Research Article
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Year 2020, Volume: 11 Issue: 1, 13 - 26, 24.03.2020
https://doi.org/10.21031/epod.562586

Abstract


References

  • Ardıç, E. Ö., Yılmaz, B., & Demir, E. (2012, June). İlköğretim 8. sınıf öğrencilerinin merkezi eğilim ve yayılım ölçüleri hakkındaki istatistiksel okuryazarlık düzeylerinin solo taksonomisine göre incelenmesi. Paper session presented at the meeting of X. Fen Bilimleri ve Matematik Eğitimi Kongresi, Niğde, Türkiye. http://kongre.nigde.edu.tr/xufbmek/dosyalar/tam_metin/pdf/2430-30_05_2012-18_28_56.pdf adresinden edinilmiştir.
  • Arican, M., & Kuzu, O. (2019). Diagnosing preservice teachers’ understanding of statistics and probability: Developing a test for cognitive assessment. International Journal of Science and Mathematics Education. 1-20. Advanced online first. doi: 10.1007/s10763-019-09985-0
  • Atuahene, F., & Russell, T. A. (2016). Mathematics readiness of first-year university students. Journal of Developmental Education, 39(3), 12-20. www.jstor.org/stable/44987415 adresinden edinilmiştir.
  • Batanero, C., & Díaz, C. (2010). Training teachers to teach statistics: What can we learn from research? Statistique et Enseignement, 1(1), 5-20. http://statistique-et-enseignement.fr/article/view/3 adresinden edinilmiştir.
  • Batanero, C., & Díaz, C. (2012). Training school teachers to teach probability: Reflections and challenges. Chilean Journal of Statistics, 3(1), 3-13. http://chjs.mat.utfsm.cl/volumes/03/01/Batanero_Diaz(2012).pdf adresinden edinilmiştir.
  • Batanero, C., Godino, J. D., & Roa, R. (2004). Training teachers to teach probability. Journal of statistics Education, 12(1). http://www.amstat.org/publications/jse/v12n1/batanero.html adresinden edinilmiştir.
  • Boyacıoğlu, H., Erduran, A., & Alkan, H. (1996, September). Permütasyon, kombinasyon ve olasılık öğretiminde rastlanan güçlüklerin giderilmesi. Paper session presented at the meeting of II. Ulusal Eğitim Sempozyumu, Marmara University, İstanbul.
  • Bradshaw, L., Izsak, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers’ understandings of rational numbers: Building a multidimensional test within the diagnostic classification framework. Educational Measurement: Issues and Practice, 33(1), 2-14. doi: 10.1111/emip.12020
  • Bulut, S., Yetkin, İ. E., & Kazak, S. (2002). Matematik öğretmen adaylarının olasılık başarısı, olasılık ve matematiğe yönelik tutumlarının cinsiyete göre incelenmesi. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 22(22), 21-28. https://dergipark.org.tr/en/download/article-file/87889 adresinden edinilmiştir.
  • Çepni, S., Bayrakçeken, S., Yılmaz, A., Yücel, C., Semerci, Ç., Köse, E., …, Gündoğdu, K. (2008). Ölçme ve değerlendirme. Ankara: Pegem Akademi.
  • Chambers, E. A., & Schreiber, J. B. (2004). Girls’ academic achievement: Varying associations of extracurricular activities. Gender and Education, 16(3), 327-346. doi: 10.1080/09540250042000251470
  • Chiesi, F., & Primi, C. (2015, February). Gender differences in attitudes toward statistics: Is there a case for a confidence gap? In K. Krainer & N. Vondrova (Eds.), CERME 9-Ninth congress of the European society for research in mathematics education (pp. 622-628). Prague, Czech Republic: Charles University & ERME.
  • Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about what students have learned: An analysis of TIMSS eighth grade mathematics. Eurasia Journal of Mathematics, Science & Technology Education, 11(6), 1563-1577. doi: 10.12973/eurasia.2015.1421a
  • de la Torre, J. (2008). An empirically based method of Q‐matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45(4), 343-362. doi: 10.1111/j.1745-3984.2008.00069.x
  • de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179-199. doi: 10.1007/s11336-011-9207-7
  • DiBello, L. V., Stout, W. F., & Roussos, L. A. (1995). Unified cognitive/psychometric diagnostic assessment likelihood–based classification techniques. In P. Nichols, S. Chipman, & R. Brennan (Eds.), Cognitively diagnostic assessment (pp. 361-390). Hillsdale, NJ: Lawrence Erlbaum.
  • Dogan, E., & Tatsuoka, K. (2008). An international comparison using a diagnostic testing model: Turkish students’ profile of mathematical skills on TIMSS-R. Educational Studies in Mathematics, 68(3), 263-272. doi: 10.1007/s10649-007-9099-8
  • Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98(1), 198-208. doi: 10.1037/0022-0663.98.1.198
  • Eitle, T. M. (2005). Do gender and race matter? Explaining the relationship between sports participation and achievement. Sociological Spectrum, 25(2), 177-195. doi: 10.1080/02732170590883997
  • Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103-127. doi: 10.1037/a0018053
  • Farooq, M. S., Chaudhry, A. H., Shafiq, M., & Berhanu, G. (2011). Factors affecting students’ quality of academic performance: A case of secondary school level. Journal of Quality and Technology Management, 7(2), 1-14. http://pu.edu.pk/images/journal/iqtm/PDF-FILES/01-Factor.pdf adresinden edinilmiştir.
  • Felson, R. B., & Trudeau, L. (1991). Gender differences in mathematics performance. Social Psychology Quarterly, 54(2), 113-126. doi: 10.2307/2786930
  • Franklin, C., & Mewborn, D. (2006). The statistical education of PreK-12 teachers: A shared responsibility. In G. Burrill (Ed.), NCTM 2006 Yearbook: Thinking and reasoning with data and chance (pp. 335-344). Reston, VA: NCTM.
  • Franklin, C., Kader, G., Mewborn, D. S., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report: A pre-K-12 curriculum framework. Alexandria, VA: American Statistical Association. https://www.amstat.org/asa/files/pdfs/gaise/gaiseprek-12_full.pdf adresinden edinilmiştir.
  • Fryer Jr, R. G., & Levitt, S. D. (2010). An empirical analysis of the gender gap in mathematics. American Economic Journal: Applied Economics, 2(2), 210-40. doi: 10.1257/app.2.2.210
  • Gürbüz, R., Toprak, Z., Yapıcı, H., & Doğan, S. (2011). Subjects perceived as difficult in secondary mathematics curriculum and their reasons. Gaziantep University Journal of Social Sciences, 10(4), 1311-1323. https://dergipark.org.tr/en/download/article-file/223364 adresinden edinilmiştir.
  • Hartz, S. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practice (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign.
  • Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210. doi: 10.1007/s11336-008-9089-5
  • Im, S., & Park, H. J. (2010). A comparison of US and Korean students’ mathematics skills using a cognitive diagnostic testing method: Linkage to instruction. Educational Research and Evaluation, 16(3), 287-301. doi: 10.1080/13803611.2010.523294
  • Jones, G. A. (2005). Exploring probability in school: Challenges for teaching and learning. New York, NY: Springer.
  • Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric Madde response theory. Applied Psychological Measurement, 25(3), 258-272. doi: 10.1177/01466210122032064
  • Karasar, N. (2005). Bilimsel araştırma yöntemleri. Ankara: Nobel Yayınevi
  • Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52-54. doi: 10.5395/rde.2013.38.1.52
  • Lee, Y. S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the US national sample using the TIMSS 2007. International Journal of Testing, 11(2), 144–177. doi: 10.1080/15305058.2010.534571
  • Leighton, J. P., & Gierl, M. J. (2007). Why cognitive diagnostic assessment? In J. P. Leighton & M. J. Gierl (Eds.), Cognitive diagnostic assessment for education (pp. 3-18). Cambridge: Cambridge University Press.
  • Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123-1135. doi: 10.1037/a0021276
  • Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82-105. https://www.stat.auckland.ac.nz/~iase/serj/SERJ8(1).pdf#page=85 adresinden edinilmiştir.
  • Milli Eğitim Bakanlığı. (2013). Ortaokul matematik dersi öğretim programı. Ankara: Milli Eğitim Bakanlığı Talim ve Terbiye Kurulu Başkanlığı.
  • Milli Eğitim Bakanlığı. (2018). Matematik dersi öğretim programı. Ankara: Milli Eğitim Bakanlığı Talim ve Terbiye Kurulu Başkanlığı.
  • Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Amsterdam: International Association for the Evaluation of Educational Achievement. https://pdfs.semanticscholar.org/9802/a1fabea7578ffd251e50bec4ac13831fbca0.pdf adresinden edinilmiştir.
  • Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in mathematics. Boston College: TIMSS & PIRLS International Study. http://timssandpirls.bc.edu/timss2015/international-results/ adresinden edinilmiştir.
  • Muthen, L. K., & Muthen, B. O. (2011). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthen & Muthen.
  • Nichols, P. D., Chipman, S. F., & Brennan, R. L. (Eds.). (2012). Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum.
  • Olpak, Y. Z., Baltaci, S., & Arican, M. (2018). Investigating the effects of peer instruction on preservice mathematics teachers’ achievements in statistics and probability. Education and Information Technologies, 23(6), 2323-2340. doi: 10.1007/s10639-018-9717-3
  • Ravand, H., & Robitzsch, A. (2015). Cognitive diagnostic modeling using R. Practical Assessment, Research & Evaluation, 20(11), 1-12. doi: 10.7275/5g6f-ak15
  • Rupp, A. A., Templin, J. L., & Henson, R. A. (2010). Diagnostic assessment: Theory, methods, and applications. New York, NY: Guilford Press.
  • Saraçbaşı, T., & Kutsal, A. (1987). Betimsel istatistik. Ankara: Hacettepe Üniversitesi.
  • Sen, S., & Arican, M. (2015). A diagnostic comparison of Turkish and Korean students’ mathematics performances on the TIMSS 2011 assessment. Journal of Measurement and Evaluation in Education and Psychology, 6(2), 238-253. doi: 10.21031/epod.65266
  • Shaughnessy, J. M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957-1009). Reston, VA: The National Council of Teachers of Mathematics.
  • Stoet, G., & Geary, D. C. (2013). Sex differences in mathematics and reading achievement are inversely related: Within-and across-nation assessment of 10 years of PISA data. PloS One, 8(3), 1-253. doi: 10.1371/journal.pone.0057988
  • Templin, J., & Bradshaw, L. (2013). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification, 30(2), 251-275. doi: 10.1007/s00357-013-9129-4
  • Templin, J., & Henson, R. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305.
  • Tsakiridou, H., & Vavyla, E. (2015). Probability concepts in primary school. American Journal of Educational Research, 3(4), 535-540. doi: 10.12691/education-3-4-21
  • Ulutaş, F., & Ubuz, B. (2008). Matematik eğitiminde araştırmalar ve eğilimler: 2000 ile 2006 yılları arası. İlköğretim Online, 7(3), 614-625. http://ilkogretim-online.org.tr/index.php/io/article/view/1751/1587 adresinden edinilmiştir.
  • von Davier, M. (2005). A general diagnostic model applied to language testing data (ETS Research Report No. RR-05-16). Princeton, NJ: Educational Testing Service. doi: 10.1002/j.2333-8504.2005.tb01993.x
  • Watson, J. M. (2006). Statistical literacy at school: Growth and goals. Mahwah, NJ: Lawrence Erlbaum.
  • Zhang, X., & Maas, Z. (2019). Using R as a simulation tool in teaching introductory statistics. International Electronic Journal of Mathematics Education, 14(3), 599-610. doi: 10.29333/iejme/5773

Investigating Preservice Middle School Mathematics Teachers’ Competencies in Statistics and Probability in Terms of Various Variables

Year 2020, Volume: 11 Issue: 1, 13 - 26, 24.03.2020
https://doi.org/10.21031/epod.562586

Abstract

In this study, probabilities of preservice middle school mathematics teachers’ possession of four fundamental cognitive skills required for learning and teaching statistics and probability topics were examined by using the log-linear cognitive diagnostic model, which is one of the cognitive diagnostic models. Moreover, the probabilities of preservice teachers’ possession of these skills were investigated according to gender, university ranking, and grade level variables. Hence, it was examined whether there was a significant relationship between the probabilities of having each skill and these variables. A Statistical Reasoning Test, which was developed by Arican and Kuzu in 2019, measured preservice teachers’ possession of four critical skills was used in collecting the data. These four skills included representing and interpreting data, drawing inferences about populations based on samples, selecting and using appropriate statistical methods to analyze data, and understanding and applying basic concepts of probability. In the 2016-2017 academic year, the test was applied to 456 preservice teachers selected from four different universities in Turkey, and probabilities of their possession of each attribute were calculated. Later, the relationship between the preservice teachers’ test scores and gender was examined by using the Mann-Whitney U test, and the relationship between their test scores and ranking of the attended university and grade level were examined using the Kruskal Wallis-H test. Although probabilities of the preservice teachers’ possession of these four skills did not significantly differ according to gender, some significant differences were detected for university ranking and grade level variables.

References

  • Ardıç, E. Ö., Yılmaz, B., & Demir, E. (2012, June). İlköğretim 8. sınıf öğrencilerinin merkezi eğilim ve yayılım ölçüleri hakkındaki istatistiksel okuryazarlık düzeylerinin solo taksonomisine göre incelenmesi. Paper session presented at the meeting of X. Fen Bilimleri ve Matematik Eğitimi Kongresi, Niğde, Türkiye. http://kongre.nigde.edu.tr/xufbmek/dosyalar/tam_metin/pdf/2430-30_05_2012-18_28_56.pdf adresinden edinilmiştir.
  • Arican, M., & Kuzu, O. (2019). Diagnosing preservice teachers’ understanding of statistics and probability: Developing a test for cognitive assessment. International Journal of Science and Mathematics Education. 1-20. Advanced online first. doi: 10.1007/s10763-019-09985-0
  • Atuahene, F., & Russell, T. A. (2016). Mathematics readiness of first-year university students. Journal of Developmental Education, 39(3), 12-20. www.jstor.org/stable/44987415 adresinden edinilmiştir.
  • Batanero, C., & Díaz, C. (2010). Training teachers to teach statistics: What can we learn from research? Statistique et Enseignement, 1(1), 5-20. http://statistique-et-enseignement.fr/article/view/3 adresinden edinilmiştir.
  • Batanero, C., & Díaz, C. (2012). Training school teachers to teach probability: Reflections and challenges. Chilean Journal of Statistics, 3(1), 3-13. http://chjs.mat.utfsm.cl/volumes/03/01/Batanero_Diaz(2012).pdf adresinden edinilmiştir.
  • Batanero, C., Godino, J. D., & Roa, R. (2004). Training teachers to teach probability. Journal of statistics Education, 12(1). http://www.amstat.org/publications/jse/v12n1/batanero.html adresinden edinilmiştir.
  • Boyacıoğlu, H., Erduran, A., & Alkan, H. (1996, September). Permütasyon, kombinasyon ve olasılık öğretiminde rastlanan güçlüklerin giderilmesi. Paper session presented at the meeting of II. Ulusal Eğitim Sempozyumu, Marmara University, İstanbul.
  • Bradshaw, L., Izsak, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers’ understandings of rational numbers: Building a multidimensional test within the diagnostic classification framework. Educational Measurement: Issues and Practice, 33(1), 2-14. doi: 10.1111/emip.12020
  • Bulut, S., Yetkin, İ. E., & Kazak, S. (2002). Matematik öğretmen adaylarının olasılık başarısı, olasılık ve matematiğe yönelik tutumlarının cinsiyete göre incelenmesi. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 22(22), 21-28. https://dergipark.org.tr/en/download/article-file/87889 adresinden edinilmiştir.
  • Çepni, S., Bayrakçeken, S., Yılmaz, A., Yücel, C., Semerci, Ç., Köse, E., …, Gündoğdu, K. (2008). Ölçme ve değerlendirme. Ankara: Pegem Akademi.
  • Chambers, E. A., & Schreiber, J. B. (2004). Girls’ academic achievement: Varying associations of extracurricular activities. Gender and Education, 16(3), 327-346. doi: 10.1080/09540250042000251470
  • Chiesi, F., & Primi, C. (2015, February). Gender differences in attitudes toward statistics: Is there a case for a confidence gap? In K. Krainer & N. Vondrova (Eds.), CERME 9-Ninth congress of the European society for research in mathematics education (pp. 622-628). Prague, Czech Republic: Charles University & ERME.
  • Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about what students have learned: An analysis of TIMSS eighth grade mathematics. Eurasia Journal of Mathematics, Science & Technology Education, 11(6), 1563-1577. doi: 10.12973/eurasia.2015.1421a
  • de la Torre, J. (2008). An empirically based method of Q‐matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45(4), 343-362. doi: 10.1111/j.1745-3984.2008.00069.x
  • de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179-199. doi: 10.1007/s11336-011-9207-7
  • DiBello, L. V., Stout, W. F., & Roussos, L. A. (1995). Unified cognitive/psychometric diagnostic assessment likelihood–based classification techniques. In P. Nichols, S. Chipman, & R. Brennan (Eds.), Cognitively diagnostic assessment (pp. 361-390). Hillsdale, NJ: Lawrence Erlbaum.
  • Dogan, E., & Tatsuoka, K. (2008). An international comparison using a diagnostic testing model: Turkish students’ profile of mathematical skills on TIMSS-R. Educational Studies in Mathematics, 68(3), 263-272. doi: 10.1007/s10649-007-9099-8
  • Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98(1), 198-208. doi: 10.1037/0022-0663.98.1.198
  • Eitle, T. M. (2005). Do gender and race matter? Explaining the relationship between sports participation and achievement. Sociological Spectrum, 25(2), 177-195. doi: 10.1080/02732170590883997
  • Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103-127. doi: 10.1037/a0018053
  • Farooq, M. S., Chaudhry, A. H., Shafiq, M., & Berhanu, G. (2011). Factors affecting students’ quality of academic performance: A case of secondary school level. Journal of Quality and Technology Management, 7(2), 1-14. http://pu.edu.pk/images/journal/iqtm/PDF-FILES/01-Factor.pdf adresinden edinilmiştir.
  • Felson, R. B., & Trudeau, L. (1991). Gender differences in mathematics performance. Social Psychology Quarterly, 54(2), 113-126. doi: 10.2307/2786930
  • Franklin, C., & Mewborn, D. (2006). The statistical education of PreK-12 teachers: A shared responsibility. In G. Burrill (Ed.), NCTM 2006 Yearbook: Thinking and reasoning with data and chance (pp. 335-344). Reston, VA: NCTM.
  • Franklin, C., Kader, G., Mewborn, D. S., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report: A pre-K-12 curriculum framework. Alexandria, VA: American Statistical Association. https://www.amstat.org/asa/files/pdfs/gaise/gaiseprek-12_full.pdf adresinden edinilmiştir.
  • Fryer Jr, R. G., & Levitt, S. D. (2010). An empirical analysis of the gender gap in mathematics. American Economic Journal: Applied Economics, 2(2), 210-40. doi: 10.1257/app.2.2.210
  • Gürbüz, R., Toprak, Z., Yapıcı, H., & Doğan, S. (2011). Subjects perceived as difficult in secondary mathematics curriculum and their reasons. Gaziantep University Journal of Social Sciences, 10(4), 1311-1323. https://dergipark.org.tr/en/download/article-file/223364 adresinden edinilmiştir.
  • Hartz, S. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practice (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign.
  • Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210. doi: 10.1007/s11336-008-9089-5
  • Im, S., & Park, H. J. (2010). A comparison of US and Korean students’ mathematics skills using a cognitive diagnostic testing method: Linkage to instruction. Educational Research and Evaluation, 16(3), 287-301. doi: 10.1080/13803611.2010.523294
  • Jones, G. A. (2005). Exploring probability in school: Challenges for teaching and learning. New York, NY: Springer.
  • Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric Madde response theory. Applied Psychological Measurement, 25(3), 258-272. doi: 10.1177/01466210122032064
  • Karasar, N. (2005). Bilimsel araştırma yöntemleri. Ankara: Nobel Yayınevi
  • Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52-54. doi: 10.5395/rde.2013.38.1.52
  • Lee, Y. S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the US national sample using the TIMSS 2007. International Journal of Testing, 11(2), 144–177. doi: 10.1080/15305058.2010.534571
  • Leighton, J. P., & Gierl, M. J. (2007). Why cognitive diagnostic assessment? In J. P. Leighton & M. J. Gierl (Eds.), Cognitive diagnostic assessment for education (pp. 3-18). Cambridge: Cambridge University Press.
  • Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123-1135. doi: 10.1037/a0021276
  • Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82-105. https://www.stat.auckland.ac.nz/~iase/serj/SERJ8(1).pdf#page=85 adresinden edinilmiştir.
  • Milli Eğitim Bakanlığı. (2013). Ortaokul matematik dersi öğretim programı. Ankara: Milli Eğitim Bakanlığı Talim ve Terbiye Kurulu Başkanlığı.
  • Milli Eğitim Bakanlığı. (2018). Matematik dersi öğretim programı. Ankara: Milli Eğitim Bakanlığı Talim ve Terbiye Kurulu Başkanlığı.
  • Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Amsterdam: International Association for the Evaluation of Educational Achievement. https://pdfs.semanticscholar.org/9802/a1fabea7578ffd251e50bec4ac13831fbca0.pdf adresinden edinilmiştir.
  • Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in mathematics. Boston College: TIMSS & PIRLS International Study. http://timssandpirls.bc.edu/timss2015/international-results/ adresinden edinilmiştir.
  • Muthen, L. K., & Muthen, B. O. (2011). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthen & Muthen.
  • Nichols, P. D., Chipman, S. F., & Brennan, R. L. (Eds.). (2012). Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum.
  • Olpak, Y. Z., Baltaci, S., & Arican, M. (2018). Investigating the effects of peer instruction on preservice mathematics teachers’ achievements in statistics and probability. Education and Information Technologies, 23(6), 2323-2340. doi: 10.1007/s10639-018-9717-3
  • Ravand, H., & Robitzsch, A. (2015). Cognitive diagnostic modeling using R. Practical Assessment, Research & Evaluation, 20(11), 1-12. doi: 10.7275/5g6f-ak15
  • Rupp, A. A., Templin, J. L., & Henson, R. A. (2010). Diagnostic assessment: Theory, methods, and applications. New York, NY: Guilford Press.
  • Saraçbaşı, T., & Kutsal, A. (1987). Betimsel istatistik. Ankara: Hacettepe Üniversitesi.
  • Sen, S., & Arican, M. (2015). A diagnostic comparison of Turkish and Korean students’ mathematics performances on the TIMSS 2011 assessment. Journal of Measurement and Evaluation in Education and Psychology, 6(2), 238-253. doi: 10.21031/epod.65266
  • Shaughnessy, J. M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957-1009). Reston, VA: The National Council of Teachers of Mathematics.
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There are 57 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Okan Kuzu 0000-0003-2466-4701

Muhammet Arıcan 0000-0002-0496-9148

Publication Date March 24, 2020
Acceptance Date September 1, 2019
Published in Issue Year 2020 Volume: 11 Issue: 1

Cite

APA Kuzu, O., & Arıcan, M. (2020). Investigating Preservice Middle School Mathematics Teachers’ Competencies in Statistics and Probability in Terms of Various Variables. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 13-26. https://doi.org/10.21031/epod.562586