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Factors affecting quality of education, educational outcomes and social inequality

Priority areas of development: humanitarian
2016
The project has been carried out as part of the HSE Program of Fundamental Studies.

Direction 1

Researchaimis twofold. First, we aimed to do in-depth analysis of Russian schools’ life. Focus was on how schools define priorities of their development, how they build communication with parents and what practices they use to teach mathematics. Second, we compared teachers’ characteristics and practices in general and vocational schools.

Methodology

We used both qualitative and quantitative methods of analysis. We analysed a series of interviews with the use of axial coding. We worked out and applied a scheme of the lessons video-records analysis. Finally, we used methods of descriptive statistics and correlational analysis while working with quantitative data.

Empirical base of research

We used data of the Russian panel study “Trajectories in Education and Career” (TrEC – http://trec.hse.ru/). We also used data of series of interviews held with teachers, school principals and principals’ assistants and video-records of mathematics lessons in 25 schools.

Research results

We described factors related to how schools define priorities of their development. The process of school self-determination mainly depends on principal views, her (his) style of management, hierarchy of school staff relationship, school traditions and on principal and principal assistants’ roles. Some other characteristics of a school such as school size and students social background are of importance as well. We analysed educational metaphors principals and their assistants regularly use when they describe school priorities and school role. Among them are market metaphor (education is regarded as service and school as a service provider), system metaphor (school as an educational system which functions are determined by the state) and institutionalist metaphor (school as a social institution). These metaphors give birth to four different types of school self-determination: competitive achievement-oriented school, conformal school, good climate oriented school, centres of students social and cultural life.

We analysed types of schools’ relationship with parents. Schools mainly try to involve parents into the following spheres: sponsorship, participation in school events, and control for kids. Parents are rarely being involved into discussion on school curricula, or into working out of a school development strategy or to questions related to teachers’ salary. We analysed practices used by schools for parents’ involvement and described their limitations.

We described teaching practices used at mathematics lessons. First, our research results show that communication with students is usually organized in a directive way at the most lessons. A teacher tends to lead and control every process in the classroom. Students mainly play a passive role carrying out teacher’s instructions.Students have problems in organizing a discussion with each other even when they work in groups. Second, we studied how teachers relate mathematics to real life and other school subjects. Word problems are the most popular type of tasks used to demonstrate how mathematics is related to real life. Teachers focus mainly on relationship of mathematics with physics and design. Finally, teachers prefer to use a rather theoretical way of explanations. They seldom rely on students’ personal real life experience.

We compared teachers’ characteristics and practices in general and vocational schools. General school student are taught by a more experienced and more qualified teachers. In general school, students more often describe real life situations using mathematics concepts and more often deal with Unified State Examination tasks. In vocational schools, students more often work in groups that are made according to their abilities level. They have more projects work as well. Students access to educational resources is more unequal in general schools.

Direction 2

Research aimisto shed more light on how inequality in education is being reproduced/eliminated at the interface between social positions and educational practices.

Research methodology

We used quantitative data analysis, in particular, regression analysis (OLS), logistic regression and decomposition analysis (KHB).

Empirical base of research

Longitudinal data from the study “Trajectories in education and careers” and from the Russian Longitudinal Monitoring Survey - Higher School of Economics (RLMS-HSE), as well from TIMSS and PISA.

Research results

We analyzed how individual income and work attitudes were related to educational practices and the level of education. In general, the level of formal education, as well as participation in informal education programs and intensive use of Internet for personal self-development show positive relationship with income and job satisfaction. The level of formal education and intensive use of Internet are also positively related to satisfaction with work conditions. The character of the relationship is heterogeneous and depends on the individuals’ wealth.

Factors of academic resilience were analyzed. We described the following types of schools based on their social composition and students’ achievements. Among them are resilient schools, nests of resilience, and non-resilient schools. The most important school-level factors that provide students resilience are teachers’ expectations (as perceived by the pupil), social background and performance of classmates and the higher status of the school. The most important factor at individual level is student attitudes toward school and toward the subject. The relationship between these factors and academic performance of pupils was estimated.

We estimated primary (non-direct) and secondary (direct) effects of cultural capital on students’ choice of an educational trajectory after the 9th grade. Analysis shows differences in inequality mechanisms that characterize access to the 10th grade in selective and non-selective schools. Gender inequality was described.

Direction 3

Research aimis to study how students’ interest in mathematics, mathematics self-esteem and anxiety are changing in the end of middle school. We also analyzed how students with different levels of interest in mathematics chose educational institution and specialization after graduating from school.

Research methodology

Structural equation modelling was used for the research purpose.

Empirical base

We used data of the Russian panel study “Trajectories in Education and Career” (TrEC – http://trec.hse.ru/).

Research results

By the end of the middle school, girls (compared to boys) show lower levels of awareness of mathematics importance and usefulness. They have lower self-esteem in mathematics as well. These gender differences are increasing as students pass from the 8th grade to the 9th grade. Instrumental motivation has a significant negative impact on further performance in mathematics for girls. This might be due to the increase in mathematics anxiety. On the other hand, instrumental motivation might have an indirect effect on performance in mathematics. A surprising result was that interest in mathematics have no direct effect on students’ achievements.

New findings were obtained about how individuals choose specializations depending on the level of their interest in mathematics, self-esteem and anxiety in mathematics. Those girls, for whom their interest in the subject is a crucial factor, do not choose STEM specializations. Girls who have chosen STEM specializations perceive their learning experience to be worse compared to boys and compared to girls from other specializations. 

Publications:


Loyalka P., Zakharov A. Does shadow education help students prepare for college? Evidence from Russia // International Journal of Educational Development. 2016. Vol. 49. P. 22-30. doi
Маркина В. М. Репрезентация Других в медиа: (вос)создание стереотипов и контрстратегии изображения инаковости // Мониторинг общественного мнения: Экономические и социальные перемены. 2016. Т. 133. № 3. С. 147-158.