Predictors of mathematics achievement of migrant children in Chinese urban schools: A comparative study

International Journal of Educational Development 42 (2015) 35–42

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Predictors of mathematics achievement of migrant children in Chinese urban schools: A comparative study Ting Liu *, Kathryn Holmes, James Albright School of Education, The University of Newcastle, Newcastle, Australia

A R T I C L E I N F O

A B S T R A C T

Article history: Received 10 June 2014 Received in revised form 22 September 2014 Accepted 14 March 2015

In China, the education of increasing numbers of migrant children in urban settings is undertaken in integrated public or segregated migrant schools. This study compares the factors and predictors related to migrant children’s mathematics achievement in public schools and migrant schools. Regression analyses were conducted to determine which independent variables (school type, sibling numbers, length of residence in urban areas, gender, preschool attendance, and parental socioeconomic status) were the predictors of migrant students’ mathematics achievement. With respect to the total sample, the overall model of five factors pertinent to student achievement outcomes in mathematics was significant (sibling numbers, length of residence in urban areas, preschool attendance, parental socioeconomic status and school type). While these factors figure prominently as significant academic predictors for the migrant school sample, the only significant predictor of mathematics achievement for the public school sample is parental socioeconomic status. In the light of these findings the paper concludes with suggestions for the intervention that inform and characterize the education of migrant children in urban schools. ß 2015 Elsevier Ltd. All rights reserved.

Keywords: Mathematics achievement Academic outcome predictors Migrant children Primary school

1. Introduction In many developing countries such as China, India, Indonesia and Brazil, recent economic development has led to rapid urbanization. Referred to as ‘internal migration’ or ‘rural–urban migration’, millions of rural families have migrated to urban areas seeking education and employment opportunities (Deshingkar and Grimm, 2005). Most countries have recognized that providing access to high quality educational institutions for these migrants is of paramount importance in order to sustain economic development (Chiswick and DebBurman, 2004). However, an accumulating body of scholarly literature addresses migrant students’ disadvantaged learning environments in urban schools, due to traditional rural–urban disparities and institutional barriers (Feng et al., 2002). Nevertheless, most previous studies have investigated migrant students as if they were one homogeneous group. This perspective is myopic, and both simplifies and distorts the deeper issues. In recent years the demographic backgrounds of Chinese migrant children have diversified significantly, particularly with

* Corresponding author at: AOB 69, School of Education, The University of Newcastle, Callaghan, NSW 2308, Australia. Tel.: +61 02 49212065. E-mail address: [email protected] (T. Liu). http://dx.doi.org/10.1016/j.ijedudev.2015.03.001 0738-0593/ß 2015 Elsevier Ltd. All rights reserved.

regard to socioeconomic status. It has become clear that while some migrant students now study in urban public schools, many others remain segregated in migrant schools (Lu and Zhou, 2013). Given that a test-oriented education system is still prevalent in China, Mathematics education not only provides the opportunity for individuals to be successful in future employment, but also facilitates national economic growth (Baker et al., 2002). Therefore, the analysis of predictors in school test outcomes is critical if China is to reflect more accurately those factors hat prove to be essential in determining student mathematics success. Migrant children’s mathematics outcomes are sensitively influenced by various factors occurring within individual, parents, and social contexts (Wu et al., 2010). To provide policy makers a more balanced interpretation of the current empirical studies in order to enhance migrant students’ mathematics outcomes, this study will be concerned to identify the multiple predictors that are likely to influence their achievement, both in segregated migrant schools and in public schools. 1.1. Education for migrant children in Chinese urban schools The feature of internal migration distinguishes China from many other countries. In China, because of the deliberate structural orientation of government policy, migrant children are often segregated from urban mainstream culture and schools.

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Historically there exists a dualistic class system which has sociostructurally embedded a hiatus between rural and urban areas, thereby starkly dividing Chinese people into agricultural and nonagricultural groups. As the recent process of urbanization has continued to burgeon, however, it is clear that rural–urban migrant people have presently became the largest social class, both distinct and separated from ‘rural’ and ‘urban’ people (Wei and Hou, 2010). It is estimated that China’s domestic migrant population was approximately 221 million in the year 2010 (NBSC, 2011). The system of household registration inhibits migrant people from obtaining equal resources in terms of welfare, employment, and public goods attainment, in comparison to urban residents. Correspondingly, Chinese migrant children are classified as being ‘out-of-district’ children seeking education in urban public schools, making it virtually impossible for them to transfer into some urban schools. Specifically, urban public schools are only allocated resources for urban children within the school district, who hold the non-agricultural registration status. This being so, migrant children’s educational funding remains allocated to their family’s rural homes, even though their parents have migrated to an urban area. Consequently, the resulting shortage of educational funds in urban areas undermines the capacity of the local educational authorities to accommodate all students, migrant and urban. Some urban public schools occasionally recruit migrant children, on the condition that they can meet the requirements of extra high tuition fees (Goodburn, 2009). Nevertheless, for the majority of migrant children who hold the agricultural household registration, fewer opportunities are available for enrolment in urban public schools than for urban children generally (Li, 2012). To address these challenges, private migrant schools have been established to provide educational opportunities for migrant children, without the limitation of household registration and expensive school admission, but at the expense of being ‘quite frankly in miserable condition’ as well as having ‘poor equipment, and few qualified teachers’ (Xia, 2006, p. 39). Given efforts made during last decade, the Chinese government has gradually taken measures to ameliorate the problems which surround the integration of migrant children in urban areas. Several regulations and laws have been promulgated to guarantee access to urban public schools for migrant children. Furthermore, the latest State Council officially issued ‘Opinions on Further Promotion of the Reform of Household Registration System’ which states that there is no difference between rural and urban residence, and moreover, that rural–urban migrant people are encouraged to live in urban areas. This indicates that Chinese urban schools will be expected to serve greater numbers of rural– urban migrant children in the following decades. According to the statistics, the proportion of migrant children enrolling in public school grew to more than 50 percent in large cities such as Beijing and Shanghai (Wang, 2009). Meanwhile, the local Ministry of Education has strengthened the school quality of migrant schools by arranging a standard curriculum and pedagogical approach between migrant schools and public schools. For example, in Shanghai urban schools, the ‘Curriculum guides for primary and secondary schools in Shanghai’ has been published in each academic year since 2004 (MoE, 2014). This document requires that all of the schools within Shanghai districts are arranged with the same schedule of teaching subjects, textbooks, teaching time, and students’ out-of-class activity. Moreover, all of the students in Shanghai schools are required to speak official language (mandarin), either migrant or nonmigrant children (Qi and Tang, 2011); and complete nine-year of compulsory education. Despite these improvements, the general problem of educational inequality persists and scholars are now addressing the

evidence which contrasts migrant students’ disadvantaged learning environments in segregated migrant schools in comparison to public schools. Improved school outcomes in mathematics for migrant children are essential for their upward mobility and opportunity for future success (Bankston, 2004; Levels et al., 2008). Therefore, this paper will examine the mathematics achievement of migrant students between school types and the related predictors of school achievement, in order to provide policymakers, migrant families and schools suggestions to better support migrant students academically in migrant schools and in public schools respectively. In the following section, the review will focus on studies related to the prediction of mathematics achievement for migrant children. Attention is given to the few Chinese studies related to the education of migrant children in urban areas which have examined the public and private provision of primary education, particularly in relation to migrant children’s achievement in mathematics. 1.2. School type and mathematics achievement levels In this paper, two types of schools for migrant children in urban areas were included in this study: segregated migrant children’s schools (migrant schools) and public integrated schools (public schools). Much literature has examined how racial and socioeconomic segregation contributed to the achievement differences among students (Agirdag et al., 2012; Rumberger and Palardy, 2005). In particular, the impact of socioeconomic school segregation has been found to be greater than that of ethnic school segregation, in having a negative influence on the scholastic achievement of immigrant students (Dronkers and Levels, 2007). Previous studies on privately segregated migrant children’s psychological health and peer relationships have identified that migrant children sometimes suffer slight psychological health problems (Tao et al., 2004) and develop poor learning habits (Liu, 2007). In contrast, migrant children’s adaption in integrated public schools is better than that in migrant schools, regardless of the student’s grade level (Li et al., 2009; Shen, 2008). Migrant children in public schools also display more satisfaction with their schooling than migrant school students in private schools (Xie, 2007). In terms of academic achievement of migrant children, there has been no national study that comparing mathematics achievement of migrant students in China. In many public schools, mathematics achievement of migrant students was excluded from the total sample of evaluation of school teaching outcomes. Take the Program for International Student Assessment (PISA) for example, migrant students were not selected to represent the sample of public schools in Shanghai (Sellar and Lingard, 2013). The few studies that have focused on migrant children’s mathematics achievement within a limited region indicate that migrant children in public schools perform better than migrant students in migrant schools (Lai et al., 2014). In predicting mathematics achievement of migrant students, some studies have regarded access to public schools as a key factor in determining educational quality for migrant children in urban schools (Chen and Feng, 2013). Other comparative studies of school type have revealed that a higher migrant family income increases the likelihood of attending public schools (Lu, 2007), postulating that differences in family background may explain part of the achievement gap between school types. Nonetheless, there is no consensus on the predictors of whether individual, family, and school determining mathematics achievement of migrant students. Particularly the prediction of mathematics achievement of migrant students between migrant schools and public schools is required further investigation.

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1.3. Student’s characteristics and mathematics achievement levels For this study, student characteristics are elaborated in terms of gender, length of residence in urban areas, sibling numbers, and preschool attendance. These variables have been used in many studies to explain students’ difference in mathematics achievement (Else-Quest et al., 2010; Moon et al., 2009). Research on the gendered gap of migrant student school performance is not conclusive. However, an increasing number of studies have attributed the gender gap to changing socio-cultural factors such as family’s and schools’ influence rather than immutable biological differences (Hyde and Mertz, 2009). Educational beliefs in many eastern countries (e.g. China) place more emphasis on students’ efforts rather than their innate ability (Lu, 2012), and as a result, there may be less gender bias in these nations with respect to mathematics education. Some Chinese studies reveal that girls outperform boys throughout primary and middle school and have a more positive school experience and exert more effort in their mathematics education than boys (Lai, 2010). Among migrant children in particular, girls participate in school activities more actively and talk about school life to parents with a higher frequency (Xie, 2007). Female migrant children are better at establishing congenial relationships with teachers and classmates and display more positive academic behaviors than their male counterparts (Li et al., 2009). Other studies suggested that migrant girls perform less well in mathematics than migrant boys, but that this difference was not statistically significant (Chen and Feng, 2013). Migrant children’s length of residence in urban areas has been found to be a significant predictor for migrant students’ achievement (Moon et al., 2009). The duration of residence in a new environment can contribute to a greater assimilation of the local norms and values (Keene et al., 2013). As a result, the more protracted a student’s stay in urban public schools, the more beneficial the performance outcomes in mathematics, while the specific grade level in which a student is situated during his/her time of residency can also impact positively on mathematics performance. Migrant children from educationally disadvantaged places of origin benefit significantly from exposure to ‘richer’ schooling environments and the longer their length of stay in such environments, the greater the level of improvement in their academic performance. Conversely, migrant children in poorly resourced private migrant schools may experience a widening achievement gap in relation to their public school counterparts, as their length of residence in urban areas increases (Lai et al., 2012, 2014). Other variables such as preschool education have been found to benefit educational attainment of students in later elementary school (Templea and Reynolds, 2007). Lack of preparation for elementary education in pre-school in urban India was found to be the main obstacle for migrated children (Tsujita, 2013). In terms of sibling numbers, it has been found to have a negative association with the educational attainment of students in European countries (Van Eijck and de Graaf, 1995), indicating that households with fewer siblings appear to be advantaged in many aspects of school performance (Lu, 2007; Siegler et al., 2012). However, these variables have not been examined together in relation to the mathematics achievement of migrant children in the Chinese context that required further investigation. 1.4. Parent’s background and mathematics achievement levels Other known influencers of students’ mathematics achievement such as parents’ demographic variables (parents’ education and income) have been found to be significant demographic variables in relation to students’ mathematics achievement (Davis-Kean, 2005; Dincer and Uysal, 2010; Engin-Demir, 2009; Guo, 2011). Parental education, especially mother’s education has been found to be an

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important factor in predicting students’ academic achievement (Altschul, 2012; Moon et al., 2009; Wu et al., 2010). However, other evidence suggests that these factors do not appear to matter for migrant children’s school performance (Lu, 2007). It has been suggested that the lower socioeconomic status of students’ parents is related to an undervaluing of their child’s education (Liu et al., [14_TD$IF]2015; Vellymalay, 2012). Chinese research positions migrant students as ‘‘an at-risk, disadvantaged group for potential school failure given their lack of residence status and lower socioeconomic status’’ (Guo, 2011, p. 123). Some studies have compared the employment attainment and wage differentials between rural migrants and urban locals. Compared with urban parents, rural migrant parents suffer occupational level discrimination resulting in a wage gap in urban areas (Cheng et al., 2013). This may disadvantage students’ school performance because migrant children from families with the lower household incomes have been found to have a lower mathematics achievement level than those from families with higher incomes (Guo, 2011). Others have identified that parents’ background significantly determines school selection for migrant children, among which mother’s education level has been found to be positively related to the likelihood of public school enrollment (Chen and Feng, 2013). In summary, the literature showed that school type, migrant students’ characteristics and their parents’ background, which have been used in many studies to explain student differences in academic achievement are possible predictors for migrant students’ mathematics achievement. This study therefore, extends the study of predictors of mathematics achievement of migrant children to include an investigation of school type, gender, and length of residence in urban areas in a comparative way. In response to this aim, two research questions are framed to guide the study. 2. Research questions 2.1. Which variables related to students’ and parents’ demographic background (sibling numbers, preschool attendance, gender, and length of residence in urban areas and parental SES) predict mathematics achievement of migrant children? 2.2. Are there different predictors between public school sample and migrant school sample?

3. Research methodology 3.1. Data The data of migrant children’s mathematics test scores were collected from four primary schools in Shanghai from January to March 2013. All participant students are children enrolled in years 2–5. Mathematics tests were conducted within all primary schools as the final examination of the semester. Each grade level uses a different mathematics test suitable for the age level of the students. Students’ scores were collected and evaluated by the local Ministry of Education. In years 2–5, the mathematics test aligns with the regional curriculum standards in Shanghai, consisting of three main sections: number and computing (20%), concepts comprehension (40%) and problem solving (40%). The math test score scale is 0–100 points. To assess students’ mastery of mathematics, scores of 60 points or above is the level required to pass the exam, below 60 points means failing in the exam, whereas 80 points or above is the excellent level in each grade. Furthermore, demographic information of participant students was also collected from the students’ school enrolment databases provided by the participating schools, including parent’s occupation, parent’s education, length of residence in the urban schools, preschool attendance, and sibling numbers. In order to control for

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differences between central and peripheral districts in Shanghai, all of the participant schools were selected within one of the suburban districts. The participant schools and students were assured of anonymity and confidentiality. In the total sample of 839 migrant children, 481 boys (57.3%) and 358 girls (42.7%) participated in the study. Of the participant migrant students, 478 students attended migrant schools (57%), and 361 students attended public schools (43%). The participant students in each of the years 2–5 are similar in number (year 2, 31.6%; year 3, 25.1%; year 4, 22.6%; and year 5, 20.7%). The age of participant students varied from 8 (year 2) to 11 years old (year 5) on average. 3.2. Empirical strategy This paper examines the mathematics test score between public schools and migrant schools, using multivariate regression to examine the factors related to migrant student’s mathematics achievement levels. The analysis seeks to explain the mathematics achievement in relation to student’s characteristics, parents’ background and school type, based on Lai’s (2012, 2014) model of predicting migrant children’s achievement. Lai’s (2012, 2014) study employs multivariate regression to examine how individual characteristics and school quality affect China’s migrant student performance and seeks to identify the determinants/correlates of the gap between migrant students and those in rural public schools. Estimating the size of the achievement gap, two possible reasons for the gap of ‘selection effect’ that parents who are better able to provide a favorable study environment in city are more likely to bring their children along with them; and ‘school effect’ that migrant schools and rural/urban schools might differ in terms of school resources and facilities and teacher quality. This paper extends the prediction model of mathematics achievement in a comparative way between migrant students in migrant schools and public schools. Lai’s (2012, 2014) regression model is adopted as follows: Y ix ¼ b0 þ b1 X i þ b2 Si þ b3 M i þ Eix where Yi is the mathematics achievement of migrant students i in school x. Xi is a dummy variable for the type of school equal to 0 if migrant student i is enrolled in a migrant school and 1 if in a public school. Si is a vector of migrant students’ variables (including sibling numbers, length of residence in urban areas, gender, preschool attendance) and Mi is the vector of their parent’s variables (parental socioeconomic status in terms of educational degree and Occupation) as in the model 1. Other factors that may also affect the mathematics outcome are included in Eix. The significance of parameter vectors b1, b2, and b3 respectively gauge the statistical significance of factor Xi (school), Si (student), and Mi (parent) in predicting student’s achievement. Therefore, we employed a model in which student, family characteristics and school type predict the migrant students’ mathematics achievement. It is assumed that variables of observable factors in this paper will be able to capture at least a part of the selection effect and school effect explaining the academic achievement of migrant children. 4. Results 4.1. Summaries of demographics Tables 1 and 2 summarize the demographic statistics allowing for a comparison between the background variables for migrant children attending urban and migrant schools. A statistically significant difference was found between the occupational distribution of parents and school type (x2 (4, N = 839) = 302.66,

Table 1 Parent’s background of migrant children: summary statistics. Variables Occupation No job Factory workers Small service business State-managed company Government officials Education background Primary school education Junior school education Senior school education Three-year diploma Bachelor Degree ***

Migrant schools

Public schools

3.1% 50.2% 36.2% 9.6% .8%

1.1% 19.1% 11.4% 63.2% 5.3%

11.3% 55.6% 32.6% .4% .0%

7.8% 30.2% 38.8% 20.5% 2.8%

x2 302.66***

139.45***

p < .001.

Table 2 Background variables of migrant children: summary statistics. Variables Length of residence in urban area Less than 2 years 2–5 years 5–8 years 8 years above Preschool education Attended kindergarten Gender Female Sibling number 0 sibling 1 sibling 2–3 siblings 3 siblings above ***

Migrant school

Public school

x2 264.27***

19.0% 44.8% 15.5% 20.7%

5.0% 6.9% 15.2% 72.9%

95.0%

100.0%

41.8%

44.1%

49.4% 28.7% 18.4% 3.6%

85.9% 11.4% 2.8% .0%

18.66*** 3.87 127.04***

p < .001.

p < .001), and between parents’ educational qualifications and school type (x2 (4, N = 839) = 139.45, p < .001). The majority of migrant children’s parents in migrant schools were only junior school graduates (55.6%). In contrast, public school parents had a higher percentage (23.3%) with a college degree or three-year diploma. Table 2 provides the background variables of the migrant children in the study. The majority of migrant students in public schools (72.9%) have migrated for more than 8 years which was a higher percentage than their counterparts in migrant schools (20.7%). No significant gender difference (p > .05) was found between school types as 41.8% of female migrant students were in migrant schools and 44.1% studied in public schools. All of the migrant children in public schools have attended kindergarten, while a small percentage of migrant children in migrant schools had no preschool education (5%). The Chinese government has implemented a strict ‘one-child policy’ to control population growth over 30 years, although many rural and remote areas have relaxed the restrictions. Most migrant children in public schools had no siblings (85.9%), whereas migrant children in migrant schools generally had a higher number of siblings. A statistically significant difference was also found between categories of students’ sibling numbers and school type: (x2 (3, N = 839) = 127.04, p < .001), as well as between the preschool attendance and school type (x2 (1, N = 839) = 18.66, p < .001). 4.2. Mathematics achievement levels Table 3 presents the findings on the mathematics achievement levels of migrant children. Migrant children in public schools

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Table 3 Mathematics achievement levels of migrant children: Summary statistics. Achievement level

Failed rate (<60 points)

Grade level

Migrant schools

Migrant schools

Public schools

80.50% 140

95.90% 117

32.70% 34

2.20% 2

55.80% 58

97.80% 87

1.40% 1

40.70% 44

7.10% 5

45.40% 49

91.40% 64

30.40% 28

7.50% 6

19.60% 18

15.00% 12

50.00% 46

77.50% 62

14.95% 62

2.43% 8

27.13% 123

6.90% 23

57.93% 293

90.65% 330

4.00% 7

Year 3

% n

11.50% 12

Year 4

% n

13.90% 15

Year 5

% n

Mean

% n

0.80% 1 0% 0

scored higher in mathematics across years 2–5 than migrant children in migrant schools. In public schools, the failure rate for migrant children was much lower than in migrant schools. Moreover, migrant students in public schools had a steady increase in the fail rate from years 3 to 5, whereas there was a sharp increase of the fail rate from years 2 to 5 in migrant schools. A statistically significant difference was found between the categories of mathematics achievement levels and school type: x2 (4, N = 839) = 307.9, p < .001. 4.3. Analysis of factors related to mathematics achievement Results of bivariate associations among variables are reported in Table 4. For both migrant school and public school samples, parental SES was significantly positively correlated with migrant student achievement (p < .01), and student sibling numbers were negatively associated with migrant student achievement (p < .01). Unlike the public school sample, for migrant children in migrant schools, the length of residence in urban areas (.556**) was significantly negatively related to student mathematics achievement. Further, stepwise regression analyses were conducted to determine which independent variables (school type, sibling numbers, length of residence in urban areas, gender, preschool attendance, and parental socioeconomic status) were the predictors of migrant students’ mathematics achievement (Table 5). For the total sample, the overall model of student characteristics (sibling numbers, preschool attendance, and length of residence in urban areas), parental SES and school type were found to be significant contributors to student mathematics achievement

Table 4 Bivariate correlations among variables. 1 Math scores Parental SES Sibling numbers Length of residence in urban areas

Migrant schools

3.30% 4

% n

1. 2. 3. 4.

Excellent rate (>80 points)

15.50% 27

Year 2

Public schools

Passing rate (60–80 points)

– .295** .172** .085

2

3 **

.200 – .333** .207**

4 **

.720 .243** – .331**

.556** .101* .510** –

Note: Correlations of migrant students (n = 478) in migrant schools are above the diagonal; the correlations of public school students (n = 361) are below the diagonal. * p < .05. ** p < .01. Parental SES is calculated as a combined measure of parental education (coded by 0–5) and parental occupation (coded by 0–4), by multiplying the highest education completed by both parents with the highest occupation held by both parents (Lim and Gemici, 2011). This step results in the range of 0 to 20, where 0 is the lowest SES and 20 is the highest SES category. The SES scores were then divided into quartiles (1 is the bottom quartile, and 4 is the top quartile).

Public schools

(R2 = .51, F (5, 833) = 176.48, p < .01). Gender was not found to be a significant predictor of mathematics achievement for the whole sample. This model accounted for 51% of variance in migrant children’s mathematics achievement. Subsequently, a regression analysis was conducted between the samples of students in migrant schools and in public schools separately. For migrant students in migrant schools, sibling numbers and length of residence in urban areas were significant negative determinants of mathematics achievement, indicating that after controlling for other variables in the model, migrant children in migrant schools with more siblings and who had migrated to the urban area for a longer length of time were more likely to have lower mathematics achievement. Preschool attendance and parental SES had significant positive weights predicting mathematics achievement (R2 = .59, F (4, 473) = 167.97, p < .01). For public school samples, parental SES was the only significant positive indicator predicting mathematics achievement (R2 = .07, F (3, 357) = 9.51, p < .01). 5. Discussion In an effort to identity factors related to mathematics achievement for migrant children, this paper reports on a comparative study in Chinese urban public schools and migrant schools. It is notable that, consistent with previous findings on migrant children’s school achievement, migrant children in migrant schools are far behind in mathematics test scores in comparison to migrant students in public schools (Chen and Feng, 2013; Guo, 2011; Lai et al., 2009; Xiong, 2010; Yuan and Hou, 2012). Moreover, in consistency with findings from Lai’s (2012, 2014) model, this paper found that students’ individual characteristics, parents’ background and school type were significant predictors of mathematics achievement of migrant children. Differences are found, however, in the relationship among selected predictors and mathematics achievement between samples of migrant children. With respect to the migrant school sample, four factors pertinent to student achievement outcomes in mathematics was significant (sibling numbers, length of residence in urban areas, preschool attendance, parental socioeconomic status), while the only significant predictor of mathematics achievement for the public school sample is parental socioeconomic status. For both migrant school sample and public school sample, parents’ background in terms of education and occupation level, is a significant determinant of migrant children’s mathematics achievement. Migrant children who have parents with higher education and a higher occupation level show higher mathematics achievement than children whose parents with lower education and a lower occupation level. In agreement with previous study which shows that the higher educational background of migrant parents indicates a higher likelihood of parental involvement in

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Table 5 Multiple regression of mathematics achievement on predictors. Total sample (N = 839)

SES SN LR PA ST G

Migrant students in migrant schools (N = 478)

Migrant students in public schools (N = 361)

B

SE

Beta

B

SE

Beta

B

SE

Beta

.068 .358 .164 .759 .265 –

.195 .018 .023 .017 .094 –

.116** .445** .289** .202** .209** –

.071 .396 .200 .686 – –

.043 .029 .024 .100 – –

.050* .484** .284** .210** – –

.088 – – – – –

.017 – – – – –

.275** – – – – –

Note. For total, R2 = .514; for migrant students in migrant schools, R2 = .587; for migrant students in public schools, R2 = .074. C: constant; SES: socioeconomic status; LR: length of residence in urban; SN: sibling numbers; PA: preschool attendance; ST: school type; G: gender. * p < .05. ** p < .01.

educating migrant children and thus contributing to students’ academic success (Altschul, 2012; Davis-Kean, 2005). The finding also confirms the strong relationship found between segregation by socioeconomic status and test scores in other countries, in that lower SES children in segregated minority schools are normally labeled as failures in academic achievement (Dronkers and Levels, 2007; Orfield and Lee, 2005). One more possible reason for this consistent observation is that Chinese rural–urban migrant groups have been diversified into different socioeconomic groups, in that low SES migrant children are segregated from urban public schools (Lu and Zhou, 2013). As Chinese urban public schools recruit migrant children on the premise of extra tuition fees (Goodburn, 2009), migrant families with a higher SES are more likely to be enrolled in urban public schools. As a result, for migrant children who with low SES status, fewer opportunities are available for them to be admitted to urban public schools (Li, 2012). Therefore, it is important to acknowledge, as we concluded that the real problem of educational inequity is not segregated or desegregated schools in themselves, but rather instructional segregation which is strongly associated with socioeconomic segregation. Moreover, for both groups of migrant children, parental SES is found to be negatively associated with children’s sibling numbers explaining that families with more economic resources and where parents have higher education levels, they are more likely to have fewer children. For migrant school sample, migrant student’s sibling numbers is a powerful indicator of mathematics achievement, which aligns with other studies, that children from households with fewer siblings achieve a higher academic achievement (Lu, 2007; Van Eijck and de Graaf, 1995). The effect of the length of a migrant child’s residence in an urban area on achievement is significant for migrant school sample. Challenging previous studies that a longer residence in urban areas helps benefits children to better mathematics achievement (Nielsen et al., 2006), it does not appear to improve the mathematics achievement levels of migrant school sample. Migrant school children who have migrated to the urban area for a longer length of time are more likely to have lower mathematics achievement levels than those who have migrated for a shorter length of time. A possible explanation might be that controlling for parents’ background variables, the lower educational resources in migrant schools have a cumulative negative effect on student’s mathematics performance as their length of urban residence increases (Lai et al., 2012, 2014). As a result, it may explain that migrant children in poorly resourced private migrant schools may experience a widening achievement gap in relation to their public school counterparts, as their length of residence in urban areas increases. Gender was not found to be a significant predictor of mathematics achievement for Chinese migrant children. This may be attributed to the educational beliefs in China that place more emphasis on students’ efforts rather than their innate ability

(Leung, 2006). In addition, the result with migrant school sample indicates that preschool attendance of migrant children predicts mathematics achievement, confirming that experience in kindergarten contributes positively to later mathematics achievement in comparison to those who have not had this experience (Hemmings et al., 2011; Templea and Reynolds, 2007). Contrary to our hypothesis, the length of urban residence, preschool education and sibling numbers are not significant predictors of mathematics achievement among public school sample. It may well be explained that most migrant students in public schools have similar background in terms of related variables such as having preschool education; being the only child in the family, and have migrated to urban areas before preschool education. As a result, in comparison to the diverse backgrounds of migrant students in migrant schools, the relatively homogenous backgrounds of migrant children in public schools may statistically masks the contribution of the related variables (sibling numbers, preschool attendance, and length of residence in urban areas) in predicting mathematics achievement. When comparing the amount of variance explained by the predictors in model between samples, the amount of variance explained is very high for the sample of migrant schools (59%) in comparison to the public schools (7%). That means that the variables of observable factors in this paper are able to capture a large part of the selection school effects which explain the academic achievement of migrant children in migrant schools. However, the observable factors only explain a small part of the variation in the school achievement levels for migrant children in public schools, indicating that further investigation is required to understand the factors related to achievement for these children. Finally, we reflect on the education of migrant children in Chinese urban schools and implications for Chinese education. Shanghai, as one of China’s largest metropolitan cities, is portrayed as a ‘high quality’ school system, because its participation in the Program for International Student Assessment (PISA) achieved top world-ranking results (Sellar and Lingard, 2013). However, the large population of migrant students which exist in Shanghai were not included in the sampling for the PISA program (Loveless, 2014). Assessment of the program’s success which do not include the test results of migrant students are intrinsically misleading, and thus exaggerate the reality of China’s educational success. The inaccurate reflection which persists clearly betrays the deeper truth that given its huge numbers of migrant children, the local government of Shanghai has not yet shown itself to be capable of accommodating migrant children within the arena of public education, which would otherwise afford them the opportunity to academically succeed as they equitably deserve. In order to better support social integration for migrant children through education, service providers should be aware of the predictors such as parental SES, sibling numbers, preschool

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education, length of residence and school factor. This paper suggests that it is possible, to improve the background profile for migrant students by facilitating the provision of preschool attendance for migrant school students. Parental SES in terms of educational levels and occupation is a significant factor explaining the mathematics achievement of all migrant students. Intervention students Intervention programs specifically targeting migrant parents, could provide support to assist them in advocating for their children leading to more successful school outcomes among migrant children, especially those attending migrant schools. Furthermore, migrant children in public schools outperformed children in migrant schools, not only because they have stronger individual and family backgrounds, but also because school factors contribute to the widening gap in academic achievement as their length of residence in urban areas increased. Even though the household registration system between rural and urban people has been abolished recently, significant change in migrant children’s education in Shanghai urban schools will not occur in a short period of time. Therefore, another suggestion is for policy-makers is to improve facilities and teaching quality in all migrant schools, in order to better improve the education of migrant children. 6. Conclusions This paper establishes that the predictors of migrant children’s mathematics achievement exhibit notable differences for migrant children in segregated migrant schools and integrated public schools. Extended Lai’s (2014) study in Beijing public schools, this paper confirmed the predictors that migrant student’s individual, family background and school type determining mathematics achievement of migrant children among Shanghai migrant samples. The data presented in this paper only represents one snap-shot in a multi-phase project that, as a component of the research, serves to inform and support subsequent data collection and analysis. The findings relating to school quality and everyday classroom practice in these two types of schools will be reported in a follow-up paper. Meanwhile, it is important to recognize several limitations of this study. It is to be admitted that one limitation of this study is that there is presently no national standardized test across grade levels in primary schools, as the mathematics test scores are currently provided by participant schools in one district only. Therefore, the generalizability of certain aspects of the conclusion could arguably be regarded as problematic. Secondly, while school type has in this paper been found to be an important factor in determining mathematics achievement, it is clear that additional future studies will be required to more fully predict the influence on migrant children’s academic achievement, based on factor variables such as teaching quality and school resources, thereby helping to understand more deeply the potential contribution to academic excellence in mathematics made by different school types in Chinese urban settings. Last but not least, this study is only a snapshot of migrant children’s school achievement in primary schools. Researchers should be careful in generalizing the results to other age groups of migrant children. For example, as they get older, children may increasingly develop social support networks outside of the family and school context. At such, demographical influences on children’s school achievement may decline in adolescence and youth. Acknowledgements The authors thank Professor Peter Sullivan at Monash University and Professor Ronald Laura in University of Newcastle for comments and assistance for this paper.

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