Ortaokul öğrencilerinin istatistiksel düşünme seviyelerinin M3ST modeline göre incelenmesi

Bu çalışmada ortaokul öğrencilerinin istatistiksel düşünme seviyeleri, SOLO taksonomisinden temellerini alan M3ST istatistiksel düşünme modeli ile incelenmiştir. Bu amaçla farklı sınıf seviyelerinden olan ortaokul öğrencileri ile çalışılmıştır. Kullanılan istatistiksel düşünme modeli dört bileşenden oluşmaktadır. Bu bileşenler verinin tanımlanması, verinin organize edilmesi ve indirgenmesi, veri gösterimi, verinin analiz edilmesi ve yorumlanmasıdır. Çalışmanın örneklemini farklı sınıf seviyesinde ortaokul öğrencilerinden toplam 90 öğrenci oluşturmaktadır. Ortaokul istatistik öğrenme alanındaki kazanımları ve literatürdeki çalışmalar göz önünde bulundurularak bir veri toplama aracı hazırlanmıştır. Uzman görüşleri doğrultusunda veri toplama aracındaki açık uçlu ve çoktan seçmeli sorulara son şekli verilmiştir. Öğrencilerin test maddelerine vermiş oldukları cevaplar analiz edilmiş, cevapların hangi istatistiksel düşünme seviyesinde yer aldığı ve öğrencilerin sınıf seviyeleri ile istatistiksel düşünme seviyeleri arasındaki ilişki olup olmadığı araştırılmıştır. Elde dilen bulgulara göre ortaokul öğrencilerinin genel olarak verinin tanımlanması bileşeninde dördüncü seviyede, diğer üç bileşen olan verinin organize edilmesi ve indirgenmesi, veri gösterimi, verinin analiz edilmesi ve yorumlanması bileşenlerinde birinci seviyede yoğunlaştığı görülmüştür. Özellikle 6. sınıftan 7. sınıfa geçişte istatistiksel düşünmede gelişim ve değişimin daha hızlı olduğu saptanmıştır. Öğrencilerin öğrenim gördüğü sınıf seviyeleri ile istatistiksel düşünme seviyeleri arasında anlamlı bir ilişki olduğu sonucuna varılmıştır.

According to the M3ST model analyze of the statistical thinking levels of middle school students

In this study, the statistical thinking levels of middle school students have been examined by using M3ST statistical thinking model based on SOLO taxonomy. This model consist of 4 compenents. Definition of data, organization and reduction of data, representation of data, analyzing and interperetation of data. 90 middle school students from different classes participated in the study. In accordance with the statistical acqusitions of middle school education, open-ended and multiple choice questions have been prepared by analyzing the questions in the literature and taking opinions of professionals. Analyzing the responses of students’ response, the levels of students have been searched according to the statistical thinking model. According the findings, the middle school students’ levels are in the fourth level in the definition of data, it has been understood that they are in the first level in the other statistical thinking components. The students are reach higher level when reach higher class. Development and change is occur guickly particularly between 6 and 7 class. The results of the study revealed that there is a significant relationship between the grades of students and the levels of statistical thinking. Results are compared other research in this field.

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  • American Statistical Association, (2005). Guidelines for assessment and instruction in statistics education college report. http://www.amstat.org/education/gaise/GAISECollege.htm.
  • Ben-Zvi, D. (2000). Towards understanding the role of technological tools in statistical learning. Mathematical Thinking and Learning, 2, 127–155.
  • Ben-Zvi, D. (2002). Seventh grade students sense making of data and data representations. In B. Phillips (Ed.), Proceedings of the Sixth International Conference on Teaching of Statistics, Cape Town, South Africa. Voorburg, The Netherlands: International Statistical Institute.
  • Ben-Zvi, D., Arcavi, A. (2001). Junior high school students construction of global views of data anddata representations. Educational Studies in Mathematics, 45, 35-65.
  • Ben-Zvi, D., Friedlander, A. (1997). Statistical thinking in a technological environment. In J. Garfield and G. Burrill (Eds.), Research on the role of technology in teaching and learning statistics. Voorburg, The Netherlands: International Statistical Institute., 45-55.
  • Berg, C.A. ve Phillips, D.G., (1994). An investigation of the relationship between logical thinking structures and the ability to construct and interpret line graphs Journal of Research in Science Teaching, 31, 323–344
  • Biggs, J., Collis, K. (1982). Evaluating the quality of learning: The SOLO taxonomy (Structure of the observed learning outcome). New York: Academic.
  • Bright, G. W., Friel, S. N. (1998). Interpretation of data in a bar graph by students in grade 6 and 8. Paper presented at the annual meeting of America Educational Research Association, San Diego, CA.
  • Callingham, RA and Watson, JM. (2004). A Developmental Scale of Mental Computation with Part- Whole Numbers, Mathematics Education Research Journal, 16, (2). 69–96.
  • Chance, B. L.(2002).Components of statistical thinking and implications for instruction and assesment. Journal of Statistics Education. http://www.amstat.org/publications/jse/v10n3/chance.html
  • Curcio, F.R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18, 382-393.
  • Friel, S.N., Curcio, F.R., Bright, G.W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 32, 124–158.
  • Gal, I. (2002). Adult statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25.
  • Garfield, J., Gal, I. (1999). Assessment and statistics education: Current challenges and directions. International Statistical Review, 67(1), 1–12.
  • 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.
  • Güven, B., Özmen, Z.M., Öztürk, T. (2012). Gerçek Yaşam Durumları İle İlgili Veri Temsil Süreçlerinin İncelenmesi. X. Fen Bilimleri ve Matematik Eğitimi Kongresi, Niğde.
  • Hoerl, R.W., Snee, R.D. (2001). Statistical thinking: Improving business performance. Pacific Grove, CA: Duxbury.
  • Jones, G.A., Thornton C.A., Langrall, C.W., Mooney, E.S., Perry, B. ve Putt, I.J., (2000), A Framework for Characterizing Children’s Statistical Thinking, Mathematical Thinking and Learning, 2, 4, 269-307.
  • Kaptan, S. (1998). Bilimsel araştırma ve istatistik teknikleri (11.Baskı). Ankara. Tek Işık Web Ofset.
  • Kaynar Y., Halat, E. (2012). İlköğretim II.. Kademe Matematik Öğretim Programının “Olasılık ve İstatistik” Alt Öğrenme Alanının “İstatistik” Boyutunun İncelenmesi X. Fen Bilimleri ve Matematik Eğitimi Kongresi, Niğde.
  • Koparan T. (2013). İstatistiksel Düşünme Modellerinin İncelenmesi. İlköğretim Online, 12(3), 730–739, http://ilkogretim-online.org.tr
  • Leinhardt, G., Zaslavsky, O., Stein, M.K. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60 (1), 1–64.
  • Mevarech, Z. R. ve Kramarsky, B., (1997) From verbal descriptions to graphic representations: Stability and change in students’ alternative conceptions Educational Studies in Mathematics, 32, 229- 263.
  • Mooney, E.S. (2002). Development of a middle school statistical thinking framework. Submitted for publication, Mathematical Thinking and Learning, 4, 1, 23–63.
  • National Council of Teachers of Mathematics. (2000). Principles and Standards for School Mathematics. Reston, VA: NCTM.
  • Pereira-Mendoza, L., Mellor, J. (1991). Students’concepts of bar graphs: Some preliminary findings. In D. Vere- Jones (Ed.), Proceedins of the Third International Conference on Teaching Statistics: 1,150–157. The Netherlands: International Statistical Institute.
  • Rumsey, D. J. (2002). Journal of Statistics Education, 10 (2). www.amstat.org/publications/jse/v10n2/rumsey.html
  • Shaughnessy, J. M., Zawojewski, J.S. (1999). Secondary students' performance on data and chance in the 1996 NAEP. The Mathematics Teacher, 92, 713 – 718.
  • Shaughnessy, J. M., Garfield, J., Greer, B. (1996). Data handling. In A. J. Bishop, K. Clements, C. Keitel, J. Kilpatrick, C. Laborde (Eds.), International handbook of mathematics education 1, 205–237. Dordrecht. Netherlands: Kluver.
  • Temiz, B. K. ve Tan, M. (2009). Lise 1. Sınıf Öğrencilerinin Grafik Yorumlama Becerileri, Selçuk Üniversitesi Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, (28), 31–43.
  • Uçar, T. Z. ve Akdoğan, N. E. (2009). İlköğretim 6–8. Sınıf Öğrencilerinin Ortalama Kavramına Yüklediği Anlamlar, İlköğretim Online, 8(2), 391–400
  • Wainer, H. (1992). Understanding graphs and tables. Educational Researcher, 21 (1), 14–23.
  • Wallman, K. K. (1993). Enhancing statistical literacy: Enriching our society. Journal of the American Statistical Association, 88, 1–8.
  • Watson J. M. (2006). Statistical Literacy at School, Growth and Goal. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Londan.
  • Watson, J. M., Mortiz, J. B. (2001). The role of cognitive conflict in developing students’ understanding of chance measurement. In J. Bobis, B. Perry, M. Mitchelmore (Eds.), Numeracy and beyond, 523–530. Sydney: MERGA.
  • Wild, C. J., Pfannkuch, M. (1999). Statistical thinking in empirical enquiry (with discussion). International Statistical Review, 67(3), 223–265.
Eğitim ve Bilim-Cover
  • ISSN: 1300-1337
  • Yayın Aralığı: Yılda 4 Sayı
  • Yayıncı: Türk Eğitim Derneği (TED) İktisadi İşletmesi