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A cross-national analysis of the student- and school-level factors affecting the demand for private tutoring

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Abstract

Private tutoring has become a worldwide phenomenon, yet there is little empirical evidence for the main factors leading the demand for private tutoring across nations. Using data from the Third International Mathematics and Science Study of 2003, this study classified the countries into four different groups according to the proportion of student participation in private tutoring and student achievement. Then, the study explored student- and school-level factors influencing the demand for private tutoring. From the HGLM analysis, the results revealed that the demand for private tutoring in Korea and Taiwan, which have higher participation rates in private tutoring and high-school-quality levels, is mostly explained by student-level variables (educational aspirations, instrumental motivation, self-confidence, and father’s education) and school context variables (the community size and school SES). Meanwhile, the demand for private tutoring in the Philippines and Romania, both of which have high incidences of private tutoring and low-school-quality levels, varies widely among schools, and many of the school process variables (e.g., the use of remedial classes, amount of school homework, frequency of tests, and the use of grouping by ability) account for the relationships with private tutoring.

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Notes

  1. Although there can be some argument against using the simple means of achievement scores as a proxy for school quality, we employed the simple country means as a proxy for quality of school education based on the vast number of OECD publications that discuss the quality of education systems in participating countries using the simple country means. Besides these OECD publications, there are quite a few studies using simple means of achievement scores as a proxy for school quality (Fuller and Clarke 1994; Hanushek et al. 2007; Lockheed and Levin 2012).

  2. To cross-validate these groupings of countries, we looked at another set of international data from the Programme for International Student Assessment (PISA) in 2006 despite the fact that these data differ in terms of the target population and the operational definition of mathematical ability. PISA deals with 15-year-olds, while TIMSS deals with eighth-graders. Moreover, PISA assesses mathematical literacy in more general terms, while TIMSS assesses the mathematical abilities taught in schools. In spite of these differences between the PISA and TIMSS studies, we found that 13 out of the 26 countries which participated in both studies were classified into the same type. If we exclude the group 3 countries in the lower left corner in Fig. 1, 12 out of 19 countries were classified into the same group. The problem with group 3 countries seems to be due to the differences in the mean private tutoring level among PISA and TIMSS participating countries. The mean private tutoring level was 46 % for PISA countries and 49 % for TIMSS countries.

  3. In the case of Romania, a 1994 study of Grade 12 pupils in a national sample found that 32.0 % in rural areas and 58.0 % in urban areas received private supplementary tutoring (UNESCO 2000). Also, the private tutoring courses often cost as much as a full year’s tuition in a private university (World Bank 1996). In the case of South Korea, government statistics indicate that 75.1 % of primary and secondary students received private tutoring, and approximately 20.9 trillion Korean won was spent on private tutoring expenses in 2008 (Korean National Statistics Office 2009). In Taiwan, government statistics indicate that in 1998, 5,536 buxibans (tutoring centers) were providing services to 1,891,096 students (Taiwan Ministry of Education 1999). Also, Zeng (1999: 52) reported that the total income of the academic buxibans was equivalent to US $212 million. Finally, a number of Filipino students are known to enroll in review centers or to hire private tutors to assist them in preparing for examinations and to ensure their admission into prestigious schools in keeping with the growth of structured, outside-school activities such as review centers (CHED CMO No. 49, 2006) in the Philippines along with the practice of high-stakes testing such as the National College Entrance Examinations (NCEE) and National Secondary Achievement Test (NSAT).

  4. Because the HGLM using the dummy dependent variable does not provide information on partitioning the total variation into between-school and within-school variation, we employed the HLM model for this purpose. For the HLM analysis, the dependent variable was how often one participated in private math tutoring in the last year, one indicating “never or almost never” to four indicating “almost every day.”

  5. The marginal effect for a dummy variable was calculated for typical students.

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Authors and Affiliations

  1. Chosun University, Gwangju, Korea

    Kyoung-Oh Song

  2. Department of Education, College of Education, Seoul National University, 599 Kwanak-Ro, Kwanak-Gu, Seoul, 151-748, Korea

    Hyun-Jeong Park

  3. Korea Institute for Curriculum and Evaluation, Seoul, Korea

    Kyong-Ah Sang

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  1. Kyoung-Oh Song
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  2. Hyun-Jeong Park
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Correspondence to Hyun-Jeong Park.

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Song, KO., Park, HJ. & Sang, KA. A cross-national analysis of the student- and school-level factors affecting the demand for private tutoring. Asia Pacific Educ. Rev. 14, 125–139 (2013). https://doi.org/10.1007/s12564-012-9236-7

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