Matematik ve Teknoloji Tutum Ölçeğinin Türkçeye Uyarlanması: Matematik Öğretmen Adayları için Geçerlik ve Güvenirlik Çalışması

Öz

Matematik öğretiminde teknoloji kullanımı, soyut kavramların görselleştirilmesi sayesinde öğrencilerin matematiksel kavramları ve kavramlar arası bağları keşfetmelerine ve zamanlarını verimli kullanılmasına katkı sağladığından bu araştırmada, Pierce, Stacey ve Barkatsas (2007) tarafından geliştirilen “Mathematics and Technology Attitude Scale (MTAS)” Matematik ve Teknoloji Tutum Ölçeği’ ni (MTTÖ) Türkçe kültürüne uyarlama amacıyla geçerlik ve güvenirlik analizleri yapılmıştır. Araştırmanın örneklemi, İlköğretim Matematik Öğretmenliği programında öğrenim görmekte olan 172 öğretmen adayından oluşmaktadır. Yapılan açımlayıcı ve doğrulayıcı faktör analizlerinde ölçeğin orijinal formu ile uyumlu olduğu görülmüştür. Ölçeğin iç tutarlılık katsayılarının .70’ in üzerinde olması, ölçeğin düzeltilmiş madde-toplam korelasyonlarının .41 ile .72 arasında sıralandığı ve alt-üst %27’lik grupların madde puanlarının karşılaştırılmasına ilişkin t-testi sonuçlarının -14.48 ile -3.36 arasında tüm maddelerin anlamlı olarak bulunması ölçeğin güvenirlik ve geçerlik kriterlerini sağladığını göstermektedir.

Anahtar Kelimeler

• Matematik Eğitimi,Ölçek uyarlama,Teknoloji,Tutum,Öğretmen adayı

Adaptation of Mathematics and Technology Attitude Scale (MTAS) into Turkish: Validity and Reliability Studies for Preservice Mathematics Teachers

Abstract

The use of technology in mathematics teaching, through the visualization of abstract concepts, contributes to the exploration of mathematical concepts and inter-conceptual connections and the efficient use of their time. The purpose of the study is to adapt Mathematics and Technology Attitudes Scale (MTAS) to Turkish and to implement validity and reliability studies of the scale. The sample consisted of 172 pre-service teachers who are enrolled in Elementary Mathematics Education Program of one of the state universities. Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) were conducted and it was found that the scale is compatible with the original scale. The fact that the internal consistency coefficient of the scale was above .70, corrected item-total correlations ranged from .41 to .72, and t-test results for the comparison of the item scores of the bottom 27% and top 27% were significant between -14.48 and -3.36 for all items indicates the validity and reliability criteria of the scale.

Keywords

• Mathematics Education,Scale Adaptation,Technology,Attitude,Pre-service Teacher

Kaynakça

1. Artigue, M. (2011). Les défis de l’enseignement des mathématiques dans l’éducation de base. Paris: UNESCO. Retrouvé à l’adresse http://unesdoc.unesco.org/images/0019/001917/191776f.pdf
2. Association of Mathematics Teacher Educators [AMTE]. (2006). Preparing teachers to use technology to enhance the learning of mathematics: A position of the Association of Mathematics Teacher Educators. Retrieved from https://amte.net/sites/default/files/amtetechnologypositionstatement.pdf
3. Brownlow, C. (2004). SPSS explained. London: Routledge.
4. Bryman, A. ve Cramer, D. (2001). Quantitative data analysis with SPSS release 10 for Windows: A guide for social scientists. London: Routledge.
5. Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. Ankara: Pegem Yayınları.
6. Chevallard, Y. (1998). À propos des TICE : Transmission et appropriation du savoir, nouveaux rôles de l’enseignant, organisation de l’établissement, Communication à l’université d'été à Toulouse, 26-28 août 1998. Retrouvé à l’adresse http://yves.chevallard.free.fr/spip/spip/article.php3?id_article=32
7. Child, D. (2006). The essentials of factor analysis. London: Continuum International Publishing Group.
8. Duru, A., Peker, M. ve Akçakın, V. (2010). Lise öğrencilerinin bilgisayar destekli matematik öğrenmeye yönelik tutumları. Turkish Journal of Computer and Mathematics Education, 1(3), 264-284.

9. Escuder, A. (2011). GeoGebra in the math classroom. In M. Koehler & P. Mishra (Eds.), Proceedings of SITE 2011-Society for Information Technology & Teacher Education International Conference (pp. 3970-3974). Nashville, Tennessee, USA: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/p/36952/
10. Escuder, A. ve Furner, J. M. (2011). The impact of geogebra in math teachers' professional development. In P. Bogacki et al. (Eds.), Proceedings of International Conference on Technologies in Collegiate Mathematics (pp. 76-84). Norfolk, USA: Department of Mathematics and Statistics Old Dominion University. Retrieved from http://archives.math.utk.edu/ICTCM/VOL23/S113/paper.pdf
11. Eyyam, R. ve Yaratan, H. (2014). Impact of use of technology in mathematics lessons on student achievement and attitudes. Journal of Social Behavior and Personality, 42, 31-42.
12. Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
13. Fraenkel, J. R., Wallen, N. E. ve Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: Mc Graw Hill.
14. Goldenberg, P. (2000). Thinking (and talking) about technology in math classrooms. Issues in Mathematics Education, Education Development Center, Inc. Retrieved from http://mcc.edc.org/pdf/iss_tech.pdf
15. Graf, K. D., Fraser R., Klingen, L. H., Stewart, J. ve Winkelmann, B. (1992). The effect of computers on the school mathematics curriculum. In B. Cornu & A. Ralston (Eds.), The influence of computers and informatics on mathematics and its teaching (pp. 57-79). Paris: UNESCO. Retrieved from http://unesdoc.unesco.org/images/0009/000937/093772eo.pdf
16. Gürbüz, R., Çavuş-Erdem, Z. ve Toprak, Z. (2015). Matematik ve Teknoloji Tutum Ölçeğinin Türkçeye uyarlanması: Lise öğrencilerinin matematik, teknoloji ve tablet kullanımına yönelik tutumları. II. Türk Bilgisayar ve Matematik Eğitimi Sempozyumu, 80, 16-18 Mayıs 2015, Adıyaman: Adıyaman Üniversitesi. http://www.bilmat.org/turkbilmat_2015/sempozyum_kitabi.pdf
17. Harrington, D. (2009). Confirmatory factor analysis. New York: Oxford University Press.
18. Heddens, J. W. ve Speer, W. R. (1997). Today’s mathematics (9.Edition). New Jersey: Merrill an Imprint of Prentice-Hall.
19. Higgins, K., Huscroft-D’Angelo, J. ve Crawford, L. (2017). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research, 57(2), 283-319.
20. Hutcheson, G. D. ve Sofroniou, N. (1999). The Multivariate social scientist: Introductory statistics using generalized linear models. Thousand Oaks, CA: Sage.
21. Joint Mathematical Council of the United Kingdom [JMC]. (2011). Digital technologies and mathematics education. Retrieved from https://www.ncetm.org.uk/files/9793653/JMC_Digital_Technologies_Report_2011.pdf
22. Jones, K. (2001). Learning geometrical concepts using dynamic geometry software. In K. Irwin (Ed.), Mathematics education research: A catalyst for change (pp. 50-58). Auckland: University of Auckland.
23. Jones, K., Mackrell, K. ve Stevenson, I. (2010). Designing digital technologies and learning activities for different geometries. In C. Hoyles & J. B. Lagrange (Eds.), The seventeeth ICMI study: Mathematics education and technology-rethinking the terrain (pp. 47-61). New York, NY: Springer.
24. Jöreskog, K. G. ve Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.
25. Laborde, C. (1997). Scénarios d’usage de Cabri-géomètre sur ordinateur ou calculatrice au lycée, In Actes de l’université d’été : Des outils informatiques dans la classe aux calculatrices symboliques et géométriques : quelles perspectives pour l’enseignement des mathématiques ?, pp. 97-103, Rennes, 26-31 août 1996, IREM de Rennes.
26. Laborde, C. (1999). Dynamic geometry environments as a source of rich learning contexts for the complex activity of proving. Educational Studies in Mathematics, 44(1-3), 151-161.
27. Lagrange, J.B. (2000). L’intégration d’instruments informatiques dans l’enseignement : une approche par les techniques. Educational Studies in Mathematics, 43(1), 1-30.
28. Marzano, R. (1992). Different kind of classroom: Teaching with dimensions of learning. Alexandria, VA: Association for Supervision and Curriculum Development.
29. National Council of Teachers of Mathematics [NCTM]. (2000). Principles and standarts for school mathematics. Reston, VA.: National Council of Teachers of Mathematics.
30. Nunnally, J.C. (1978). Psychometric theory. New York: McGraw-Hill.
31. Olive, J., Makar, K., Hoyos, V., Kor, L., Kosheleva, O. ve Straeβer, R. (2010). Mathematical knowledge and practices resulting from access to digital technologies. In C. Hoyles & J.B. Lagrange (Eds.), The seventeeth ICMI study: Mathematics education and technology-rethinking the terrain (pp. 133-177). New York, NY: Springer.
32. Pett, M. A., Lackey, N. R. ve Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.
33. Pierce, R., Stacey, K. ve Barkatsas, A. (2007). A scale for monitoring students' attitudes to learning mathematics with technology. Computers and Education, 48(2), 285 - 300.
34. Ruthven, K. ve Hennessy, S. (2002). A practitioner model of the use of computer-based tools and resources to support mathematics teaching and learning. Educational Studies in Mathematics, 49(1), 47–88.
35. Sinclair, S. ve Bruce, C. D. (2015). New opportunities in geometry education at the primary school. ZDM Mathematics Education, 47(3), 319–329.
36. Tabachnick, B.G. ve Fidell, L.S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.
37. Tabuk, M. (2018). Adaptation of the Mathematics and Technology Attitudes Scale (MTAS) into Turkish: Validity and reliability studies for middle school students. Journal of Education and Training Studies, 6(7), 38-43.