09 SES 02 B, Relating Student Attitudes and Teaching Practices to Science Achievement
Learning in the field of Science, Technology, Engineering, and Mathematics (STEM) is promoted in the educational policies of numerous countries. The achievement of students in Croatia in science and mathematics is low in comparison to their achievement in other school subjects (Burušić, Babarović, Šakić, 2008). The debates about what determines achievement in STEM school subjects in primary school still persist. Several STEM-relevant variables show a significant association with achievement in math and science, including student level variables, school level variables and variables of child’s broader social environment (Hattie, 2009). The aim of this paper is to explain how school achievement in STEM school subjects among primary school students in Croatia is shaped by structurally different spheres of influence, namely by students’ home environment and family, leisure activities, hobbies and school activities. Huitt’s (2003) Transactional Model of the Teaching/Learning Process is used as a theoretical framework. The model proposes that the main variables that impact students’ academic achievement are home and other context variables, school level variables and classroom level variables. When student level characteristics are considered, a small gender differences in school achievement are found in mathematics (Frost, Hyde, & Fennema, 1994; Hyde, Fennema, & Lamon, 1990) and in science (Murphy & Whitelegg, 2006), with girls outperforming boys. Girls’ and boys’ also differ in their motivational orientation to STEM subjects, with girls prefering biological sciences and chemistry, whereas boys are more motivated in physical sciences (Weinburgh, 1995) and in computer-related tasks (e.g. Hayward et al., 2003). In predicting school achievement, it is important to consider not only the characteristics of the students, but also their social environment (Keith & Fine, 2005). When family background is considered, research has shown that socioeconomic status (including parental education, employment status and income) is the best predictor of school achievement (Dahl & Lochner, 2005; Milne & Plourde, 2006) and a good predictor of math and science achievement in primary school (Sirin, 2005). Likewise, among all other aspects of parents’ behavior, parents’ involvement in their children’s schooling is a powerful predictor of academic success (Gutman & Midgley, 2000; Fan & Chen, 2001). Furthermore, the impact of informal, out-of-school activities on STEM educational outcomes are also considerable (Braund & Reiss, 2004). Taking into account the importance of outside of school context and the family variables, in this research we are looking for evidence that achievement can also be improved by positive experience in formal school contexts. We assume that experience during the science and math school lessons can significantly add to explanation of achievement, above family influences and out of school activities. Therefore, the aim of this study is to identify the contribution of students’ attitudes to school science and experiences with STEM school subjects in explaining STEM school achievement, after controlling individual characteristics, family influences and experience in out-of-school activities.
Archer, L, Osborne, J, DeWitt, J, Dillon, J, Wong, B & Willis, B (2013). ASPIRES: young people’s science and career aspirations, age 10-14. King’s College London, London. Braund, M., & Reiss, M. J. (2004). Learning science outside the classroom. Psychology Press. Burušić, J., Babarović, T., & Šakić, M. (2008). Vanjsko vrednovanje obrazovnih postignuća u osnovnim školama Republike Hrvatske: Učenici 8. razreda, školska godina 2007/2008., istraživački izvještaj. Zagreb: NCVVO/ Institut društvenih znanosti Ivo Pilar Dahl, G. B., & Lochner, L. (2005) The impact of family income on child achievement. Cambridge, MA: National Bureau of Economic Research Fan, X., & Chen, M. (2001). Parental involvement and students' academic achievement: A meta-analysis. Educational psychology review, 13(1), 1-22. Frost, L. A., Hyde, J. S., & Fennema, E. (1994). Gender, mathematics performance, and mathematics-related attitudes and affect: A meta-analytic synthesis. International Journal of Educational Research, 21(4), 373-385. Gutman, L. M., & Midgley, C. (2000). The role of protective factors in supporting the academic achievement of poor African American students during the middle school transition. Journal of youth and adolescence, 29(2), 223-249. Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge Hayward, B., Alty, C., Pearson, S., & Martin, C. (2003). Young people and ICT 2002. London, DfES. Huitt, W. (2003). A transactional model of the teaching/learning process: A summary. Educational Psychology Interactive. Valdosta, GA: Valdosta State University Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics performance: a meta-analysis. Psychological bulletin, 107(2), 139. Keith, T. Z., Fine, J. G. (2005). Multicultural influences on school learning: Similarities and differences across groups. U C. L. Frisby i C. R. Reynolds (Ur), Comprehensive Handbook of Multicultural School Psychology (pp. 457-482). New York: Wiley Milne, A., & Plourde, L. A. (2006). Factors of a low-SES household: What aids academic achievement? Journal of Instructional Psychology, 33(3), 183. Murphy, P., & Whitelegg, E. (2006). Girls in the Physics Classroom: A Review of the Research on the Participation of Girls in Physics. Institute of Physics. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of educational research, 75(3), 417-453. Weinburgh, M. (1995). Gender differences in student attitudes toward science: A meta‐analysis of the literature from 1970 to 1991. Journal of Research in Science Teaching, 32(4), 387-398.
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