Author :
Abstract
Because scrutinizing the factors that derived from international studies and have potential to make a country more successful in scientific literacy has been one major concern of researchers in science education field, we carried out a study to expose the differences between a low-performing country (Turkey) and a high-performing country (Finland) with regard to their students’ views of science and familiarity to ICT (Information Communication Technology) based on the PISA 2006 results. A principal component analysis was performed to the items selected from the student questionnaire and ICT familiarity student questionnaire in PISA 2006 to gather the related factors and factor scores. Then, discriminant function analysis was conducted on the basis of the factor scores to explore the differences between a low-performing country (Turkey) and a high-performing country (Finland). The results revealed that the two countries were significantly discriminated based on the seven composite (latent) variables that consist 60 observed variables regarding socioeconomic status, doing well in ICT tasks, self-efficacy in science , importance to given to science, frequency in ICT tasks, student-centered activities, and science activities in leisure time. Whereas the use of student-centered activities and ICT tasks were encouraged in the low-performing country (Turkey), students in the high-performing country (Finland) tended to have high socioeconomic status and high self-efficacy in science. In addition, the results revealed that, even though the students in the high-performing country used the ICT tasks better than the students in the low-performing country, students in the low-performing country (Turkey) tended to do more activities related to ICT tasks.
Keywords
Abstract
Because scrutinizing the factors that derived from international studies and have potential to make a country more successful in scientific literacy has been one major concern of researchers in science education field, we carried out a study to expose the differences between a low-performing country (Turkey) and a high-performing country (Finland) with regard to their students’ views of science and familiarity to ICT (Information Communication Technology) based on the PISA 2006 results. A principal component analysis was performed to the items selected from the student questionnaire and ICT familiarity student questionnaire in PISA 2006 to gather the related factors and factor scores. Then, discriminant function analysis was conducted on the basis of the factor scores to explore the differences between a low-performing country (Turkey) and a high-performing country (Finland). The results revealed that the two countries were significantly discriminated based on the seven composite (latent) variables that consist 60 observed variables regarding socioeconomic status, doing well in ICT tasks, self-efficacy in science , importance to given to science, frequency in ICT tasks, student-centered activities, and science activities in leisure time. Whereas the use of student-centered activities and ICT tasks were encouraged in the low-performing country (Turkey), students in the high-performing country (Finland) tended to have high socioeconomic status and high self-efficacy in science. In addition, the results revealed that, even though the students in the high-performing country used the ICT tasks better than the students in the low-performing country, students in the low-performing country (Turkey) tended to do more activities related to ICT tasks.
Keywords
- Altschuld, J.W. (1995). Evaluating the use of computers in science assessment: Considerations and recommendations. Journal of Science Education and Technology 4: 57–64.
- Amos, S., Boohan, R., & Open University. (2002). Aspects of teaching secondary science: Perspectives on practice. London: RoutledgeFalmer.
- Andrew, S. (1998). Self-efficacy as a predictor of academic performance in science. Journal of Advanced Nursing, 27, 596–603.
- Aypay, A., Erdogan, M., & Sozer, M.A (2007). Variation among Schools on Classroom Practices in Science Based on TIMSS-1999 in Turkey. Journal of Research in Science Teaching, 44 (10), 1417-1435.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman
- Berger, C. F., Lu, C. R., Beltzer, S. J., and Voss, B. E. (1994). Research on the uses of technology in science education. In Gabel,D. L. (Ed.), Handbook of Research in Science Teaching and Learning, Macmillan, New York, pp. 466–490.
- Bong, M., & Skaalvik, E.M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15, 1–40.
- Britner, S.L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7, 271–285.
- Caccovo, F. (2001). Teaching introductory microbiology with active learning. American Biology Teacher 63, 172-174.
- Cavallo, A. M. L., & Laubach. T. A. (2001). Students’ science perceptions and enrollment decisions in differing learning cycle classrooms. Journal of Research in Science Teaching, 38(9), 1029-1062.
- Ceylan, E. & Berberoğlu, G. (2007). Factors Related with Students’ Science Achievement: A Modeling Study, Education & Science, 32, 36-48.
- Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research 53: 445– 460.
- D’ Agostino, J. J. (2000). Instructional and School Effects on Students’ Longitudinal reading and Mathematics Achievements, School Effectiveness and School Improvement, 11, 197-235.
- George, D., & Mallery, P. (2006). SPSS for Windows: Step by step (6th ed.). Boston: Pearson A and B.
- Green, S.B., Salkind, N.J., & Akey, T.M. (2000). Using SPSS for windows: Analyzing and understanding data (2nd ed.). Englewood Cliffs, NJ: Prentice Hall.
- Gustafsson, J-E. (1998). Social background and teaching factors as determinants of reading achievement at class and individual levels. Journal of Nordic Educational Research, 18, 241–250.
- House, J.D. (2007). Relationships between self-beliefs, instructional practices, and chemistry achievement of students in Chinese Taipei: Results from the TIMSS 1999 assessment. International Journal of Instructional Media, 34, 187-205.
- House, J.D. (2008). Effects of Classroom Instructional Strategies and Self-Beliefs on Science achievement of Elementary School Students in Japan: Results from the TIMSS 2003 Assessment. Education, 129, 259
- Jonassen,D. H., Campbell, J. P., and Davidson, M. E. (1994). Learning with media: Restructuring the debate. Educational Technology, Research, and Development, 42: 31–39.
- Konstantopoulos, S., (2006). Trends of School Effects on Student Achievement: Evidence from NLS:72, HSB:82, and NELS:92. Teachers College Record, 108, 2550-2581.
- Kupermintz, H. (2002). Affective and conative factors as aptitude resources in high school science achievement. Educational Assessment, 8, 123–137.
- Lau, S., & Roeser, R.W. (2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science. Educational Assessment, 8, 139–162.
- Leung, F.K. (2002). Behind the High Achievement of East Asian Students. Educational Research and Evaluation, 8, 87-108.
- Linn, M.C., Lewis, C, Tsuchida, 1., & Songer, N.B. (2000). Beyond fourth-grade science: Why do U.S. and Japanese students diverge? Educational Researcher, 29, 4-14.
- Lokan, J., & Greenwood, L. (2000). Mathematics achievement at lower secondary level in Australia. Studies
- Mere, K., Reiska, P., & Smith, T.M. (2006). Impact of SES on Estonian Students ‘ Science Achievement Across Different Cognitive Domains. Prospects: Quarterly Review of Comparative Education, 36, 497-516.
- Nolen, S.B. (2003). Learning Environment, Motivation, and Achievement in High School Science. Journal of Research in Science Teaching, 44, 347-368.
- Organization for Economic Cooperation and Development. (2007). PISA 2006 Science Competencies for Tomorrows’ World, Volume 1-2, Author, Paris, France.
- Organization for Economic Cooperation and Development. (2009). PISA 2006 Technical Report, Author, Paris, France.
- Pajares, F. (1996). Self-efficacy beliefs in achievement settings. Review of Educational Research, 66, 543-578.
- Papanastasiou, C. (2002). School, Teaching, and Family Influence on Students Attitude toward Science: Based on TIMSS data on Cyprus. Studies in Educational Evaluation, 28, 71-86.
- Papanastasiou, C. (2008). A residual analysis of effective schools and effective teaching in mathematics. Studies in Educational Evaluation, 34, 24-30.
- Papanastasiou, E. C., and Ferdig, R. E. (2003). Computer use and mathematical literacy. An analysis of existing and potential relationships. In Proceedings of the Third Mediterranean Conference on Mathematical Education, Athens, Hellas: Hellenic Mathematical Society, pp. 335–342.
- Pelgrum, W.J., & Plomp, T. (2002). Indicators of ICT in mathematics: Status and covariation with achievement measures. In D.F. Robitaille & A.E. Beaton (Eds.), Secondary analysis of the TIMSS data (pp. 317-330). Dordrecht, Netherlands: Kluwer.
- Ravitz, J., Mergendoller, J., and Rush,W. (2002, April). Cautionary tales about correlations between student computer use and academic achievement. Paper Presented at Annual Meeting of the American Educational Research Association, New Orleans, LA.
- Reiser, R. A. (1994). Clark’s invitation to the dance: An instructional designer’s response. Educational Technology, Research and Development 42: 45–48.
- Stevens, J. (1992). Applied Multivariate Statistics for the Social Sciences (2nd ed.). Hillsdale, N.J.: L. Erlbaum Associates.
- Stright, A.D., & Supplee, L.H. (2002). Children's self-regulatory behaviors during teacher-directed, seat- work, and small-group instructional contexts. Journal of Educational Research, 95, 235-244.
- Valijarvi, J., Linnakyle, P., Kupari, P., Reinikainen, P., & Arffman, I. (2000). The Finish Success in PISA-Some Reasons behind It (WWW page). URL http://www.jyu.fi/ktl/pisa/publication1.pdf
- Van de Grift,W. J. C. M. , & Houtveen, A. A. M. (2006). Underperformance in primary schools. School Effectiveness and School Improvement, 17, 255–273.
- Von Secker, C., & Lissitz, R. W. (1999). Estimating the impact of instructional practices on student achievement in science. Journal of Research in Science Teaching, 36, 1110–1126.
- Vrasidas, C., and McIsaac, M. (2001). Integrating technology in teaching and teacher education: Implications for policy and curriculum reform. Educational Media International 38: 127– 132.
- Weller,H. (1996). Assessing the impact of computer-based learning in science. Journal of Research on Computing in Education 28: 461–486.
- White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91, 461-481.
- White, S. W., Reynolds, P. D., Thomas, M. M., & Gitzlaff, N.J. (1993). Socioeconomic status and achievement revisited. Urban Education, 28, 328-343.
- Yalcinalp, S., Geban, O., and Ozkan, I. (1995). Effectiveness of using computer-assisted supplementary instruction for teaching the mole concept. Journal of Research in Science Teaching 32: 1083–1095.
- Yang, Y. (2003). Dimensions of Socio-economic Status and their Relationship to Mathematics and Science Achievement at Individual and Collective Levels. Scandinavian Journal of Educational Research, 47, 21
- 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.
- Yuretich, R.F., Khan, S.A., & Leckie, R.M. (2001). Active-learning methods to improve student performance and scientific interest in a large introductory oceanography course. Journal of Geoscience Education, 49, 111-119.
- Zimmerman, B.J. (2000). Attaining self-regulation: A social cognitive perspective. In M Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego: Academic Press.