Predictors of Attitudes about Science of Students in PISA 2006: A Comparison between Europe and Asia

Author(s):

Piyathip TinnawornIsabel Menezes

Conference:

ECER 2010

Network:

9. Assessment, Evaluation, Testing and Measurement

Format:

Paper

Session Information

09 SES 07 B, International Large-Scale Assessments of Student Performance I

Paper Session

Time:

2010-08-26

15:30-17:00

Room:

P673, Porthania

Chair:

Josef Basl

Contribution

In contemporary societies, science is relevant to everyone’s life, and an understanding of science is an essential tool – thus justifying why educational reform agendas focused on a general scientific literacy as an important goal to be achieved (AAAS, 1989; Lee & Fradd, 1996). Empirical studies show that the relationship between formal scientific knowledge and attitudes toward science is significant and positive (Sturgis & Allum, 2004; Bybee, 2008); additionally, scientific interest is strongly related to family background characteristics, particularly parental education, income, and young adults’ socioeconomic outcomes (Sandefur et al, 2005). Some studies pointed that father’s socioeconomic characteristics and occupation were important predictors among boys (Mark, 2008). The literature also has some major findings about the variables which are related to students’ learning achievement, for instance, significant and positive relationship between the student-teacher relations and achievement (Hill & Rowe, 1998); a strong relationship between school climate and achievement (Willms, 1992); a strong and positive relationship between home environment and achievement (Ferry, et al., 2000).

This paper considers the relative influence of students’ characteristics, family background, school characteristic and cultural context in the attitudes about science in Europe and Asia.

Method

The OECD’s 2006 Programme for International Student Assessment (PISA) has been used in this study. PISA 2006 is a well known database to investigate student achievement of more than 400,000 15-year-old students from 57 countries. This database focus on students’ competencies in science but the assessment also included reading and mathematics and collected data on student, family and institutional factors that could help to explain differences in performance. We will present a cross-cultural study comparing the predictors of attitudes in science using data from European and Asian countries that can be considered as “high-achievers” (Belgium N = 8,856 and Korea N = 5,175) and “low-achievers” (Portugal N = 5,109 and Thailand N = 6,192); our goal is to perform a secondary analysis to compare the predictors of attitudes in science.

Expected Outcomes

It is expectable that the results will reveal differences in the sets of predictors that explain the differences of attitudes of 15 year old students about science. European and Asian countries have specific cultural characteristics which are educationally relevant and account for a strong variation of the results. For instance, our preliminary analyses show that books at home, educational level of mother have a different predictive power in European and Asian countries, and the same can be said about individual-level variables (e.g., gender, grade). Additionally, the fact that we are comparing countries with mean results above or below the international mean might also reveal different patterns. Results will be discussed in terms of their implications for a comprehensive understanding of cross-cultural differences in educational research.

References

American Association for the Advancement of Science (AAAS). (1989). Science for all Americans: Project 2061. Washington, DC: AAAS. Bybee, R. W. (2008). Scientific Literacy, Environmental Issues, and PISA 2006: The 2008 Paul F-Brandwein Lecture. Journal of Science Education and Technology, 17 (6), 566-585 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. Hill, P. W., & Rowe, K. J. (1998). Modeling educational effectiveness in classroom: The use of multi-level structural equations to model students’ progress. Educational Research and Evaluation, 4 (4), 307-347. Lee, O., & Fradd, S. H. (1996). Literacy skills in science learning among linguistically diverse students, Science Education, 80, 651-671. Mark, G. N. (2008). Gender Differences in the Effects of Socioeconomic Background: Recent Cross-National Evidence. Journal of International Sociology, 23 (6), 845-863. Sandefur, G. D., Eggerling-Boeck, J., & Park, H. (2005). “Off to a Good Start? Postsecondary Education and Early Adult Life” in On the Frontier to Adulthood. Chicago and London: University of Chicago Press, 292-320. Sturgis, P., & Allum, N. (2004). Science in society: re-evaluating the deficit model of public attitudes, Public Understanding of Science, 13 (1), 55-75. Willms, J. D. (1992). Monitoring school performance: A guide for educators. Washington, DC: Falmer.

Author Information

Piyathip Tinnaworn

University of Porto, Portugal

Faculty of Psychology and Educational Sciences

Porto

Isabel Menezes