Dergiler / International Journal of Assessment Tools in Education / 2020 / Cilt: 7 - Sayı: 2

The Development of Teachers’ Knowledge of the Nature of Mathematical Modeling Scale

Sayfa
236–254
Yayın Tarihi
2022-10-19
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Potential uses and applications of the MMKS by researchers and educators are discussed, and implications for further research are provided.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study addresses a gap in the literature on mathematical modeling education by developing the mathematical modeling knowledge scale (MMKS).
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Temel Bulgular & Veri:The MMKS was developed and validated with a total sample of 364 K–12 teachers from several public-schools using three phases.
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Sonuç & Katkı:Potential uses and applications of the MMKS by researchers and educators are discussed, and implications for further research are provided.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Nature of modeling · MMKS · Factor analysis

Özet

This study addresses a gap in the literature on mathematical modeling education by developing the mathematical modeling knowledge scale (MMKS). The MMKS is a quantitative tool created to assess teachers’ knowledge of the nature of mathematical modeling. Quantitative instruments to measure modeling knowledge is scare in the literature partially due to the lack of appropriate instruments developed to assess such knowledge among teachers. The MMKS was developed and validated with a total sample of 364 K–12 teachers from several public-schools using three phases. Phase 1 addresses content validity of the scale using reviews from experts and interviews with knowledgable teachers. Initial psychometric properties and piloting results are presented in phase 2 of the study, and phase 3 reports on the findings during the field test, factor structure, and factor analyses. The results of the factor analyses and other psychometric measures supported a 12-item, one-factor scale for assessing teachers’ knowledge of the nature of mathematical modeling. The reliability of the MMKS was moderately high and acceptable (α = .84). The findings suggest the MMKS is a reliable, valid, and useful tool to measure teachers’ knowledge of the nature of mathematical modeling. Potential uses and applications of the MMKS by researchers and educators are discussed, and implications for further research are provided.

Abstract

This study addresses a gap in the literature on mathematical modeling education by developing the mathematical modeling knowledge scale (MMKS). The MMKS is a quantitative tool created to assess teachers’ knowledge of the nature of mathematical modeling. Quantitative instruments to measure modeling knowledge is scare in the literature partially due to the lack of appropriate instruments developed to assess such knowledge among teachers. The MMKS was developed and validated with a total sample of 364 K–12 teachers from several public-schools using three phases. Phase 1 addresses content validity of the scale using reviews from experts and interviews with knowledgable teachers. Initial psychometric properties and piloting results are presented in phase 2 of the study, and phase 3 reports on the findings during the field test, factor structure, and factor analyses. The results of the factor analyses and other psychometric measures supported a 12-item, one-factor scale for assessing teachers’ knowledge of the nature of mathematical modeling. The reliability of the MMKS was moderately high and acceptable (α = .84). The findings suggest the MMKS is a reliable, valid, and useful tool to measure teachers’ knowledge of the nature of mathematical modeling. Potential uses and applications of the MMKS by researchers and educators are discussed, and implications for further research are provided.

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Bu Makaleye Atıf Yap

ASEMPAPA, R. S. (2022). The Development of Teachers’ Knowledge of the Nature of Mathematical Modeling Scale. International Journal of Assessment Tools in Education, Cilt: 7 - Sayı: 2, 236–254. https://ajindex.com/articles/540118

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Kaynakça (10)

  1. Ang, K. C. (2015). Mathematical modelling in Singapore schools: A framework for instruction. In N. H. Lee & K. E. D. Ng (Eds.), Mathematical modelling: From theory to practice (pp. 57–72). Singapore: World Scientific. Asempapa, R. S., & Foley, G. D. (2018). Classroom assessment of mathematical modeling tasks. In M. Shelley & S. A. Kiray (Eds.), Education Research Highlights in Mathematics, Science, and Technology 2018 (pp. 6–20). Ames; IA. International Society for Research in Education and Science (ISRES). Ball, D. L. (1990). The mathematical understandings that prospective teachers bring to teacher education. Elementary School Journal, 90, 449–466.
  2. Ball, D. L., Thames, M. H., & Phelps, G. (2008). Content knowledge for teaching: What makes it special? Journal of Teacher Education, 59, 389–407.
  3. Benjamin, T. E., Marks, B., Demetrikopoulos, M. K., Rose, J., Pollard, E., Thomas, A., & Muldrow, L. L. (2017). Development and validation of scientific literacy scale for college preparedness in STEM with freshmen from diverse institutions. International Journal of Science and Mathematics Education, 15(4), 607–623.
  4. Blum, W. (2015). Quality teaching of mathematical modelling: What do we know, what can we do? In S. J. Cho (Ed.), Proceedings of the 12th International Congress on Mathematical Education: Intellectual and attitudinal challenges (pp. 73–96).
  5. New York, NY: Springer. Blum, W., & Borromeo Ferri, R. (2009). Mathematical modelling: Can it be taught and learnt? Journal of Mathematical Modelling and Application, 1, 45–58.
  6. Blum, W., & Leiss, D. (2007). How do students and teachers deal with modelling problems? In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modelling: Education, engineering and economics–ICTMA 12 (pp. 222–231).
  7. Chichester, United Kingdom: Horwood. Boaler, J. (2001). Mathematical modelling and new theories of learning. Teaching Mathematics and Its Applications, 20, 121–127.
  8. Borromeo, F. R. (2018). Learning how to teach mathematical modeling in school and teacher education. Picassoplatz, Switzerland: Springer. https://doi.org/10.1007/978-3-319-68072-9
  9. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: Guilford Press. Consortium for Mathematics and Its Applications [COMAP] and Society for Industrial and Applied Mathematics [SIAM] (2016). Guidelines for assessment and instruction in mathematical modeling education. Retrieved from http://www.comap.com/Free/GAIMME/
  10. Converse, J. M., & Pres