Main Article Content
Abstract
Purpose ― This study investigates the factors affecting the learning outcomes of Asian students.
Methods ─ The effects of both educational inputs and economic and socioeconomic variables on the PISA scores of 10 Asian countries (Hong Kong, Indonesia, Japan, Singapore, Macau, Korea, Turkey, Israel, Qatar, and Thailand) for the years 2006, 2009, 2012 and 2015 were examined using unbalanced panel data.
Findings ─ The results show that country-level economic factors strongly affect academic achievement. Furthermore, country-level economic factors dominate the other explanatory factors in the numerical and statistical sense.
Implication ─ The findings provide valuable information for educators, policymakers, and researchers aiming to develop efficient educational strategies to improve educational quality. Furthermore, the results offer policy suggestions for addressing factors that impact the quality of education both at the national and international levels.
Originality ─ This research enhances the current body of knowledge by investigating how economic and socioeconomic variables affect students' math, science, and reading performance, particularly emphasizing Asian countries.
Keywords
Article Details
Copyright (c) 2024 Ebru Çağlayan Akay, Zamira Oskonbaeva
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References
Anil, D., & Ozer, Y. (2012). The effect of the aim and frequency of computer usage on student achievement according to PISA 2006. Procedia-Social and Behavioral Sciences, 46, 5484-5488. https://doi.org/10.1016/j.sbspro.2012.06.462
Aristizábal, G.C., Giménez, G., & Ximénez de Embún, D.P. (2017). Educational inequalities in Latin America, PISA 2012: causes of differences in school performance between public and private schools. Revista De Educacion, 376, 33-61.
Areepattamannil, S., & Caleon, I. S. (2013). Relationships of cognitive and metacognitive learning strategies to mathematics achievement in four high-performing East Asian education systems. The Journal of Genetic Psychology, 174(6), 696-702. https://doi.org/10.1080/00221325.2013.799057
Baltagi, B. H., & Chang, Y.J. (1994). Incomplete panels: A comparative study of alternative estimators for the unbalanced one-way error component regression model. Journal of Econometrics, 62(2), 67-89.
Baltagi, B. H., & Li, Q. (1990). A Lagrange multiplier test for the error components model with incomplete panels. Econometric Reviews, 9, 103-107. https://doi.org/10.1080/07474939008800180
Baltagi, B.H. (2008). Econometric Analysis of Panel Data. John Wiley & Sons Ltd., Chichester.
Baltagi, B.H. (1985). Pooling cross-sections with unequal time-series lengths. Economics Letters, 18, 133-136. https://doi.org/10.1016/0165-1765(85)90167-3
Bedard, K., & Dhuey, E. (2006). The persistence of early childhood maturity: International evidence of long run age effects. Quarterly Journal of Economics, 121(4), 1437-1472. https://doi.org/10.1162/qjec.121.4.1437
Benfratello, L. (2014). Random effects regression for panel data. In A.C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2402
Biørn, E. (1981). Estimating economic relations from incomplete cross-section/time-series data. Journal of Econometrics, 16, 221-236. https://doi.org/10.1016/0304-4076(81)90109-3
Bosworth, R. (2014). Class size, class composition, and the distribution of student achievement. Education Economics, 22(2), 141-165. https://doi.org/10.1080/09645292.2011.568698
Bouhlila, D. S. (2017). Parents' education and literacy skills: Evidence on inequality of socioeconomic status in Arab countries. World Development Perspectives, 5, 34-43. https://doi.org/10.1016/j.wdp.2017.02.006
Bray, M., & Kwok, P. (2003). Demand for private supplementary tutoring: Conceptual considerations, and socioeconomic patterns in Hong Kong. Economics of Education Review, 22, 611-620. https://doi.org/10.1016/S0272-7757(03)00032-3
Byun, S., & Park, H. (2012). The academic success of East Asian American youth: The role of shadow education. Sociology of Education, 85, 40-60. https://doi.org/10.1177/0038040711417009
Chao, R., & Tseng, V. (2002). Parenting of Asians. In M.H. Bornstein (Ed.), Handbook of Parenting, Vol. 4: Social Conditions and Applied Parenting (pp. 59-93). Erlbaum.
Cheang, C. J. Y., & Goh, E. C. L. (2018). Why some children from poor families do well: An in-depth analysis of positive deviance cases in Singapore. International Journal of Qualitative Studies on Health and Well-being, 13(sup1).https://doi.org/10.1080/17482631.2018.1563431
Cheung, K.C. (2017). The effects of resilience in learning variables on mathematical literacy performance: A study of learning characteristics of the academic resilient and advantaged low achievers in Shanghai, Singapore, Hong Kong, Taiwan and Korea. Educational Psychology, 37(8), 965-982. https://doi.org/10.1080/01443410.2016.1194372
Coleman, J. S., Campbell, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., ... & York, R. L. (1966). Equality of Educational Opportunity. U.S. Department of Health, Education, and Welfare, U.S. Government Printing Office.
Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19(2), 294-304. https://doi.org/10.1037/0893-3200.19.2.294
Fredriksson, P., & Ockert, B. (2005). Is early learning really more productive? The effect of school starting age on school and labour market performance. IZA Discussion Paper no. 1659.
Gamazo, A., & Martínez-Abad, F. (2020). An exploration of factors linked to academic performance in PISA 2018 through data mining techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01106
Giambona, F., & Porcu, M. (2018). School size and students' achievement: Empirical evidences from PISA survey data. Socioeconomic Planning Sciences, 64, 66-77. https://doi.org/10.1016/j.seps.2017.12.007
Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson.
Gujarati, D. N. (2016). Basic Econometrics (U.K. ed.). Pearson. https://doi.org/10.1007/978-1-137-37502-5
Hanushek, E. A., & Woessmann, L. (2011a). How much do educational outcomes matter in OECD countries? Economic Policy, 26(67), 427-491. https://doi.org/10.1111/j.1468-0327.2011.00265.x
Hanushek, E. A., & Woessmann, L. (2011b). The economics of international differences in educational achievement. In E.A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the Economics of Education, 3, 89-200. https://doi.org/10.1016/B978-0-444-53429-3.00002-8
Halle, T. G., Kurtz-Costes, B., & Mahoney, J. L. (1997). Family influences on school achievement in low-income, African American children. Journal of Educational Psychology, 89(3), 527-537. https://doi.org/10.1037/0022-0663.89.3.527
Ho, S. C. (2010). Family influences on science learning among Hong Kong adolescents: What we learned from PISA. International Journal of Science and Mathematics Education, 8(3), 409-428. https://doi.org/10.1007/s10763-010-9198-3
Hossain, R., Roy, C. K., & Akter, R. (2022). The effects of foreign direct investment and trade openness on economic growth amid crises in Asian economies. Economic Journal of Emerging Markets, 14(2), 231-243. https://doi.org/10.20885/ejem.vol14.iss2.art7
Jerrim, J. (2015). Why do East Asian children perform so well in PISA? An investigation of Western-born children of East Asian descent. Oxford Review of Education, 41(3), 310-333. https://doi.org/10.1080/03054985.2015.1028525
Jerrim J., & Choi, A. (2014). The mathematics skills of school children: How does England compare to the high-performing East Asian jurisdictions? Journal of Education Policy, 29(3), 349-376. https://doi.org/10.1080/02680939.2013.831950
Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Examining students' achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece. International Journal of Educational Research, 79, 106-115. https://doi.org/10.1016/j.ijer.2016.05.013
Kaplan, L. S., & Owings, W.A. (2001). Teacher quality and student achievement: Recommendations for principals. NASSP Bulletin, 85(628), 64-73. https://doi.org/10.1177/019263650108562808
Khan, Z., Rabbi, F., Ahmad, M., & Siqun, Y. (2019). Remittances inflow and private investment: A case study of South Asian economies via panel data analysis. Economic Research-Ekonomska Istraživanja, 32(1), 2723-2742. https://doi.org/10.1080/1331677X.2019.1655464
Lau, K.C., & Lam, T.Y. (2017). Instructional practices and science performance of 10 top-performing regions in PISA 2015. International Journal of Science Education, 39(15), 2128-2149. https://doi.org/10.1080/09500693.2017.1387947
Lam, T.Y., & Lau, K.C. (2014). Examining factors affecting science achievement of Hong Kong in PISA 2006 using hierarchical linear modeling. International Journal of Science Education, 36(15), 2463-2480. https://doi.org/10.1080/09500693.2013.879223
Leung, F. (2006). Mathematics education in East Asia and the West: Does culture matter? In F. Leung, K.-D. Graf, & F. Lopez-Real (Eds.), Mathematics Education in Different Cultural Traditions: A Comparative Study of East Asia and the West (pp. 21-46). Springer.
Li, H., Lei, P.W., & Pace, C.L. (2013). Reading subskill differences between students in Shanghai-China and the U.S.: Evidence from PISA 2009. Educational Research and Evaluation, 19(6), 490-509. https://doi.org/10.1080/13803611.2013.803439
Lin, S.W., Tzou, H.I., Lu, I.C., & Hung, P.H. (2021). Taiwan: Performance in the Programme for International Student Assessment. In N. Crato (Ed.), Improving a Country's Education (pp. 179-194). Springer. https://doi.org/10.1007/978-3-030-59031-4_10
Lynn, R., & Meisenberg, G. (2010). National I.Q.s calculated and validated for 108 nations. Intelligence, 38, 353-360. https://doi.org/10.1016/j.intell.2010.04.007
Manning, M. L. (1998). Gender differences in young adolescents' mathematics and science achievement. Childhood Education, 74, 168-171. https://doi.org/10.1080/00094056.1998.10522697
Marks, G. N. (2016). The relative effects of socioeconomic, demographic, non-cognitive and cognitive influences on student achievement in Australia. Learning and Individual Differences, 49, 1-10. https://doi.org/10.1016/j.lindif.2016.05.012
Meroni, E. C., Vera-Toscano, E., & Costa, P. (2015). Can low skill teachers make good students? Empirical evidence from PIAAC and PISA. Journal of Policy Modeling, 37(2), 308-323. https://doi.org/10.1016/j.jpolmod.2015.02.006
Meng, L., Muñoz, M., Hess, K.K., & Liu, S. (2017). Effective teaching factors and student reading strategies as predictors of student achievement in PISA 2009: The case of China and the United States. Educational Review, 69(1), 68-84. https://doi.org/10.1080/00131911.2016.1155537
Mikk, J. (2015). Explaining the difference between PISA 2009 reading scores in Finland and Estonia. Educational Research and Evaluation, 21(4), 324-342. https://doi.org/10.1080/13803611.2015.1062400
Myrberg, E. (2007). The effect of formal teacher education on reading achievement of 3rd-grade students in public and independent schools in Sweden. Educational Studies, 33(2), 145-162. https://doi.org/10.1080/03055690601068311
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