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MODELING RELATIONSHIPS AMONG LEARNING, ATTITUDE, SELF-PERCEPTION, AND SCIENCE ACHIEVEMENT FOR GRADE 8 SAUDI STUDENTS

  • M’HAMED TIGHEZZAEmail author
Article

Abstract

The purpose of the present study was to examine the validity of modeling science achievement in terms of 3 social psychological variables (school connectedness, science attitude, and active learning) and 2 self-perception variables (self-confidence and science value). Two models were tested: full mediation and partial mediation. In the full-mediation model, effects of the 3 social psychological variables upon science achievement were hypothesized to be completely mediated through science value and self-confidence. In the partial-mediation model, however, those 3 variables were hypothesized to affect achievement directly as well as indirectly through the mediating roles of science value and self-confidence. Data were obtained from Grade 8 Saudi students (N = 4,099) who participated in TIMSS 2007. The relationships among constructs were examined with the use of structural equation modeling software Mplus7. Results indicated that both models performed adequately in terms of fit indices, but the partial-mediation model was retained due to its superiority over the full-mediation model in representing the sample covariance matrix as tested through chi-square difference test. The mediating role of self-confidence in the relationships of science attitude and active learning to achievement was substantiated, but the mediating role of science value was not supported.

Key words

active learning school connectedness science achievement science attitude science value self-confidence 

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References

  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.CrossRefGoogle Scholar
  2. Anderson, J. O., Chiu, M.-H. & Yore, L. D. (2010). First cycle of PISA (2000–2006)—International perspectives on successes and challenges: Research and policy direction [Special issue]. International Journal of Science and Mathematics Education, 8(3), 373–388.CrossRefGoogle Scholar
  3. Anderson, J. O., Lin, H.-S., Treagust, D. F., Ross, S. P. & Yore, L. D. (2007). Using large-scale assessment datasets for research in science and mathematics education: Programme for International Student Assessment (PISA). International Journal of Science and Mathematics Education, 5(4), 591–614.CrossRefGoogle Scholar
  4. Burnham, K. P. & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer.Google Scholar
  5. Chapman, R. L., Buckley, L. D., Sheehan, M. C., Shochet, I. M. & Romaniuk, M. (2011). The impact of school connectedness on violent behavior, transport risk taking behavior and associated injuries in adolescence. Journal of School Psychology, 49(4), 399–410.CrossRefGoogle Scholar
  6. Coms, A. W. & Snygg, D. (1959). Individual behavior (2nd ed.). New York: Harper & Row.Google Scholar
  7. Croninger, R. G. & Lee, V. E. (2001). Social capital and dropping out of high schools: Benefits to at-risk students of teachers’ support and guidance. Teachers College Record, 4, 548–581.CrossRefGoogle Scholar
  8. Eccles, J. S. & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.CrossRefGoogle Scholar
  9. Eklöf, H. (2006). Motivational beliefs in the TIMSS 2003 context: Theory, measurement, and relation to test performance (unpublished doctoral dissertation). Umeå University, Umeå, Sweden.Google Scholar
  10. Eklöf, H. (2007). Self-concept and valuing of mathematics in TIMSS 2003: Scale structure and relation to performance in a Swedish setting. Scandinavian Journal of Educational Research, 51(3), 297–313.CrossRefGoogle Scholar
  11. Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. doi: 10.1146/annurev.psych. 58.110405.085530.CrossRefGoogle Scholar
  12. Hammouri, H. A. (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.CrossRefGoogle Scholar
  13. Henley, A. B., Shook, C. L. & Peterson, M. (2006). The presence of equivalent model in strategic management research using structural equation modeling: Assessing and addressing the problem. Organizational Research Methods, 9(4), 516–535.CrossRefGoogle Scholar
  14. Hewson, C. (2006). Secondary analysis. In J. Victor (Ed.), The Sage dictionary of social research methods (pp. 274–275). London: Sage.Google Scholar
  15. House, J. D. (1996). Student expectancies and academic self-concept as predictors of student achievement. Journal of Psychology, 130, 679–681.CrossRefGoogle Scholar
  16. House, J. D. (2000). Relationships between instructional activities and science achievement of adolescent students in Hong Kong: Findings from the Third International Mathematics and Science Study (TIMSS). Studies in Educational Evaluation, 27, 275–289.Google Scholar
  17. House, J. D. (2001). Relationships between instructional activities and mathematics achievement of adolescent students in Japan: Findings from the Third International Mathematics and Science Study (TIMSS). Instructional Journal of Instructional Media, 28(1), 93–106.Google Scholar
  18. Hung, M. (2010). Examining inquiry-based science instruction, students’ attitudes toward science and achievement using moderated mediation via structural equation modeling. Online Educational Research Journal, 5, 1–46. Retrieved from http://www.oerj.org/View?action=viewPDF&paper=5 Google Scholar
  19. Jensen, F. & Sjaastad, J. (2013). A Norwegian out-of-school mathematics project’s influence on secondary students’ stem motivation. International Journal of Science and Mathematics Education. doi: 10.1007/s10763-013-9401-4. Advance online publication.Google Scholar
  20. Kadijevich, D. J. (2008). TIMSS 2003: Relating dimensions of mathematics attitude to mathematics achievement. Zbornik instituta za pedagoka istraivanja, 40(2), 327–346. doi: 10.2298/ZIPI0802327K.CrossRefGoogle Scholar
  21. Kiamanesh, A. R. (2004, July). Self-concept, home background, motivation, attribution and their effects on Iranian students’ science achievement. Paper presented at the Third International Biennial SELF Research Conference, Berlin, Germany.Google Scholar
  22. Kiamanesh, A. R., Hejazi, E., & Esfahani, Z. N. (2004, July). The role of math self-efficacy, math self-concept, perceived usefulness of mathematics and math anxiety in math achievement. Paper presented at the Third International Biennial SELF Research Conference, Berlin, Germany.Google Scholar
  23. Kiamanesh, A. R., & Mahdavi-Hezaveh, M. (2008). Influential factors causing the gender differences in mathematics’ achievement scores among Iranian eight graders based on TIMSS 2003 data. Paper presented at the 3rd IEA International Research Conference (IRC-2008), Taipei, Taiwan.Google Scholar
  24. Kline, R. B. (2005). Principals and practice of structural equation modeling (2nd ed.). New York: Guildford.Google Scholar
  25. Koballa, T. R., Jr. & Glynn, S. M. (2007). Attitudinal and motivational constructs in science learning. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 75–102). Mahwah: Erlbaum.Google Scholar
  26. Lee, V. E. & Burkam, D. T. (1996). Gender differences in middle grade science achievement: Subject domain, ability and course emphasis. Science Education, 80, 613–650.CrossRefGoogle Scholar
  27. Libbey, H. (2004). Measuring student relationships to school: Attachment, bonding, connectedness, and engagement. Journal of School Health, 74, 274–283.CrossRefGoogle Scholar
  28. Liem, A., Lau, S. & Nie, Y. (2008). The role of self-efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33(4), 486–512.CrossRefGoogle Scholar
  29. Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. Mahwah: Erlbaum.Google Scholar
  30. MacCallum, R. C. & Austin, J. T. (2000). Application of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–206.CrossRefGoogle Scholar
  31. Maddox, S. J. & Prinz, R. J. (2003). School bonding in children and adolescents: Conceptualization, assessment, and associated variables. Clinical Child and Family Psychology Review, 6, 31–49.CrossRefGoogle Scholar
  32. Marsh, H. W. (1986). The bias of negatively worded items in rating scales for young children: A cognitive developmental phenomena. Developmental Psychology, 22, 37–49.CrossRefGoogle Scholar
  33. Marsh, H. W. (1992). Content specificity of relations between academic achievement and academic self-concept. Journal of Educational Psychology, 84(1), 35–42.CrossRefGoogle Scholar
  34. Martin, M. O., Mullis, I. V. S., Gonzalez, E. J. & Chrostowski, S. J. (2004). TIMSS 2003 international science report: Findings from IEA’s Trends in International Mathematics and Science Study at the fourth and eighth grades. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  35. Ministry of Higher Education (2006). Educational system of Saudi Arabia. Washington, DC: Saudi Arabia Cultural Mission.Google Scholar
  36. Mullis, I. V. S., Martin, M. O. & Foy, P. (2008a). TIMSS 2007 international mathematics report. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  37. Mullis, I. V. S., Martin, M. O. & Foy, P. (2008b). TIMSS 2007 international science report. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  38. Mullis, I. V. S., Martin, M. O., Olson, J. F., Berger, D. R., Milne, D. & Stanco, G. M. (Eds.). (2008c). TIMSS 2007 encyclopedia: A guide to mathematics and science education around the world. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  39. Olson, J. F., Martin, M. O. & Mullis, I. V. S. (Eds.). (2008). TIMSS 2007 technical report. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  40. Pajares, M. F. & Schunk, D. H. (2002). Self and self-beliefs in psychology and education: A historical perspective. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 3–21). New York: Academic.CrossRefGoogle Scholar
  41. Papanastasiou, C. (2002). School, teaching and family influence on student attitudes toward science: Based on TIMSS data Cyprus. Studies in Educational Evaluation, 28, 71–86.CrossRefGoogle Scholar
  42. Parker, V. & Berger, B. (2000). Effects of science intervention program on middle-grade students achievement and attitude. School Science and Mathematics, 100(5), 236–242.CrossRefGoogle Scholar
  43. Rennie, L. J. & Punch, K. F. (1991). The relationship between affect and achievement in science. Journal of Research in Science Teaching, 28, 193–209.CrossRefGoogle Scholar
  44. Schunk, D. H., Pintrich, P. R. & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd ed.). Upper Saddle River: Pearson/Merrill Prentice Hall.Google Scholar
  45. Shen, C. (2002). Revisiting the relationship between student achievement and their self-perceptions: A cross-national analysis based on TIMSS 1999 data. Assessment in Education: Principles, Policy and Practice, 9, 161–184.CrossRefGoogle Scholar
  46. Shrigley, R. L. (1990). Attitude and behavior are correlates. Journal of Research in Science Teaching, 27, 97–113.CrossRefGoogle Scholar
  47. Tytler, R. & Osborne, J. (2012). Student attitudes and aspirations toward science. In B. J. Fraser, K. D. Tobin & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 597–625). Dordrecht: Springer.CrossRefGoogle Scholar
  48. United States National Research Council (2000). How people learn: Brain, mind, experience, and school—Expanded edition (J. D. Bransford, A. L. Brown, & R. R. Cocking, Eds.). Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.Google Scholar
  49. United States National Research Council (2007). Taking science to school: Learning and teaching science in grades K–8 (R. A. Duschl, H. A. Schweingruber, & A. W. Shouse, Eds.). Board on Science Education, Center for Education, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.Google Scholar
  50. Wang, J., & Lin, E. (2008, July). Re-examining the self-concept and mathematics achievement relationship using comparative studies. Paper presented at the 11th International Congress on Mathematical Education, Monterrey, Mexico. Retrieved from http://dg.icme11.org/tsg/show/15#inner-documents
  51. Wigfield, A. & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81.CrossRefGoogle Scholar
  52. Wigfield, A. & Eccles, J. S. (2002). The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation (pp. 92–120). New York: Academic.Google Scholar
  53. Williams, T., Ferraro, D., Roey, S., Brenwald, S., Kastberg, D., Jocelyn, L., Smith, C. & Stearns, P. (2009). TIMSS 2007 U.S. technical report and user guide (NCES 2009-012). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, US Department of Education.Google Scholar
  54. Willingham, W., Pollack, J. M. & Lewis, C. (2002). Grades and test scores: Accounting for observed differences. Journal of Educational Measures, 1(39), 1–37.CrossRefGoogle Scholar
  55. World Bank (2008). The road not travelled: Education reform in the Middle East and North Africa. Education Flagship Report. Washington, DC: Author. Available from http://go.worldbank.org/JLMVU0I6R0
  56. Yore, L. D., Anderson, J. O. & Chiu, M.-H. (2010). Moving PISA results into the policy arena: Perspectives on knowledge transfer for future considerations and preparations [Special issue]. International Journal of Science and Mathematics Education, 8(3), 593–609.CrossRefGoogle Scholar

Copyright information

© National Science Council, Taiwan 2013

Authors and Affiliations

  1. 1.The Excellence Research Center of Science and Mathematics Education and Department of Psychology, College of EducationKing Saud UniversityRiyadhSaudi Arabia

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