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Öz

The purpose of this study is to determine the statistical literacy levels of performance related to sampling concept of middle school students with the Partial Credit Model in Rasch measurement. Within this scope, a Statistical Literacy Test for the Sampling Concept (OKYIOT), which is composed of 12 open ended questions was developed. A total of 60 middle school students in Trabzon in 2010-2011 academic year participated in the study. Firstly questions created about sampling and according to statistical literacy hierarchy rubrics were created. Students were asked to answer questions related to sampling. Partial Credit Model was used as the measurement model for the examination of construct validity. All analyses were completed using Winsteps 3.72 computer software. Reliability figures were also satisfactory (RI = 0,91; RP = 0,81; Cronbach alpha = 0,80). The results of this study demonstrated that works in accordance with the purpose of OKYIOT test items and student responses. In this study students often concentrated on the third level of statisticial literacy framework. This result is compatible with other studies.

Anahtar Kelimeler

• Statistical Literacy, Statistics Education, Rasch Measurement Method, Middle School Students

8. Sınıf Öğrencilerinin Örneklem Kavramına Yönelik İstatistiksel Okuryazarlık Seviyelerinin Belirlenmesi

Öz

Bu çalışma ile ortaokul 8. sınıf öğrencilerinin örneklem kavramına yönelik istatistiksel okuryazarlık seviyelerinin Rasch ölçme yöntemlerinden kısmi puan modeli kullanılarak belirlenmesi amaçlanmıştır. Bu amaçla uzman görüşleri doğrultusunda 12 açık uçlu sorudan oluşan Örneklem Kavramına Yönelik İstatistiksel Okuryazarlık Testi (ÖKYİOT) geliştirilmiştir. Geliştirilen bu test 2010-2011 Eğitim Öğretim yılında Trabzon’da bir ortaokulda okuyan toplam 60 sekizinci sınıf öğrencisine uygulanmıştır. Bu araştırmada Watson ve Callingham (2003) istatistiksel okuryazarlık modeli kullanılarak elde edilen verilerin analizi Rasch ölçümü yapan Winsteps 3.72 bilgisayar programı ile yapılmıştır. Yapılan analizler sonucunda araştırmaya katılan öğrencilerin örneklem kavramına yönelik istatistiksel okuryazarlık seviyelerinin çoğunlukla üçüncü seviyede bulunduğu görülmüştür.

Anahtar Kelimeler

• İstatistiksel Okuryazarlık, İstatistik Eğitimi, Ortaokul Öğrencileri, Rasch Modeli

Kaynakça

1. Adams, R.J. (1988). Applying the partial credit model to educational diagnosis. Applied Measurement in Education, 1(4), 347–361.
2. Berberoğlu, G. (1988). Seçme amacıyla kullanılan testlerde Rasch modelinin katkıları. Yayınlanmamış Doktora Tezi. Hacettepe Üniversitesi, Ankara.
3. Bond, T. G., Fox, C. M. (2001). Applying the rasch model; fundamental measurement in the human sciences. Mahwah New Jersey: Lawrence Erlbaum Associates.
4. Bond, T. G., Fox, C. M. (2007). Applying the Rasch model. Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
5. Callingham, R., Watson, J. M. (2005). Measuring statistical literacy. Journal of Applied Measurement, 6 (1), 29, 19–47.
6. Çepni, (2008), Kuramdan Uygulamaya Fen ve Teknoloji Öğretimi (7. Baskı), Pegem A yayınları, Ankara.
7. Dodd, B.G. (1984). Attitude scaling: A comparison of the graded response and partial credit latent trait models (Doctoral Dissertation, University of Texas at Austin, 1984). Dissertation Abstracts International, 45, 2074A.
8. Dodd, B.G., Koch, W.R. (1987). Effects of variations in item stop values on item and test information in the partial credit model. Applied Psychological Measurement, 11, 371–384.

9. Elhan A. H, Atakurt Y. (2005). Ölçeklerin değerlendirilmesinde niçin Rasch analizi kullanılmalıdır? Ankara Üniversitesi Tıp Fakültesi Mecmuası. 58, 47–50
10. Englehard, G., Jr. (1990). Thorndike, Thurstone and Rasch: A comparison of their approaches to item-invariant measurement. American Educational Research Association Conference, Boston.
11. Haney. W., Madaus. G. (1989). Searching for alternatives to standartdized tests: Why, whats and whithers. Phi Delta Kapan, 70(9), 683–687
12. Izard, J., Haines, C., Crouch, R., Houston, S., Neill, N. (2003). Assessing the impact of the teaching of modelling: Some implications. In S. Lamon, W. Parker, K. Houston (Eds.), Mathematical Modelling: A Way of Life: ICTMA 11, (165–177). Chichester: Horwood Publishing.
13. Kaptan, S. (1998). Bilimsel Araştırma Teknikleri ve İstatistik Yöntemleri. 11. Baskı Ankara.
14. Koparan, T., Güven, B. (2014). Proje Tabanlı Öğrenme Yaklaşımının Öğrencilerin Olasılık Kavramına Yönelik İstatistiksel Okuryazarlık Seviyelerine Etkisi. Eğitim ve Öğretim Araştırmaları Dergisi, 3(1), 07, 60–84.
15. Leavy, A.M., Middleton, J.A. (2001, April). Middle Grade Students Understanding of the Statistical Concept of Distribution. American Educational Research Association Annual Conference in Seattle, Washington, USA.
16. Linacre, J.M. (2011). A user’s guide to WINSTEPS: Rasch model computer programs MESA Pres: Chicago.
17. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrica, 47(2), 149–174.
18. Masters, G.N. (1988). The analysis of partial credit scoring. Applied Measurement in Education, 1(4), 279–297
19. Misailidou, C., Williams, J. (2003). Diagnostic assessment of children’s proportional reasoning. Journal of Mathematical Behaviour, 22, 335–368.
20. Pellerino, J.W., Chudowsky. N., Glaser, R. (Eds.) (2001). Knowing what students know: The science and design of educational assesment. Washington, DC: National Acedemy Pres.
21. Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests (Expanded ed.). Chicago MI: University of Chicago Press.
22. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph. No 17.
23. Shepard, L.A. (2000). The role of classroom assesment in teaching and learning. CSE Technical Report 517. Los Angeles, CA: National Center for Research on Evaluation, Standards, and Students Testing.
24. Van der Linden, W. J., Hambleton, R. K. (1997). Item response theory: Brief history, common models and extensions. Handbook of Modern Item Response Theory. New York: Springer.
25. Watson Jane M. (2006) Statistical Literacy at School, Growth and Goal. Lawrence Erlbaum Assocıates, Publishers. Londan. 27–53.
26. Callingham, R., Watson, J. M. (2005). Measuring statistical literacy. Journal of Applied Measurement, 6 (1), 29, 19–47.
27. Watson, J., Kelly, B., Izard, J. (2004). Student change in understanding of statistical variation after instruction and after two years: An application of Rasch analysis. AARE Conference, Melbourne, Vic http://www.aare.edu.au (search code WAT04867)
28. Wright, B. (1999). Model selection: Rating scale or partial credit?. Rasch Measurement Transactions, 12(3), 641-642.
29. Wright, B., Masters, G. (1982). Rating scale analysis. Chicago: MESA Press.