09 SES 03 A, Research into the Predictive Validity of Individual and Contextual Characteristics for Academic Success and Returns on Education
This study is conducted in order to investigate the relationships between different factors affecting educational competitiveness, which is crucial to enhancing national competitiveness in every country, and to put forward policy implications whereby each country may raise the level of its educational competitiveness. It is generally accepted that educational competitiveness can greatly affect national competitiveness. International institutions such as the International Institute of Management and Development (IMD) and the World Economic Forum (WEF) have published reports on the national competitiveness of different countries. Educational competitiveness, as a sub-branch of national competitiveness, is regarded as an important element in national development. Hence, researchers and practitioners have primarily concentrated on which factors are most strongly associated with enhancing educational competitiveness, and on how to strengthen it.
The international institutions which evaluate students’ achievements from a comparative perspective are the International Association for the Evaluation of Educational Achievement (IEA) and the OECD. The former publishes Trends in International Mathematics and Science Study (TIMSS), while the latter publishes the reports of the Programme for International Student Assessment (PISA). TIMSS and PISA are concerned with evaluating students’ achievements in, respectively, mathematics and science, and reading, mathematics, and science. This study selects PISA score as a final indicator to represent educational competitiveness.
It is generally understood that many factors can affect the educational competitiveness of a country, and many studies have indicated that a number of factors can be involved in improving the educational sector in one country. Here, we address the potential factors associated with educational competitiveness and their interrelationships.
First, we hypothesize that per capita GDP is associated with total expenditure on education.The expenditure of OECD member countries on education increased by 28 percent between 2000 and 2006, reaching an average annual growth rate of 4 percent. In spite of the fact that expenditure on education nowadays accounts for a large proportion of GDP, and also has been increasing constantly, there have been few studies proving that growth in education spending leads to growth in educational quality. In the meantime, some studies (Choi, 2008; Shin and Joo, 2013) have concluded that accumulated per capita expenditure on education has positively affected PISA score. On the basis of these research findings, this study hypothesizes that per capita GDP, total expenditure on education, and total per capita expenditure on education affect educational competitiveness, and that per capita GDP also affects total expenditure on education as a percentage of GDP, and total per capita expenditure on education.
Second, we hypothesize that parents’ concerns about education is associated with educational competitiveness. It is important, in relation to educational competitiveness, whether parents are strongly concerned about a student’s future career or not. This is more important in Asian than in Western societies. Parental concerns about children’s education can be represented by total expenditure on education burdened by the private sector. There have been few studies examining the relationships between total expenditure on education burdened by the private sector and educational competitiveness. Here, following the work of some scholars (Choi, 2008; KEDI, 2010), we hypothesize that private-source expenditure on education as a percentage of GDP is positively associated with educational competitiveness.
Third, we hypothesize that pupil.teacher ratio can affect educational competitiveness. The ratio of students to teaching staff is an important issue as regards the quality of education worldwide. It is assumed that the smaller the number of students a teacher can teach, the greater will be the effectiveness of the teaching.
On the basis of the theoretical discussion above, we suggest the following research questions: Which configurations can affect educational competitiveness as a dependent variable?
Biever, T. and Martens, K. (2011). The OECD PISA study as a soft power in education? Lessons from Switzerland and US, European Journal of Education, 46(1), part 1. Borgonovi, F., Montt, G. (2012). Parental involvement in selected PISA countries and economies. OECD Education working paper no. 73. Choi, Y. C. (2008). Relationships between national competitiveness and decentralization, Korean Association of Local Government Studies Summer Conference Proceedings. Choi, Y. C. and Lee, J. H. (2015). What most matters in strengthening educational competitiveness?: An Application of FS/QCA method. 7th World Conference on Educational Sciences Proceedings. Holzinger, K. and Knill, C. (2008). Theoretical framework: causal factors and convergence expectations, in K. Holzinger, C. Knill and B. Arts (eds), Environmental Policy Convergence in Europe: The impact of international institutions and trade. Cambridge: Cambridge University Press. . IMD (2015). World Competitiveness Yearbook. Geneva: IMD. KEDI (2010). Analysis of Effects of Education on National Competitiveness. Seoul: KEDI. Lee, C. and Lee, K. H. (2006). Analysis of the Conditions of Korea Education Competiveness Index of IMD World Competitiveness Yearbook. The Journal of Korean Education, 33(1), 173-197. Lingard, B. and Grek, S. (2007). The OECD, indicators and PISA: an exploration of events and theoretical perspectives. Edinburgh, ESRC/ESP Research Project. OECD (2015). Education at a Glance. Paris: OECD. OECD (2015). PISA 2014 Results in Focus. Paris: OECD. Ragin, C. (2000). Fuzzy-Set Social Science. Chicago: The University of Chicago Press. Shin, H. S. and Joo, Y. H. (2013). Global governance and educational policy in Korea, Korean Journal of Educational Research, 51(3), 133-159. Tanzi, V. and Schuknecht, L. (1998). Can small governments secure economic and social well being?, in Grubel, H. (ed.), How To Spend the Fiscal Dividend: What is the optimal size of government? Vancouver: Fraser Institute. Thygeson, M.M., Solberg, L., Asche, S.E., Fontaine, P., Pawlson, L. G., Scholle, S.H. (2011). Using Fuzzy Set Qualitative Comparative Analysis to Explore the Relationship between Medical Homenss and Quality. Health Research and Education Trust. DOI: 10.1111/J.1475. Research Article. Yavuz, M. (2009). Factors that affect mathematics-science (MS) scores in the secondary education institutional exam: an application of structural equation modeling, Educational Sciences: Theory and Practice, 9(3), 1557-1572.
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