Does the quality of school instruction relate to the use of additional tutoring in science? Comparative analysis of five post-socialist countries

ABSTRACT

In many countries, out-of-school tutoring in academic subjects has become a widespread phenomenon that affects high proportions of students. Scholarly literature has considered various determinants of participation in such tutoring, but school quality has as yet been rather neglected. The aim of the study is to explore the relationship between the quality of school instruction and perceived learning environments on the one hand, and participation in additional instruction in science on the other. Using the Programme for International Student Assessment (PISA) 2015 dataset, we analysed data on five post-socialist countries (Bulgaria, Croatia, Hungary, Poland, and Slovakia). By employing two-level logistic regression models, we estimated the probabilities of taking additional instruction in science in and outside of the school building. Different aspects of learning environments as perceived by students have a somewhat indirect influence, which is mediated through the students’ individual performance in science. Except in Bulgaria, school overall achievement is not related to taking additional tutoring.

KEYWORDS:

• Additional instruction
• science
• school instruction quality
• learning environments quality
• extra tutoring

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Vít Šťastný is a post-doc researcher at the Institute for Research and Development of Education, Faculty of Education, Charles University. His research interests include shadow education and comparative education with emphasis on French- and German-speaking countries.

David Greger is director of the Institute for Research and Development of Education at the Faculty of Education at the Charles University. He works mainly in the field of comparative education, educational policy, sociology of education, and quantitative methodology.

Petr Soukup is a statistician and sociologist at the Faculty of Social Sciences at the Charles University. He is mainly interested in advanced statistical techniques (multilevel models, structural equation models, categorical data analysis). He focuses substantively on sociology of education and environmental sociology.

Notes

1 As the research did not encompass fee-free tutoring, the prevalence of tutoring (without the distinction of its paid nature) could be even higher.

2 The other countries to choose this option were Australia, Belgium, Denmark, Germany, Greece, Hong Kong, Iceland, Italy, Korea, Latvia, Lithuania, Peru, Spain, Slovenia, Thailand, United Kingdom, Beijing-Shanghai-Jiangsu-Guangdong (B-S-J-G; China), and Spain (Regions). Although another post-socialist country, Slovenia, administered the educational career questionnaire, it is not included in this paper because several variables of interest (PERFEED, TDTEACH, ADINST) are missing in the international PISA dataset.

3 In the scholarly literature on shadow education, the use of large-scale international assessments (such as PISA or the Trends in International Mathematics and Science Study [TIMSS]) in the analysis of shadow education has been criticised for producing biased results (for a summary, see Bray & Kobakhidze, 2014), mainly due to inconsistencies in the definition of the central concept of private tutoring/shadow education. Private supplementary tutoring may be defined as “tutoring in academic subjects which is provided by the tutors for financial gain and which is additional to the provision by mainstream schooling” (Bray & Kwok, 2003, p. 612). The concept of additional instruction employed by PISA 2015 satisfies two of the three attributes of private supplementary tutoring (it is additional to the regular classes and concerns science). Regarding the third characteristic (privateness/paid nature), the studies of shadow education usually omit unpaid fee-free tutoring, based on the assumption that free tutoring provided by teachers is a part of their duties in schools (Kobakhidze, 2018, p. 185), and the provision of such extra lessons given to needy students voluntarily by mainstream teachers is usually put aside because it raises different issues than paid tutoring (Bray, 2003). In the case of the PISA data, assumptions have to be made about whether the additional instruction (tutoring) is paid for by parents/students or not, because the PISA questionnaire does not differentiate between paid and unpaid tutoring and the variable mixes shadow education with fee-free tutoring.

4 Correlation matrices between variables of learning environments are in Appendix 2.

5 Although this variable was not directly related to ability grouping in science lessons, it was included in order to keep to the initial conceptual framework.

6 Although the OECD (2011) report analysed a different edition of PISA than is used in our article (and thus the wording of the questions measuring the concept of out-of-school learning was slightly different), we assume that the patterns of use and the identified factors underlying participation in out-of-school lessons might still be valid for our study and analysis of PISA 2015, which is why we include gender and ESCS as control variables.

7 That is, the weight retains the final number of respondents and does not result in a recalculation of the sample to the population size.

Additional information

Funding

This work was supported by the Czech Science Foundation under Grant number P402/12/G130.