PEN Academic Publishing   |  ISSN: 1309-0682

Orjinal Araştırma Makalesi | Akdeniz Eğitim Araştırmaları Dergisi 2014, Cil. 8(15) 11-30

The Impact of Student and School Characteristics and their Interaction on Turkish Students’ Mathematical Literacy Skills in the Programme for International Student Assessment (PISA) 2003

Çiğdem İş Güzel

ss. 11 - 30   |  Makale No: mjer.2014.002

Yayın tarihi: Haziran 01, 2014  |   Okunma Sayısı: 88  |  İndirilme Sayısı: 212


Özet

PISA is one of the most influential international assessment program for providing feedback to education policy makers in the participating countries. In the present study, HLM analysis was carried out for the Turkish database for deriving findings related to student and school related factors as PISA described. For the student related factors, it was found that more educational resources at home, lower student teacher relations, positive feelings about school, higher levels of mathematics self-efficacy, lower levels of mathematics anxiety, more positive self-concept, more preferences for control strategies, less preferences for elaboration and memorization strategies and more positive disciplinary climate in mathematics lessons reveal higher mathematical literacy measures. Similarly, for the school related factors, it was found that higher performing schools have higher self-efficacy of the students, larger school size, higher proportion of females enrolled, lower total student-teacher ratio and mathematics student-teacher ratio, higher academic selectivity, higher quality of physical infrastructure, more positive evaluations of student-related factors and the less positive evaluations of teacher-related factors affecting school climate. Moreover, the disciplinary climate in mathematics lessons has more of an influence on mathematical literacy in schools with larger school size and with larger mathematics student-teacher ratio. The results were discussed in terms of education policy impact in the Turkish educational system.

Anahtar Kelimeler: Programme for International Student Assessment (PISA), Mathematical Literacy, Hierarchical Linear Modeling (HLM), Student-Level Factors, School-Level Factors


Bu makaleye nasıl atıf yapılır?

APA 6th edition
Guzel, C.I. (2014). The Impact of Student and School Characteristics and their Interaction on Turkish Students’ Mathematical Literacy Skills in the Programme for International Student Assessment (PISA) 2003. Akdeniz Eğitim Araştırmaları Dergisi, 8(15), 11-30.

Harvard
Guzel, C. (2014). The Impact of Student and School Characteristics and their Interaction on Turkish Students’ Mathematical Literacy Skills in the Programme for International Student Assessment (PISA) 2003. Akdeniz Eğitim Araştırmaları Dergisi, 8(15), pp. 11-30.

Chicago 16th edition
Guzel, Cigdem Is (2014). "The Impact of Student and School Characteristics and their Interaction on Turkish Students’ Mathematical Literacy Skills in the Programme for International Student Assessment (PISA) 2003". Akdeniz Eğitim Araştırmaları Dergisi 8 (15):11-30.

Kaynakça
  1. Abu-Hilal, M. M. (2000). A structural model for predicting mathematics achievement: Its relation with anxiety and self-concept in mathematics. Psychological Reports, 86, 835-847. [Google Scholar]
  2. Akyüz, G. (2006). Teacher and classroom characteristics: Their relationship with mathematics achievement in Turkey, European Union countries and candidate countries. Unpublished doctoral dissertation, Middle East Technical University, Ankara, Turkey. [Google Scholar]
  3. Alwin, D. F., & Thornton, A. (1984). Family origins and the schooling process: Early versus late influence of parental characteristics. American Sociological Review, 49, 784-802. [Google Scholar]
  4. Ames, C., & Ames, R. (1984). Systems of student and teacher motivation: Toward a qualitative definition. In  Al-Halal, A. (2001). The effects of individualistic learning and cooperative learning strategies on elementary students’ mathematics achievement and use of social skills. Dissertation Abstracts International, 62(5), 1697A. (UMI No. 3015154). [Google Scholar]
  5. Baker, D. P., & Stevenson, D. L. (1986). Mother’s strategies for children school achievement: Manage the transition to high school. Sociology of Education, 59, 156-166. [Google Scholar]
  6. Berberoğlu, G. (2011). Okullarda ölçme ve değerlendirme ne amaçla yapılmalıdır? Cito Eğitim: Kuram ve Uygulama, 11, 9-24. [Google Scholar]
  7. Bidwell, C. E., & Kasarda, J. D. (1975). School district organization and student achievement. American Sociological Review, 40, 55-70. [Google Scholar]
  8. Boocock, S. S. (1980). Sociology of education. In Dowson, M., & McInerney, D. M. (1998). Age, gender, cultural, and socioeconomic differences in students' academic motivation, cognition, and achievement. East Lansing, MI: National Center for Research on Teacher Learning. (ERIC Document Reproduction Service No. ED 427 016). [Google Scholar]
  9. Bos, K., & Kuiper, W. (1999). Modeling TIMSS data in a European comparative perspective: Exploring influencing factors on achievement in mathematics in grade 8. Educational Research and Evaluation, 5(2), 157-179. [Google Scholar]
  10. Brookover, W. B., & Beady, C., Flood, P., Schweitzer, J., & Wisenbaker, J. (1979). School social systems and student achievement: Schools can make a difference. New York: Praeger. [Google Scholar]
  11. Cooper, S. E., & Robinson, D. A. G. (1991). The relationship of mathematics self-efficacy beliefs to mathematics anxiety and performance. Measurement & Evaluation in Counseling & Development, 24(1). [Google Scholar]
  12. D’Agostino, J. V. (2000). Instructional and school effects on students’ longitudinal reading and mathematics achievements. School Effectiveness and School Improvement, 11(2), 197-235. [Google Scholar]
  13. Eccles, J. S., Meece, J. L., & Wigfield, A. (1990). Predictors of mathematics anxiety and its influence on young adolescents’ course enrollment intentions and performance in mathematics. Journal of Educational Psychology, 82(1), 60-70. [Google Scholar]
  14. Eccles, J. S. (1994). Understanding women’s educational and occupational choice: Applying the Eccles et al. model of achievement related choices. In Organisation for Economic Co-operation and Development (2004). Learning for tomorrow’s world: First results from PISA 2003. (p. 123). Paris: OECD Publications. [Google Scholar]
  15. Edington, E. D., & Martellaro, H. C. (1989). Does school size have any relationship to academic achievement? Rural Educator, 11(2), 6-11. [Google Scholar]
  16. Fan, X., Chen, M., & Matsumoto, A. R. (1997). Gender differences in mathematics achievement: Findings from the national education longitudinal study of 1998. The Journal of Experimental Education, 65, 229-242. [Google Scholar]
  17. Ferry, T. R., Fouad, N. A., & Smith, P. L. (2000). The role of family context in a social cognitive model for career-related choice behavior: A mathematics and science perspective. Journal of Vocational Behavior, 57, 348-364. [Google Scholar]
  18. Finn, J. (1989). Withdrawing from school. In Organisation for Economic Co-Operation and Development (2004). Learning for tomorrow’s world: First results from PISA 2003. (p. 115). Paris: OECD Publications. [Google Scholar]
  19. Gallagher, H. A. (2004). Vaughn elementary’s innovative teacher evaluation system: Are teacher evaluation scores related to growth in student achievement? Peabody Journal of Education, 79(4), 79-107. [Google Scholar]
  20. Hackett, G., & Betz, N. E. (1989). An exploration of the mathematics self-efficacy / mathematics performance correspondence. Journal for Research in Mathematics Education, 20, 263-271. [Google Scholar]
  21. Halinen, I., Sinko, P., & Laukkanen, R. (2005). A land of readers. Educational Leadership, 63(2), 72-75. [Google Scholar]
  22. Hall, J. M., & Ponton, M. K. (2005). Mathematics self-efficacy of college freshman. Journal of Developmental Education, 28(3), 26-33. [Google Scholar]
  23. Hallinan, M. T., & Sørensen, A. B. (1987). Ability grouping and sex differences in mathematics achievement. Sociology of Education, 60, 63-72. [Google Scholar]
  24. Hill, P. W., & Rowe, K. J. (1998). Modeling educational effectiveness in classrooms: The use of multi-level structural equations to model students’ progress. Educational Research and Evaluation, 4(4), 307-347. [Google Scholar]
  25. Hvistendahl, R., & Roe, A. (2004). The literacy achievement of Norwegian minority students. Scandinavian Journal of Educational Research, 48(3), 307-324. [Google Scholar]
  26. İş Güzel, Ç. (2006). A cross-cultural comparison of the impact of human and physical resource allocations on students’ mathematical literacy skills in Programme for International Student Assessment (PISA) 2003. Unpublished doctoral dissertation, Middle East Technical University, Ankara, Turkey. [Google Scholar]
  27. İş Güzel, Ç., & Berberoğlu, G. (2005). An analysis of the Programme for International Student Assessment 2000 (PISA 2000) mathematical literacy data for Brazilian, Japanese and Norwegian Students. Studies in Educational Evaluation, 31, 283-314. [Google Scholar]
  28. Jenkins, P. H. (1995). School delinquency and school commitment. In Organisation for Economic Co-Operation and Development (2004). Learning for tomorrow’s world: First results from PISA 2003. (p. 115). Paris: OECD Publications. [Google Scholar]
  29. Kjærnsli, M, & Lie, S. (2004). PISA and scientific literacy: Similarities and differences between the Nordic countries. Scandinavian Journal of Educational Research, 48(3), 271-286. [Google Scholar]
  30. Lee, J. (2004). Evaluating the effectiveness of instructional resource allocation and use: IRT and HLM analysis of NAEP teacher survey and student assessment data. Studies in Educational Evaluation, 30, 175-199. [Google Scholar]
  31. Lee, V. E., & Bryk, A. S. (1989). A multilevel model of the social distribution of high school achievement. Sociology of Education, 62, 172-192. [Google Scholar]
  32. Lee, V. E., Smith, J. B., & Croninger, R. G. (1997). How high school organization influences the equitable distribution of learning in mathematics and science. Sociology of Education, 70, 128-150. [Google Scholar]
  33. Leino, K., Linnakylä, P., & Malin, A. (2004). Finnish students’ multiliteracy profiles. Scandinavian Journal of Educational Research, 48(3), 251-270. [Google Scholar]
  34. Lemke, M., Sen, A., Pahlke, E., Partelow, L., Miller, D., Williams, T., Kastberg, D., & Jocelyn, L. (2004). International outcomes of learning in mathematics literacy and problem solving: PISA 2003 results from the U.S. perspective. (NCES 2005-003). U.S. Department of Education, National Center for Education Statistics, Washington, DC: U.S. Government Printing Office. [Google Scholar]
  35. Lim, T. K. (1995). Perceptions of classroom environment, school types, gender and learning styles of secondary school students. Educational Psychology, 15(2), 161-169. [Google Scholar]
  36. Linnakylä, P., Malin, A., & Taube, K. (2004) Factors behind low reading literacy achievement. Scandinavian Journal of Educational Research, 48(3), 231-249. [Google Scholar]
  37. Ma, X. (1997). Reciprocal relationships between attitude toward mathematics and achievement in mathematics. Journal of Educational Research, 90, 221-229. [Google Scholar]
  38. Marsh, H. W. (1986). Verbal and math self-concepts: An internal/external frame of reference model. American Educational Research Journal, 23(1), 129-149. [Google Scholar]
  39. National Education Publications (2003). National report of TIMSS 1999. Ankara: National Education Publications.  [Google Scholar]
  40. National Education Publications (2005). National report of PISA 2003. Ankara: National Education Publications. [Google Scholar]
  41. O’Brien, V., Martinez-Pons, M., & Kopala, M. (1999). Mathematics self-efficacy, ethnic identity, gender, and career interests related to mathematics and science. The Journal of Educational Research, 92(4), 231-235. [Google Scholar]
  42. Okebukola, P. A., & Ogunniyi, M. B. (1984). Cooperative, competitive and individualistic science laboratory interaction patterns: Effects on students’ achievement and acquisition of practical skills. In Al-Halal, A. (2001). The effects of individualistic learning and cooperative learning strategies on elementary students’ mathematics achievement and use of social skills. Dissertation Abstracts International, 62(5), 1697A. (UMI No. 3015154). [Google Scholar]
  43. Olszewski-Kubilius, P., & Turner, D. (2002). Gender differences among elementary school-aged gifted students in achievement, perceptions of ability, and subject preference. Journal for the Education of the Gifted, 25(3), 233-268. [Google Scholar]
  44. Organisation for Economic Co-Operation and Development (2001). Knowledge and skills for life: First results from PISA 2000. Paris: OECD Publications. [Google Scholar]
  45. Organisation for Economic Co-Operation and Development (2002). Sample tasks from the PISA 2000 assessment. Paris: OECD Publications. [Google Scholar]
  46. Organisation for Economic Co-Operation and Development (2004). Learning for tomorrow’s world. Paris: OECD Publications. [Google Scholar]
  47. Organisation for Economic Co-Operation and Development (2005). PISA 2003 technical report . Paris: OECD Publications. [Google Scholar]
  48. Raudenbush, S. W., Bryk, A., Cheong, Y. F., & Congdon, R. (2001). HLM5: Hierarchical linear and nonlinear modeling. Chicago, IL: Scientific Software International, Inc. [Google Scholar]
  49. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. CA: Sage Publications, Inc. [Google Scholar]
  50. Reynolds, A. J., & Walberg, H. J. (1992). A structural model of high school mathematics outcomes. Journal of Educational Research, 85(3), 150-158. [Google Scholar]
  51. Ryoo, H. S. (2001). Multilevel influences on student achievement: An international comparative study. Dissertation Abstracts International, 62(3), 870A. (UMI No. 3011267). [Google Scholar]
  52. Scheerens, J., & Bosker, R. J. (Eds.) (1997). Foundations of educational effectiveness. London: Routledge.  [Google Scholar]
  53. Tate, W. F. (1997). Race-ethnicity, SES, gender, and language proficiency trends in mathematics achievement: An update. Journal for Research in Mathematics Education, 28, 652-679. [Google Scholar]
  54. Tiedemann, J. (2000). Gender-related beliefs of teachers in elementary school mathematics. Educational Studies in Mathematics, 41, 191-207. [Google Scholar]
  55. Turmo, A. (2004). Scientific literacy and socio-economic background among 15-year-olds: A Nordic perspective. Scandinavian Journal of Educational Research, 48(3), 287-305. [Google Scholar]
  56. Voyer, D. (1998). Mathematics, gender, spatial performance, and cerebral organization: A suppression effect in talented students. Roeper Review, 20(4), 251-258. [Google Scholar]
  57. Watt, H. M. G. (2000). Measuring attitudinal change in mathematics and english over the 1st year of junior high school: A multidimensional analysis. The Journal of Experimental Education, 68(4), 331-361. [Google Scholar]
  58. Willms, J. D. (1992). Monitoring school performance: A guide for educators. Washington, DC: Falmer. [Google Scholar]
  59. Yayan, B., & Berberoğlu, G. (2004). A re-analysis of the TIMSS 1999 mathematics assessment data of the Turkish students. Studies in Educational Evaluation, 30, 87-104. [Google Scholar]