BibTex RIS Kaynak Göster

Using PISA 2003, Examining the Factors Affecting Students’ Mathematics Achievement

Yıl 2010, Cilt: 38 Sayı: 38, 44 - 54, 01.06.2010

Öz

Bu çalışmanın amacı, öğrenme stratejilerinin matematik başarısı üzerine etkilerini incelemektir. ÖrneklemTürkiye’deki Uluslararası Öğrenci Değerlendirme Programına (PISA) katılan öğrencilerden oluşmaktadır. Bu veri 158okulda 15 yaşındaki 4493 Türk öğrenciden oluşmaktadır. Analiz genelleştirilmiş hiyerarşik lineer modellerin özel bir durumuolan iki aşamalı bernoulli modeli ile yapılmıştır. Okullar ve okullar içindeki öğrencilerden meydana gelen kümelenmiş veriseti iki aşamalı hiyerarşik bir yapıda incelenmiştir. Çok aşamalı regresyon analizi kullanılarak katsayılar tahmin edilmiş veokullar karşısında farklılıklar modellenmiştir. Bu çalışmanın sonucunda matematik başarısı için lokasyon, cinsiyet vematematiğe olan ilgi değişkenlerinin pozitif ve detaylı öğrenme stratejisi değişkeninin de güçlü negatif etkiye sahip olduğugösterilmiştir

Kaynakça

  • Bransford, J. Sherwood, R. Vye, N. & Rieser, J. (1986). Teaching thinking and problem solving. American Psychologist, 41(10), 1078-1089.
  • Chiu, M. M. and Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18, 321-336.
  • Chow, B. W. Chiu M. M. & Mebride-Chang, C. (2007). Universals and specifics in learning strategies: Explaining adolescent mathematics, science and reading achievement across 34 countries. Learning and Individual Differences, 17, 344-365.
  • Czuchry, M. and Dansereau, D. F. (1998). The generation and recall of personally relevant information. Journal of Experimental Education, 66(4), 293-315.
  • Dunn, C. Chambers, D. & Rabren, K. (2004). Variables Affecting Students' Decisions to Drop Out of School. Remedial and Special Education, 25, 314.
  • Gall, M. D. Gall, J. P. Jacobsen, D. R. & Bullock, T. L. (1990). Tools for learning: A Guide to Teaching Study Skills. Association for Supervision and Curriculum Development.
  • Graham, S. and Golan, S. (1991). Motivational influences on cognition: Task involvement ego involvement and depth of information processing. Journal of Educational Psychology, 83(2), 187-194.
  • Halpern, D. F. (1998). Teaching critical thinking for transfer across domains. American Psychologist, 53, 449-455.
  • Halpern, D. F. (2000). Sex differences in cognitive abilities. Third Edition, London: Erlbaum.
  • Hammouri, H. A. M. (2004). Attitudinal and motivational variables related to mathematics achievement in Jordan: findings from the Third International Mathematics and Science Study (TIMSS). Educational Research, 46(3), 241-257.
  • Hedeker, D. and Gibbons, R. (1994). A Random-Effects Ordinal Regression Model for Multilevel Analysis. Biometrics,50(4), 933-944.
  • Kantrowitz, B. and Wingert, P. (1991). A dismal report card. Newsweek, 117(24), 64-68.
  • Lau, K. L. and Chan, D. (2001). Motivational characteristics of under-achievers in Hong Kong. Educational Psychology, 21(4), 417-430.
  • Nolen, S. B. (1988). Reasons for studying: Motivational orientations and study strategies. Cognition and Instruction, 5(4), 269-287.
  • Organisation for Economic Co-operation and Development (2005a). PISA 2003 Data Analysis Manual. Paris: OECD.
  • Ramirez, M. J. (2006). Understanding the low mathematics achievement of Chilean students: A cross_national analysis using TIMSS data. International Journal of Educational Research, 45, 102-116.
  • Raudenbush, S.W. and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Second Edition, Thousand Oaks, Sage Publications.
  • Raudenbush, S.W. Bryk, A. Cheong, Y. F. Congdon, R. & Toit, M. (2004). HLM 6: Hierarchical Linear and Nonlinear Modeling. Lincolnwood. IL: Scientific Software International (Second printing with revisions).
  • Schmidt, A. E. (2000). An Approximation of a Hierarchical Logistic Regression Model Used to Establish the Predictive Validity of Scores on a Nursing Licensure Exam. Educational and Psychological Measurement, 60, 463-478.
  • Valentine, J. C. DuBois, D. L. & Cooper, H. (2004). The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39(2), 111-133.
  • Vermunt, J. D. and Vermetten Y.J. (2004). Patterns in student learning: Relationships between learning strategies, conceptions of learning, and learning orientations. Educational Psychology Review, 16(4), 359-384.
  • Wang, D. B. (2004). Family background factors and mathematics success: A comparison of Chinese and US students. International Journal of Educational Research, 41, 40-54.
  • Wilcox, P. and Clayton, R. R. (2001). A multilevel analysis of school-based weapon possession. Justice Quarterly, 18(3), 509-541.
  • Wilkins, J. L. M. (2004). Mathematics and Science Self-Concept: An International Investigation. The Journal of Experimental Education, 72(4), 331-346.
  • Yayan, B. ve Berberoğlu, G. (2004). A Re-Analysis of the TIMSS 1999 Mathematics Assessment Data of the Turkish Students. Studies in Educational Evaluation, 30, 87-104.
Yıl 2010, Cilt: 38 Sayı: 38, 44 - 54, 01.06.2010

Öz

Kaynakça

  • Bransford, J. Sherwood, R. Vye, N. & Rieser, J. (1986). Teaching thinking and problem solving. American Psychologist, 41(10), 1078-1089.
  • Chiu, M. M. and Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18, 321-336.
  • Chow, B. W. Chiu M. M. & Mebride-Chang, C. (2007). Universals and specifics in learning strategies: Explaining adolescent mathematics, science and reading achievement across 34 countries. Learning and Individual Differences, 17, 344-365.
  • Czuchry, M. and Dansereau, D. F. (1998). The generation and recall of personally relevant information. Journal of Experimental Education, 66(4), 293-315.
  • Dunn, C. Chambers, D. & Rabren, K. (2004). Variables Affecting Students' Decisions to Drop Out of School. Remedial and Special Education, 25, 314.
  • Gall, M. D. Gall, J. P. Jacobsen, D. R. & Bullock, T. L. (1990). Tools for learning: A Guide to Teaching Study Skills. Association for Supervision and Curriculum Development.
  • Graham, S. and Golan, S. (1991). Motivational influences on cognition: Task involvement ego involvement and depth of information processing. Journal of Educational Psychology, 83(2), 187-194.
  • Halpern, D. F. (1998). Teaching critical thinking for transfer across domains. American Psychologist, 53, 449-455.
  • Halpern, D. F. (2000). Sex differences in cognitive abilities. Third Edition, London: Erlbaum.
  • Hammouri, H. A. M. (2004). Attitudinal and motivational variables related to mathematics achievement in Jordan: findings from the Third International Mathematics and Science Study (TIMSS). Educational Research, 46(3), 241-257.
  • Hedeker, D. and Gibbons, R. (1994). A Random-Effects Ordinal Regression Model for Multilevel Analysis. Biometrics,50(4), 933-944.
  • Kantrowitz, B. and Wingert, P. (1991). A dismal report card. Newsweek, 117(24), 64-68.
  • Lau, K. L. and Chan, D. (2001). Motivational characteristics of under-achievers in Hong Kong. Educational Psychology, 21(4), 417-430.
  • Nolen, S. B. (1988). Reasons for studying: Motivational orientations and study strategies. Cognition and Instruction, 5(4), 269-287.
  • Organisation for Economic Co-operation and Development (2005a). PISA 2003 Data Analysis Manual. Paris: OECD.
  • Ramirez, M. J. (2006). Understanding the low mathematics achievement of Chilean students: A cross_national analysis using TIMSS data. International Journal of Educational Research, 45, 102-116.
  • Raudenbush, S.W. and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Second Edition, Thousand Oaks, Sage Publications.
  • Raudenbush, S.W. Bryk, A. Cheong, Y. F. Congdon, R. & Toit, M. (2004). HLM 6: Hierarchical Linear and Nonlinear Modeling. Lincolnwood. IL: Scientific Software International (Second printing with revisions).
  • Schmidt, A. E. (2000). An Approximation of a Hierarchical Logistic Regression Model Used to Establish the Predictive Validity of Scores on a Nursing Licensure Exam. Educational and Psychological Measurement, 60, 463-478.
  • Valentine, J. C. DuBois, D. L. & Cooper, H. (2004). The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39(2), 111-133.
  • Vermunt, J. D. and Vermetten Y.J. (2004). Patterns in student learning: Relationships between learning strategies, conceptions of learning, and learning orientations. Educational Psychology Review, 16(4), 359-384.
  • Wang, D. B. (2004). Family background factors and mathematics success: A comparison of Chinese and US students. International Journal of Educational Research, 41, 40-54.
  • Wilcox, P. and Clayton, R. R. (2001). A multilevel analysis of school-based weapon possession. Justice Quarterly, 18(3), 509-541.
  • Wilkins, J. L. M. (2004). Mathematics and Science Self-Concept: An International Investigation. The Journal of Experimental Education, 72(4), 331-346.
  • Yayan, B. ve Berberoğlu, G. (2004). A Re-Analysis of the TIMSS 1999 Mathematics Assessment Data of the Turkish Students. Studies in Educational Evaluation, 30, 87-104.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

İbrahim Demir Bu kişi benim

Serpil Kılıç Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2010
Yayımlandığı Sayı Yıl 2010 Cilt: 38 Sayı: 38

Kaynak Göster

APA Demir, İ., & Kılıç, S. (2010). Using PISA 2003, Examining the Factors Affecting Students’ Mathematics Achievement. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 38(38), 44-54.