematics, 85, 53–74.
  • Pollak, H. (1979). the interaction between mathematics and other school subjects. UNESCO, New Trends in Mathematics Teaching IV. Paris.
  • Radatz, H. (1980). Students’ errors in the mathematical learning process: a survey. For the Learning of Mathematics, 1(1), 16–20.
  • Rittle-Johnson, B., & Koedinger, K. R. (2005). Designing knowledge scaffolds to support mathematical problem solving. Cognition and Instruction, 23, 313–349.
  • Sáenz, C. (2009). The role of contextual, conceptual and procedural knowledge in activating mathematical competencies (PISA). Educational Studies in Mathematics, 71, 123–143.
  • Steen, L. A., Turner, R., & Burkhardt, H. (2007). Developing mathematical literacy. In W. Blum, P.L. Galbraith, H.W. Henn and M. Niss (Eds), Modelling and Applications in Mathematics Education (pp. 285-294). US: Springer.
  • Swan, M., Turner, R. ve Yoon, C. (2006). The Roles of Modelling in Learning Mathematics. (eds: W. Blum, P. Galbraith, H.-W. Henn ve M. Niss), Modelling and Applications in Mathematics Education The 14. ICMI Study (s. 275- 284). New York: Springer.
  • Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. Handbook of mixed methods in social and behavioral research, 1, 13-50.
  • Widjaja, W. (2011) Towards mathematical literacy in the 21st century: perspectives from Indonesia. Southeast Asian mathematics education journal, 1(1),75-84.
There are 24 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Arzu Aydoğan Yenmez 0000-0001-8595-3262

Semirhan Gökçe 0000-0002-4752-5598

Publication Date January 30, 2023
Published in Issue Year 2023

Cite

APA Aydoğan Yenmez, A., & Gökçe, S. (2023). Investigating the Role of Modeling Practices on Mathematical Literacy. Bartın University Journal of Faculty of Education, 12(1), 180-189. https://doi.org/10.14686/buefad.1027353

All the articles published in the journal are open access and distributed under the conditions of CommonsAttribution-NonCommercial 4.0 International License 

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Bartın University Journal of Faculty of Education