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The Effect of Project-Based Learning on Students’ Statistical Literacy Levels for Inference

Year 2014, Volume: 2 Issue: 1, 33 - 48, 30.06.2014

Abstract

The aim of this study is to define the effect of project based learning approach on students’ statistical literacy levels for inference. To achieve this aim, a test which consists of 10 open-ending questions in accordance with the views of experts was developed. 70 8th grade middle school students, 35 in experimental group and 35 in control group, took this test twice, one before the application and one after the application. All the raw scores were turned into linear points by using the Winsteps 3.72 modelling program that makes the Rash analysis and Ancova analysis were carried out with the linear points. Depending on the findings, there is no significant difference in pre-test and post-test scores on both control and experimental groups. Students’ levels of statistical literacy before and after the application were shown through the obtained person-item maps. According to the results obtained was not observed a significant difference between the experimental and control groups

References

  • Berberoğlu, G. (1988). Seçme amacıyla kullanılan testlerde Rasch modelinin katkıları. Yayınlanmamış Doktora Tezi. Hacettepe Üniversitesi, Ankara.
  • Ben-Zvi, D., Garfield, J. (2008). Introducing the emerging discipline of statistics education. School Science and Mathematics, 108, 355–361.
  • Biggs, J., Collis, K. (1982). Evaluating the quality of learning: The SOLO taxonomy. New York, NY: Academic Press.
  • Bond, T. G., Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Lawrence Erlbaum Associates, Inc: Mahwah, New Jersey.
  • Bond, T. G., Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
  • Buck Institute for Education, (2010). What is PBL? http://www.bie.org/about/what_is_pbl
  • Carnell, L.J. (2008). The effect of a student-designed data collection project on attitudes towards statistics. Journal of Statistics Education, 16(1).
  • Curcio, F.R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18, 382–393.
  • Çepni, S. (2007). Araştırma ve Proje Çalışmalarına Giriş, Üçüncü Baskı, Trabzon.
  • Elhan A. H, Atakurt Y. (2005). Ölçeklerin değerlendirilmesinde niçin Rasch analizi kullanılmalıdır? Ankara Üniversitesi Tıp Fakültesi Mecmuası 2005; 58.47–50.
  • GAISE (2005). Guidelines for assessment and instruction in statistics education (GAISE) report: A curriculum framework for PreK–12 statistics education. The American Statistical Association (ASA). www.amstat.org/education/gaise
  • Garfield, J. (1993). "An Authentic Assessment of Students' Statistical Knowledge," in National Council of Teachers of Mathematics 1993 Yearbook: Assessment in the Mathematics Classroom, ed. N. Webb, Reston, VA: NCTM, 187– 196.
  • Garfield, J. (1995). How students learn statistics. International Statistical Review, 63(1). 25–34.
  • Garfield, J., & Ahlgren, A. (1998). Difficulties in learning basic concepts in probability and statistics: Implications for research. Journal for Research in Mathematics Education, 19, 44-63.
  • Gordon, F. S., & Gordon, S. P. (1992). Sampling + Simulation = Statistical Understanding. In F. S. Gordon (Ed.), Statistics for the twenty-first century, 207-216. Washington, DC: The Mathematical Association of America
  • 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.
  • Harris, J. H. ve Katz, L. G. (2001). Young investigators: The project approach in the early years. New York: Teachers College Press.
  • Koparan, T., Güven, B. (2013). Proje tabanlı öğrenme yaklaşımının ilköğretim 8. sınıf öğrencilerinin örneklem kavramına yönelik istatistiksel okuryazarlık seviyelerine etkisi. Eğitim ve Öğretim Araştırmaları Dergisi, 2(1), 185–196.
  • 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.
  • Korkmaz, H. (2002). Fen Eğitiminde Proje Tabanlı Öğrenmenin Yaratıcı Düşünme, Problem Çözme ve Akademik Risk Alma Düzeylerine Etkisi. Yayınlanmamış Doktora Tezi, Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara
  • Misailidou, C. & Williams, J. (2003). Diagnostic assessment of children’s proportional reasoning. Journal of Mathematical Behaviour, 22, 335–368.
  • Moursund, D. (1999). Project Based Learning Using Information Technology, Eugene, Canada.
  • National Council of Teachers of Mathematics (2000). Principles and standards for school mathematics. http://standards.nctm.org.
  • Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests (Expanded ed.), Chicago MU: University of Chicago Press.
  • Roberts, H. V. (1992), "Student-Conducted Projects in Introductory Statistics Courses," in Statistics for the Twenty- First Century, eds. Florence Gordon and Sheldon Gordon, MAA Notes No. 26, Washington, DC: Mathematical Association of America, 109–121.
  • Shaughnessy J. M., Ciancetta M., Best K. ve Canada D. (2004). Students’ attention to variability when comparing distributions. Paper presented at the 82nd Annual Meeting of the National Council of Teachers of Mathematics, Philadelphia, PA.
  • Watson, J., Callingham, R. (2003). Statistical literacy: A complex hierarchical construct Statistics Education Research Journal, 2, 3–46.
  • Watson J. M. (2006). Statistical Literacy at School, Growth and Goal. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Londan.
  • 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. Refereed paper presented at the AARE Conference, Melbourne, Vic. http://www.aare.edu.au/pages/index.asp
  • Hunter, W. G. (1977), "Some Ideas About Teaching Design of Experiments, with 2^5 Examples of Experiments Conducted by Students," The American Statistician, 31, 12–17.
  • Jung, H., Jun.,W., L. Gruenwald. (2001). A Design and Implementation of Web-Based Project-Based Learning Support Systems. www.cs.ou.edu/~database/documents/jjg01.pdf
  • Linacre, J. M. (2011). A user’s guide to WINSTEPS: Rasch model computer programs. MESA Pres: Chicago. http://www.winsteps.com
  • Lajoie, S. P., & Romberg, T. A. (1998). Identifying an agenda for statistics instruction and assessment in K–12. In S. P. Lajoie (Ed.), Reflections on statistics: Learning, teaching, and assessment in grades K–12, vii–xxi. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Leinhardt, G. Zaslavsky, O., & Stein, M.K. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60(1), 1-64.
  • Love, T. E. (1998). A project-drive second course. Journal of Statistics Education, 6(1).
  • Mooney, E. S. (2002). Development of a middle school statistical thinking framework. Submitted for publication, Mathematical Thinking and Learning, 4, 1, 23–63.
  • Moritz, J.B. (2004). Reasoning about covariation. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking, 227-255. Dordrecht: Kluwer Academic Publishers.
  • Roseth, C. J., Garfield, J. B. ve Ben-Zvi, D. (2008). Collaboration in learning and teaching statistics. Journal of Statistics Education, 16(1). www.amstat.org/publications/jse/v16n1/roseth.html
  • Rubin, A., Bruce, B. ve Tenney, Y. (1990, April). Learning about sampling: Trouble at the core of statistics. Paper Presented at the Annual Meeting of the American Educational Research Association, Boston.
  • Rubin, A., Hammerman, J. ve Konold, C. (2006). Exploring informal inference with interactive visualization software. In A. Rossman & B. Chance (Eds.), Working cooperatively in statistics education: Proceedings of the Seventh International Conference on Teaching Statistics, Salvador, Brazil. www.stat.auckland.ac.nz/~iase/publications/17/2D3_RUBI.pdf
  • Shaughnessy, J. M. & Zawojewski, J. S. (1999). Secondary students’ performance on data and chance in the 1996 NAEP. The Mathematics Teacher, 92, 713–718.
  • Wainer, H. (1992). Understanding graphs and tables. Educational Researcher, 21(1), 14–23.
  • Van der Linden, W. J., Hambleton, R. K. (1997). Item response theory: Brief history, common models and extensions. In van der Linden, W. J. & Hambleton, R. K. (Eds.), Handbook of Modern Item Response Theory. New York: Springer.

Proje Tabanlı Öğrenmenin Öğrencilerin Çıkarıma Yönelik İstatistiksel Okuryazarlık Seviyelerine Etkisi

Year 2014, Volume: 2 Issue: 1, 33 - 48, 30.06.2014

Abstract

Bu çalışmanın amacı proje tabanlı öğrenme yaklaşımının ortaokul 8. sınıf öğrencilerinin çıkarıma yönelik istatistiksel okuryazarlık seviyelerine etkisini belirlemektir. Bu amaçla öğrencilerin çıkarıma yönelik istatistiksel okuryazarlık seviyelerini belirlemek için uzman görüşleri doğrultusunda 10 açık uçlu sorudan oluşan bir veri toplama aracı geliştirilmiştir. Geliştirilen bu veri toplama aracı 35’i deney grubu, 35’i kontrol grubu olmak üzere toplam 70 ortaokul 8. sınıf öğrencisine uygulama öncesi ve uygulama sonrası olmak üzere iki kez uygulanmıştır. Testlerden elde edilen tüm ham puanlar Rasch analizi yapan Winsteps 3.72 modelleme programı ile lineer puanlara dönüştürülmüş ve lineer puanlar ile Ancova analizi yapılmıştır. Elde edilen bulgulara göre proje tabanlı öğrenme yaklaşımının öğrencilerin çıkarıma yönelik istatistiksel okuryazarlık seviyelerini etkilemediği sonucuna varılmıştır. Öğrencilerin uygulama öncesi ve uygulama sonrası istatistiksel okuryazarlık seviyeleri elde edilen kişi madde haritaları ile ortaya konmuştur

References

  • Berberoğlu, G. (1988). Seçme amacıyla kullanılan testlerde Rasch modelinin katkıları. Yayınlanmamış Doktora Tezi. Hacettepe Üniversitesi, Ankara.
  • Ben-Zvi, D., Garfield, J. (2008). Introducing the emerging discipline of statistics education. School Science and Mathematics, 108, 355–361.
  • Biggs, J., Collis, K. (1982). Evaluating the quality of learning: The SOLO taxonomy. New York, NY: Academic Press.
  • Bond, T. G., Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Lawrence Erlbaum Associates, Inc: Mahwah, New Jersey.
  • Bond, T. G., Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
  • Buck Institute for Education, (2010). What is PBL? http://www.bie.org/about/what_is_pbl
  • Carnell, L.J. (2008). The effect of a student-designed data collection project on attitudes towards statistics. Journal of Statistics Education, 16(1).
  • Curcio, F.R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18, 382–393.
  • Çepni, S. (2007). Araştırma ve Proje Çalışmalarına Giriş, Üçüncü Baskı, Trabzon.
  • Elhan A. H, Atakurt Y. (2005). Ölçeklerin değerlendirilmesinde niçin Rasch analizi kullanılmalıdır? Ankara Üniversitesi Tıp Fakültesi Mecmuası 2005; 58.47–50.
  • GAISE (2005). Guidelines for assessment and instruction in statistics education (GAISE) report: A curriculum framework for PreK–12 statistics education. The American Statistical Association (ASA). www.amstat.org/education/gaise
  • Garfield, J. (1993). "An Authentic Assessment of Students' Statistical Knowledge," in National Council of Teachers of Mathematics 1993 Yearbook: Assessment in the Mathematics Classroom, ed. N. Webb, Reston, VA: NCTM, 187– 196.
  • Garfield, J. (1995). How students learn statistics. International Statistical Review, 63(1). 25–34.
  • Garfield, J., & Ahlgren, A. (1998). Difficulties in learning basic concepts in probability and statistics: Implications for research. Journal for Research in Mathematics Education, 19, 44-63.
  • Gordon, F. S., & Gordon, S. P. (1992). Sampling + Simulation = Statistical Understanding. In F. S. Gordon (Ed.), Statistics for the twenty-first century, 207-216. Washington, DC: The Mathematical Association of America
  • 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.
  • Harris, J. H. ve Katz, L. G. (2001). Young investigators: The project approach in the early years. New York: Teachers College Press.
  • Koparan, T., Güven, B. (2013). Proje tabanlı öğrenme yaklaşımının ilköğretim 8. sınıf öğrencilerinin örneklem kavramına yönelik istatistiksel okuryazarlık seviyelerine etkisi. Eğitim ve Öğretim Araştırmaları Dergisi, 2(1), 185–196.
  • 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.
  • Korkmaz, H. (2002). Fen Eğitiminde Proje Tabanlı Öğrenmenin Yaratıcı Düşünme, Problem Çözme ve Akademik Risk Alma Düzeylerine Etkisi. Yayınlanmamış Doktora Tezi, Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara
  • Misailidou, C. & Williams, J. (2003). Diagnostic assessment of children’s proportional reasoning. Journal of Mathematical Behaviour, 22, 335–368.
  • Moursund, D. (1999). Project Based Learning Using Information Technology, Eugene, Canada.
  • National Council of Teachers of Mathematics (2000). Principles and standards for school mathematics. http://standards.nctm.org.
  • Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests (Expanded ed.), Chicago MU: University of Chicago Press.
  • Roberts, H. V. (1992), "Student-Conducted Projects in Introductory Statistics Courses," in Statistics for the Twenty- First Century, eds. Florence Gordon and Sheldon Gordon, MAA Notes No. 26, Washington, DC: Mathematical Association of America, 109–121.
  • Shaughnessy J. M., Ciancetta M., Best K. ve Canada D. (2004). Students’ attention to variability when comparing distributions. Paper presented at the 82nd Annual Meeting of the National Council of Teachers of Mathematics, Philadelphia, PA.
  • Watson, J., Callingham, R. (2003). Statistical literacy: A complex hierarchical construct Statistics Education Research Journal, 2, 3–46.
  • Watson J. M. (2006). Statistical Literacy at School, Growth and Goal. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Londan.
  • 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. Refereed paper presented at the AARE Conference, Melbourne, Vic. http://www.aare.edu.au/pages/index.asp
  • Hunter, W. G. (1977), "Some Ideas About Teaching Design of Experiments, with 2^5 Examples of Experiments Conducted by Students," The American Statistician, 31, 12–17.
  • Jung, H., Jun.,W., L. Gruenwald. (2001). A Design and Implementation of Web-Based Project-Based Learning Support Systems. www.cs.ou.edu/~database/documents/jjg01.pdf
  • Linacre, J. M. (2011). A user’s guide to WINSTEPS: Rasch model computer programs. MESA Pres: Chicago. http://www.winsteps.com
  • Lajoie, S. P., & Romberg, T. A. (1998). Identifying an agenda for statistics instruction and assessment in K–12. In S. P. Lajoie (Ed.), Reflections on statistics: Learning, teaching, and assessment in grades K–12, vii–xxi. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Leinhardt, G. Zaslavsky, O., & Stein, M.K. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60(1), 1-64.
  • Love, T. E. (1998). A project-drive second course. Journal of Statistics Education, 6(1).
  • Mooney, E. S. (2002). Development of a middle school statistical thinking framework. Submitted for publication, Mathematical Thinking and Learning, 4, 1, 23–63.
  • Moritz, J.B. (2004). Reasoning about covariation. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking, 227-255. Dordrecht: Kluwer Academic Publishers.
  • Roseth, C. J., Garfield, J. B. ve Ben-Zvi, D. (2008). Collaboration in learning and teaching statistics. Journal of Statistics Education, 16(1). www.amstat.org/publications/jse/v16n1/roseth.html
  • Rubin, A., Bruce, B. ve Tenney, Y. (1990, April). Learning about sampling: Trouble at the core of statistics. Paper Presented at the Annual Meeting of the American Educational Research Association, Boston.
  • Rubin, A., Hammerman, J. ve Konold, C. (2006). Exploring informal inference with interactive visualization software. In A. Rossman & B. Chance (Eds.), Working cooperatively in statistics education: Proceedings of the Seventh International Conference on Teaching Statistics, Salvador, Brazil. www.stat.auckland.ac.nz/~iase/publications/17/2D3_RUBI.pdf
  • Shaughnessy, J. M. & Zawojewski, J. S. (1999). Secondary students’ performance on data and chance in the 1996 NAEP. The Mathematics Teacher, 92, 713–718.
  • Wainer, H. (1992). Understanding graphs and tables. Educational Researcher, 21(1), 14–23.
  • Van der Linden, W. J., Hambleton, R. K. (1997). Item response theory: Brief history, common models and extensions. In van der Linden, W. J. & Hambleton, R. K. (Eds.), Handbook of Modern Item Response Theory. New York: Springer.
There are 43 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Timur Koparan This is me

Bülent Güven This is me

Publication Date June 30, 2014
Published in Issue Year 2014 Volume: 2 Issue: 1

Cite

APA Koparan, T., & Güven, B. (2014). Proje Tabanlı Öğrenmenin Öğrencilerin Çıkarıma Yönelik İstatistiksel Okuryazarlık Seviyelerine Etkisi. Karaelmas Eğitim Bilimleri Dergisi, 2(1), 33-48.