You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content. Maybe the most important quote to make sure that people do read the article : We should keep in the forefront of our minds that the trends discussed here are exactly that—trends.
The last element is to me crucial, therefore I want to add this second quote: To compensate, teachers should offer in the classroom what these children are missing at home. I want to add these recent posts who might interest you: Research shows language gap between rich and poor children begins in infancy The stress of growing up poor can have an impact on the brain functions as an adult research Study: Self-worth boosts ability to overcome poverty In education it pays off to be rich report Discovering more mechanisms of the influence of poverty on learning: How Poverty Molds the Brain Very interesting research: Poverty reduces brainpower needed for navigating other areas of life Low birth-weight tied to academic struggles because of reduced brain volume research.
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Again, these factors lead to a widening of the attainment gap between low- and high-SES students. There is a difficult balance faced by teachers: to recognise the challenges faced by students from low-SES backgrounds, but not to use them as a reason to lower expectations for attainment or behaviour.
High-expectations are not an additional source of stress if coupled with high levels of support. Teachers must offer low-SES students the environment they are missing at home. They must pass on both human and social capital by demonstrating academic knowledge and modelling interactions with others. This must be delivered in a calm and controlled environment, and taught both implicitly and explicitly.
Providing children with a supportive and consistent learning environment in the classroom can lower levels of stress hormones and make cognition possible for all students, regardless of their background or the challenges they face. Students from low-SES backgrounds face many challenges in terms of financial, human and social capital, and are disadvantaged by both family investments and exposure to chronic stress.
Teachers can alleviate some of this stress by providing a calm and supportive environment, while demonstrating high standards, and passing on human and social capital both explicitly and implicitly.
Enjoyed this summary? Subscribe to 52 Papers for a paper each week, summarised, in your inbox. American Educator Spring Why does family wealth affect learning? Scope of Paper Why do wealthy kids usually do better in school than poor kids? Key Findings On average, kids from wealthy families do significantly better at school than kids from poor families. Students from families with low socioeconomic status are exposed to chronic stress, which inhibits brain development and cognitive processing.
It also hinders formation of memories, and therefore learning. Teachers can alleviate some of the disadvantage faced by low-SES students by providing a calm and supportive learning environment. More Detail The background to socioeconomic status On average, kids from wealthy families do significantly better at school than kids from poor families. The measures are ordered from 1 to 5, indicating never, rarely once per month , occasionally 1—2 times per week , frequent 2—3 times per week , and very often 6—7 times a week.
The quality of the school that children attend has a very important influence on their learning behavior and academic achievement. The scale of these indicators ranged from 1 to 5. The higher the value means the higher level of the satisfaction. In the multiple regression analysis, we take the average of these four as the value of the school quality.
Although the subjective evaluation of children may not fully reflect the quality of the school they attend, it still reflects to a great extent their perception and evaluation of the quality of the school. It is an ordinal variable ranging from 1 to 4, with 1 poor, 2 medium, 3 good, and 4 excellent. The scores were standardized according to the province of the child and the grade of enrollment in the analysis.
It is necessary to pay special attention to the fact that the opportunities of secondary education for children in China are rather regional, and the selection of middle schools from elementary schools, of high schools from middle schools, and of colleges from high schools is implemented based on the regional county, city, and province processes gradationally.
In the same way, their competitors are also not country-level students but the peer group in that specific region. Therefore, both the influence of family background and the measurement of academic achievement should be relative and regional based.
For that, the control variables also include gender and ethnicity. Table 1 reports the sample distribution and descriptive statistics of each of the measured and latent variables. In our sample, urban samples took Based on the analysis framework Fig. For the corresponding relationship between latent variables and measured indicators, please refer to Table 1. We set the socio-economic status of the family as the only exogenous variable other than gender, ethnicity, and region.
Third, there is no direct measure for laten variable children's academic achievement in Fig. Fourth, as it can be arbitrary to assume the correlation between the measurement error terms of the variables which is to be adjusted according to LISREL, it is assumed that the error terms of all endogenous variables are not relevant.
Model 1, model 2, model 3, model 4, and model 5 respectively control for the urban and rural areas, family socioeconomic status, and parental education participation scores. This shows that the difference between urban and rural areas is largely due to differences in the socio-economic status of the family.
The higher the parental education participation scores such as checking homework, discussing school issues, etc.
The more satisfied the child is with the school, the higher the score of the benchmark test. Controlling other variables, the benchmark score of the child who participated in the remedial class is 0. Among them, whether the children are on the tutorial class is analyzed with a binary logistic regression approach, and the rest outcomes are analyzed with multiple regression analysis. For every 1-year increase in years of education of parents, their educational participation score would increase by 0.
In terms of educational opportunities, urban children are more likely to participate in extracurricular tutorial classes and attend better-quality schools. The incidence of urban children participating in extracurricular remedial classes was 4.
The enthusiasm for learning among urban children is significantly lower than that among rural children. However, multiple regression analysis cannot simultaneously analyze the intrinsic relationship among the independent variables.
The assumption that all variables are not biased due to measurement error may not be realistically either. The evaluation of the goodness of fit of the structural equation model is a prerequisite for explaining the relationship between the measured and the latent variables. The closer their values are to 1, the better the model fits. RMSEA not only excludes the influence of sample size, but can also perform statistical tests on the values. Table 4 reports the goodness of fit of implementing the model in the total sample and subsamples.
In the hypothetical model Fig. According to the results of goodness-of-fit tests with various subsamples, our hypothetical model fits the inherent structure of data quite well. Table 5 summarizes the relationship between the measured and latent variables.
The analysis shows that the factor loading of the measurement index is statistically significant, and the loading of most measurement indexes reaches 0. This shows that, overall, the indicators used in the analysis have a high degree of validity, and the latent variables are measured well. It should be noted that in the measurement model, the loading of three measurement indicators is less than 0. The loading of parents requiring that their children finishing homework is also less than 0.
Although the loading of the log of household per capita income is less than 0. Therefore, it is not a measurement that we focus on. Figure 3 and Table 6 report the path diagrams and test results of the relationship between the latent variables. Overall, the model specified in this paper explains 1. The scarcity of quality schooling resources makes the competition to be fierce. From Fig. The family background only explains the 1. It should be noted that this may be related to our use of household-based survey data and insufficient measurement of school quality.
Unlike the mechanism for obtaining quality school opportunities, the extracurricular remedial class is an education service provided by the market. Families are free to purchase.
The mechanisms affecting their acquisition are mainly the market accessibility and family purchase willingness and ability. From Table 6 , it can be seen that family socio-economic status explained Even though most parents recognize the importance of education, families with different socioeconomic status may create different learning environments Zhao and Hong ; Wang and Shi Thus, the hypothesis 2 of this study the higher the social economic status of the family, the higher the degree of parental participation in the education of the children is supported by the data.
High-quality schools not only have excellent teachers, but also have a good source of students. Research hypothesis 4c the more education services children receive in the market, the better their academic performance is supported.
The higher the degree of parent participation, the better the academic performance of children, and the hypothesis 4a is supported by data. Nowadays in China, regional factor urban or rural is an important variable affecting education.
Not only does the distribution of education resources across urban and rural areas differ tremendously, but urban and rural households also have quite different socioeconomic status, lifestyles, and education patterns. The analysis in Table 2 shows that urban children have significantly better academic performance than rural children. With the structural equation model, we further compare the paths of the effect of family background across urban and rural areas. Table 8 reports the path coefficients among the various latent variables and the explanatory power of the structural equation model.
In general, there are three differences in ways that family background influences the academic achievement of rural students and urban students. The socioeconomic status of the family explained Footnote 1 Second, the family background has significant urban-rural differences on the purchase of education services, and the family socio-economic status explains Most of the existing studies focus on the influence of family background on college education attainment.
Actually, the educational attainment of the higher education is affected by the education attainment during their childhood period. In the literature of the relationship between family background and academic performance in middle school Fang and Feng and high school Yang , the discussion is also limited in the correlation between family background and academic achievement. There is a lack of discussion on the mechanisms of childhood academic achievement, that is, the path through which the family background can affect education attainment during childhood, which needs further examination in the research of education.
It is in this sense that extensive public policy efforts in promoting education equity at the stage of compulsory education are needed. The existing studies separately demonstrate the impact of educational opportunities and parental involvement. However, these two forces act on the children simultaneously.
At the same time, parents with different socio-economic status are also heterogeneous to a great extent in their behavior support for children. Besides, compared with urban students, the academic achievement of rural students is more dependent on their own learning behavior. The empirical analysis of these two paths contributes to the existing literature on family background and education for educators. At the national level, relevant departments shall strive for the success of every school providing compulsory education, improve school facilities, upgrade the quality of teachers, and achieve a balanced allocation of educational resources, thereby reducing the impact of school factors on children's academic performance.
Given the applicability of the data, there are still issues that need attention by future research. This may be explained by the higher heterogeneity in family background and educational opportunities in urban areas compared to the rural counterpart.
But this argument needs further data analysis and tests to confirm. This can be learnt from the proportions of power explanation of each latent variable by the structural equation model and the simplified model in Table 6.
Becker, Gary S. Human capital: a theoretical and empirical analysis, with special reference to education. Chicago: University of Chicago Press.
Google Scholar. Bourdieu, Pierre, and Jean-Claude Passeron. In Reproduction in education, society and culture , ed. Richard Nice, 2nd ed. Calif: Sage Publications. Cheadle, Jacob E.
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