Session Information
24 SES 13, Mathematical Thinking and Achievement
Paper/Poster Session
Contribution
Mathematical literacy is one of the core competencies for all students, and its expectation is institutionalized in modern education. Therefore, mathematics becomes a taken-for-granted subject that must appear in the class schedule in school whatever is located in a highly modernized metropolis or a peripheral rural region. However, given its abstract and logical character, it is deemed as one of the most challenging subjects for teachers and students.
Factors that foster students’ mathematical performance is an area that is highly relevant for educational practitioners. Apart from well -developed students’ individualistic explanations such as mathematics self-concept, mathematical interest, and mathematics anxiety, the scholarly focus now shifts to the effect of teachers’ pedagogies and school resources. Discussion of the importance of the classroom and school environment on academic performance, net of the individualistic causes such as students’ cognitive abilities, psychological states and their family background deserves our attention (on general academic performance: Chiu and Khoo 2006; Gamoran et al. 2000; Lee 2000; Parcel and Dufur 200; Opdenakkar and Damme 2007; Rutter and Maughan 2002; Valenzuela et al. 2014; on mathematical performance: Chiu and McBridge-Chang 2005; Lee et al. 1997; Welch et al. 1982). Previous studies identify some school characteristics are nominated as factors that bolster students’ mathematics learning, they are (1) teachers’ instructional and pedagogical skills (Bodovski and Farkas 2007; Boaler 2002; Zakaria et al. 2013); (2) good school-based curriculum (Broh 2002; Lee et al. 1997) and (3) stable and cooperative school-family relations (Dika and Singh 2002; Morgan and Soerensen 1999) etc. These school characteristics are more “real” and hardware aspect of school organizations, implying they are correlated with the tangible school financial and human resources. Literature also identifies the effect of more dynamics dimension of school environment on student learning, namely (4) the composition of student bodies and peer interaction (Opdenakker and Van Dammn 2001; Rumberger and Palardy 2005; Thrupp et al. 2002). The methodological tools to test against these explanations typically involve multi-level hierarchical regression as it involves both students’ individualistic explanations and teachers/schools level explanation.
This study, built on the well established and verified individualistic and school-level explanations, tries to demonstrate an interesting phenomena in Hong Kong: most of the variations in the instrument of positive psychological states of students - which is perceived as strong predictors of mathematics performance - are explained by the between school differences rather than within school differences. On the other hand, as the government partly or fully subsidizes nearly most of the schools, each school received similar governmental input regarding the number of teachers and other school facilitates. Tsang (2012) documented the effort of government in making sure all students in the Hong Kong education system received an equal amount of per-student investment from the 1980s onward. If the equality effort is successful, schools should be no substantial differences in term of the school resources. And it is then predicted the school effect on mathematics performance should be very similar among school given all schools received the same amount of government input (i.e., the quality of teacher team and facilities should be very similar). However, school variations in academic performance (even within public schools) were evident in both PISA data and local literature (e.g., Lo et al. 1997). It means that variations between schools in Hong Kong mostly explained variations in mathematics ability. In other words, students’ mathematics ability is depended on the school's students attending. In this context, we are formulating the following research questions.
Method
Main research question: To what extent the literature of school effect on the student performance - developed in western countries- can apply to an Asian metropolis like Hong Kong? How do students’ psychological factors affect their mathematics performance varying by schools? Method: The Program for International Student Assessment (PISA) has been collecting the academic performance of 15-year-old students’ from more than sixty-five countries since 2000. Each year, the assessment focuses on different themes including reading, mathematics, and science literacies. In 2012, PISA focused on students’ mathematics learning well (Prensel et al. 2013). The 2012 PISA Hong Kong sample is a two-level data structure consisting of school-level and student-level information. The student-level data has information from both students and their parents. The school-level information is made up of a questionnaire filled by each school’s principal. The student level variables that I use in this study are mathematics self-efficacy, mathematics anxiety, perseverance, openness for problem solving, SES, and parent-perceived school quality. The school-level variables that I use are school autonomy, teacher participation, educational resources, and teacher shortage. SES is calculated from the highest occupational status of parents, highest educational level of parents, family wealth, cultural possessions, and home educational resources. My analytical sample size is 1406 after using listwise deletion to remove all missing values. Multilevel Modelling was used for the data analysis.
Expected Outcomes
We took the school level factors into account to understand how they influence students’ psychological variables and mathematics performance in the Hong Kong context. The result suggested that the school level accounts for a substantial portion for students’ differences. In the student-level, it is not surprising that SES was the significantly positively related to mathematics performance. Among the affective factors, we found only self-efficacy was significantly positively related to students’ mathematics. In contrast, math anxiety was significantly negatively related to students’ performance. In the school level, only teacher autonomy was negatively significant related to students’ mathematics. In other words, when teachers had a great extent of autonomy, students had lower mathematics performance. This phenomenon may be related to Hong Kong educational system- a centralized educational system in which only a central government decides school resources, the curriculum, and examination (Kwan and Li 2015). Thus, if teachers have a great level of autonomy to decide teaching materials and the mathematics content, students’ learning process may not be the same as the official mathematics content regulated by the government. As a result, they may be behind the official criteria of mathematics abilities. Thus, the centralized educational system may serve one of the explanations to explain the negative effect of teacher autonomy on students’ mathematics performance. Regarding the effect of school resources, there was no significant effect on mathematics performance. This result is in line with the previous studies that pointed out schools receive a similar amount of resources in Hong Kong. Also, the slope of SES for the school level did not have a significant relationship with mathematics performance. Altogether, it indicates students’ mathematics performance was influenced by the individual SES, self-efficacy, and teacher autonomy in the Hong Kong context.
References
Boaler, J. (2002). Experiencing school mathematics: Traditional and reform approaches to teaching and their impact on student learning. Routledge. Bodovski, K., & Farkas, G. (2007). Mathematics growth in early elementary school: The roles of beginning knowledge, student engagement, and instruction. The Elementary School Journal, 108(2), 115-130. Broh, B. A. (2002). Linking extracurricular programming to academic achievement: Who benefits and why?. Sociology of education, 69-95. Chiu, M. M., & Khoo, L. (2005). Effects of resources, inequality, and privilege bias on achievement: Country, school, and student level analyses. American Educational Research Journal, 42(4), 575-603. Dika, S. L., & Singh, K. (2002). Applications of social capital in educational literature: A critical synthesis. Review of educational research, 72(1), 31-60. 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, 128-150. Lee, V. E. (2000). Using hierarchical linear modeling to study social contexts: The case of school effects. Educational psychologist, 35(2), 125-141. Morgan, S. L., & Sørensen, A. B. (1999). Parental networks, social closure, and mathematics learning: A test of Coleman's social capital explanation of school effects. American Sociological Review, 661-681. Opdenakker, M. C., & Van Damme, J. (2001). Relationship between school composition and characteristics of school process and their effect on mathematics achievement. British educational research journal, 27(4), 407-432. Opdenakker, M. C., & Damme, J. V. (2007). Do school context, student composition and school leadership affect school practice and outcomes in secondary education?. British educational research journal, 33(2), 179-206. Parcel, T. L., & Dufur, M. J. (2001). Capital at home and at school: Effects on student achievement. Social forces, 79(3), 881-911. Rumberger, R. W., & Palardy, G. J. (2005). Does segregation still matter? The impact of student composition on academic achievement in high school. Teachers college record, 107(9), 1999. Rutter, M., & Maughan, B. (2002). School effectiveness findings 1979–2002. Journal of school psychology, 40(6), 451-475. Thrupp, M., Lauder, H., & Robinson, T. (2002). School composition and peer effects. International journal of educational research, 37(5), 483-504. Tsang (2012). An analysis of Hong Kong SAR education policy (in Chinese). Hong Kong: Joint Publisher Van de Werfhorst, H. G., & Mijs, J. J. (2010). Achievement inequality and the institutional structure of educational systems: A comparative perspective. Annual review of sociology, 36, 407-428. Zakaria, E., Solfitri, T., Daud, Y., & Abidin, Z. Z. (2013). Effect of cooperative learning on secondary school students’ mathematics achievement. Creative Education, 4(02), 98.
Search the ECER Programme
- Search for keywords and phrases in "Text Search"
- Restrict in which part of the abstracts to search in "Where to search"
- Search for authors and in the respective field.
- For planning your conference attendance you may want to use the conference app, which will be issued some weeks before the conference
- If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.