Skip to main content

ORIGINAL RESEARCH article

Front. Psychol., 22 July 2022
Sec. Cognition

Language Ability Accounts for Ethnic Difference in Mathematics Achievement

  • 1College of Education, Hebei Normal University, Shijiazhuang, China
  • 2State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills.

Introduction

Although numerous cross-cultural studies have been conducted in juveniles, including those on delinquency (e.g., Link, 2008; Kemme, 2010; Schlesinger, 2010; Ungvary et al., 2017; Kobayashi and Farrington, 2019; Yun and Cui, 2019; Mancinelli et al., 2021), cognitive emotions (e.g., Pethtel and Chen, 2010; Alonso-Arbiol et al., 2011; Benítez et al., 2016; Potthoff et al., 2016; Susino and Schubert, 2016; Han et al., 2021; Zhou et al., 2021), traits (e.g., Adams and Hanna, 2012; Feilhauer et al., 2012; Wu and Bodigerel-Koehler, 2013; Bartel-Radic and Giannelloni, 2017; Wilson et al., 2017; Kuśnierz et al., 2020; Srirangarajan et al., 2020), and mental diseases (e.g., Lus and Mukaddes, 2009; Mpango et al., 2017; Wong et al., 2017; Nielsen et al., 2018; Sørlie et al., 2018; Ghafoor et al., 2019), there are relatively few cross-ethnic studies. China is a multi-ethnic country composed of 56 ethnic groups within which about 120 million are ethnic minorities. Among them, 18 ethnic minorities have their own written languages. Therefore, with such a large family of nationalities, cross-ethnic research is very necessary and important in China. Effective cross-ethnic research will help deepen the understanding of the status quo for ethnic differences and allow us to see the challenges that currently exist. These kinds of studies will help narrow the developing gap in education between ethnic minority regions of China and others. Thus, it will help all ethnic groups make common progress (Chen and Xu, 2018, in Chinese version; Hu and Zhou, 2018, in Chinese version).

Previous cross-ethnic studies have focused on social issues, such as social relationships (including neighborhood, e.g., Matlasz et al., 2020), school characteristics (Shin, 2012; Raccanello et al., 2020), classmate relationships (Stark et al., 2015), mental health (Richter et al., 2011; Bhui et al., 2012; Priest et al., 2014), friendship (Munniksma and Juvonen, 2012; Chen and Graham, 2015), medical care and nutrition (including dental care, Vitamins, obesity, and inflammation, Gorelick et al., 2011; Kant and Graubard, 2011; Özel et al., 2020; Scheidell et al., 2020), parental activity (Ali and Frederickson, 2010; Wilson et al., 2011; Cardel et al., 2012), and addiction (including drug, alcohol, and tobacco, e.g., Cummings et al., 2011; Bares and Andrade, 2012; Cook and Caetano, 2014; Greene et al., 2014; Greene and Maggs, 2020). However, past cross-ethnic studies have paid little attention to problems related to academic achievement. In fact, there are often obvious ethnic differences in academic performance. Most previous studies have found that academic performance of ethnic minorities is poorer than that of the majority nationality. This has been shown for African Americans in the United States (Gonzales et al., 2004; Lemke et al., 2004; Perie et al., 2005; Reyna and Brainerd, 2007; Powell et al., 2012; Baxter et al., 2013), and several Indian minorities in India (Jia, 2007, in Chinese version; Yang, 2009, in Chinese version; An and Cheng, 2012, in Chinese version; Wang and Yang, 2019, in Chinese version). As a multi-ethnic country, China also struggles in achieving equal academic performance for its minority populations. Minority students typically lag behind Han Chinese in mathematics achievement during the compulsory education period (Zeng et al., 2015, in Chinese version; He and Sun, 2019, in Chinese version; Liu et al., 2019, in Chinese version).

Ethnic Differences in Mathematical Achievement

The underachievement of minority students is a worldwide problem. Among them, the poor performance in mathematics has become a prominent problem.

In Europe and America, math scores for minority students differ from those for majority students (Baxter et al., 2013; Sonnenschein and Sun, 2016; Spörlein and Schlueter, 2018; Saw et al., 2018). For example, in a study combining data from 61 countries, the school math test scores of minority students were lower than those of majority students, and minority students tended to score at the lower end of the math spectrum (Spörlein and Schlueter, 2018). Specifically, studies showed that Hispanic children from three urban middle schools scored lower in math than their non-Hispanic peers (Safavian and Conley, 2016), and that ethnic differences begin as early as kindergarten and continue through high school (e.g., Muller et al., 2001; Schneider and Saw, 2016). A study of grades 9–11, for example, also found that Hispanic students scored lower on mathematical assessment tests than White students (about 0.4 standard deviation, Saw et al., 2018). Ethnic differences also exist in some specific mathematical abilities. White children have significantly higher scores in mathematics than Black or Latino children in America, which covers number sense, spatial sense, algebra, and functions (Sonnenschein and Sun, 2016). Similarly, the Program for International Student Assessment found that in many Organization for Economic Co-operation and Development (OECD) countries (e.g., France, Switzerland, and the United Kingdom), immigrant student performance was not as good as that of native students in both reading and mathematics (OECD, 2010). Another study compared the math achievement gap between 4th grade mainstream students and immigrant students in TIMSS 2015, and showed that for all countries, 4th grade immigrant students performed significantly lower than mainstreamers (p < 0.001), except in England and Ireland (Arikan et al., 2020).

As a developing country with a large population, India’s minority education issues are very prominent (Karuna, 1993; Arouri et al., 2019). The Scheduled Castes and Tribes account for about 15% of the Indian population (Das, 1982; Gallanter, 1984), and are usually regarded as minorities in the sociological literature (Vander Zanden, 1983). In India, the low social status and remote living areas of the Scheduled Castes and Tribes have increased the difficulty of providing adequate literacy education, resulting in literacy rates that are far lower than the national average (Huang, 1998, in Chinese version). The gap in absolute illiteracy between Scheduled Tribes and non-Scheduled Tribes rose from 22% in 1971 to 33% in 1991, before falling to 18% in 2001 (Ministry of Tribal Affairs, 2008). In 1999, the Indian Scheduled Tribes and non-Scheduled Tribes had a secondary educational completion rate of 4.35 and 21.99%, respectively, and by 2009, Scheduled Tribe’s data had risen to 7.98%, But far less than the 32.8% of Non-Scheduled Tribe. After more than 10 years of development, the difference between Scheduled Tribes and non-scheduled Tribes is not narrowing, but widening (Basant and Sen, 2014). Arouri et al. (2019) studied the disparities in ethnic and racial education in India and found that academic performance of ethnic minorities is lower than that of the main ethnic groups in all respects, including percentage of being enrolled in school, grade of education completed, and math test scores, to name a few.

Ethnic differences in mathematical achievement are also prominent in China (Zeng et al., 2015, in Chinese version; Liu et al., 2019, in Chinese version). Studies have found that the standard math scores of minority students are significantly lower than those of Han students (e.g., Zeng et al., 2015, in Chinese version). There is also a significant difference in passing rate for mathematics between minority students and Han students; the passing rate on the final mathematics examination has been lower for ethnic minority students (79.5%) than for Han students (87.5%) in 2019 (Liu et al., 2019, in Chinese version). In order to alleviate this problem, the Chinese government has carried out a series ethnic education initiatives in terms of legislation, policy formulation, and training plans (Chen and Zhong, 2018, in Chinese version; Ren and Wei, 2018, in Chinese version). The Fourteenth Five-Year Plan for the National Economic and Social Development and the Outline of the Vision to 2035 adopted at the Fourth Session of the Thirteenth National People’s Congress of China both put forward the requirements of “improving the quality and level of education in ethnic minority areas and strengthening the promotion of the national common language.”

Reasons for Ethnic Differences in Math Achievement

Previous studies have primarily investigated ethnic differences in mathematical ability from the perspective of social factors, such as socioeconomic status, educational resources, parental involvement, and emotional factors (Azmitia et al., 2008; Halpern-Manners et al., 2009; Ceballo et al., 2014; Sonnenschein and Sun, 2016; Assari et al., 2020; Brandmiller et al., 2020).

Ethnic differences in academic achievement often result from differences in socioeconomic status (Sonnenschein and Sun, 2016; Assari et al., 2020). Family poverty is one of the strongest barriers to educational success for an ethnic minority student (Dahl and Lochner, 2012; Trieu and Jayakody, 2018; Dolean and Cãlugãr, 2020). For example, ethnic minority groups perform worse than White Americans on math scores in American (Potter and Morris, 2016) and tend to be poorer than White Americans in terms of economic status (Dahl and Lochner, 2012; Trieu and Jayakody, 2018). A study of Romania children found that living conditions could partially mediate the relationship between ethnicity and IQ (Dolean and Cãlugãr, 2020). The educational level of parents is another effective indicator of socio-economic status. Lower academic achievement levels of minority students are usually closely related to the lower educational level of their parents (Dolean and Cãlugãr, 2020). Similarly, a study of African Americans and White Americans in the United States has reported that higher parental education is associated with higher math and reading scores (Assari et al., 2020).

Inequality between minorities and the majority in educational resources, such as school quality and learning environment also contributes to ethnic differences in mathematics (e.g., Halpern-Manners et al., 2009). Differences in the quality of schools attended lead to differences in academic achievement between White Americans and African Americans or Hispanic Americans (Halpern-Manners et al., 2009). In Vietnam, being closer to school as well as effective support from parents, relatives, schools, and school peers was reported to effectively prevent minority students from dropping out of school and to promote academic success (Trieu and Jayakody, 2018).

The lack of parental involvement in education is also a contributing factor to poor academic ability in ethnic minority students. Some longitudinal studies have found that Asian and White children perform better than Black and Hispanic children in math and reading in United States, which was mediated by parental awareness and involvement in the their children’s educational development (Huang et al., 2005; Azmitia et al., 2008; Ceballo et al., 2014; Sonnenschein and Sun, 2016). Another study in the southwestern United States showed that sensitive support from African American and Hispanic American fathers was associated with their children’s mathematics achievement, even after controlling for sensitive support from the mothers (Caughy et al., 2020).

Social emotional factors, such as motivation and attitudes, also contribute to ethnic differences in math achievement. The motivation to learn mathematics for minority students is not clear. Brandmiller et al. (2020) showed that learning motivation was significantly correlated with immigrant backgrounds (r = 0.09, p < 0.001) and that learning motivation was less in students with immigrant backgrounds than in students without immigrant backgrounds. There were also significant differences between Han and Tibetan students in math knowledge memory, math logical thinking, the will to learn math, and math learning motivation (p < 0.005), among which, the factor with greatest difference was the motivation to study math (Xi and Zhang, 2008, in Chinese version). Learning attitudes for minorities are relatively weak. Trieu and Jayakody (2018) suggested that rejection of the official language by minority Vietnamese students is a major obstacle to their learning, and the social distance between majority teachers and minority students may weaken their bond (Giacchino-Baker, 2007; Baulch et al., 2010). Many researchers have found that minority students in Vietnam rarely interact with their teachers, they usually sit quietly in class and do not participate in class discussions (Tran, 2013), which are all manifestations of the negative learning attitude of minority students.

To sum up, minority students in many countries generally face the problem of poor performance in mathematics, and most existing studies have tried to explain such ethnic differences by social factors, such as SES, education resources, parental involvement in education, or social emotion (Klebanov et al., 1998; Dahl and Lochner, 2012; Sonnenschein and Sun, 2016; Assari et al., 2020).

We think there is another possible explanation for the difference in mathematical ability between the majority and ethnic minorities. Ethnic minorities often use their own national language as their mother tongue, which might prevent adequate fluency in a country’s official language. However, there no study has examined whether differences in language processing ability alone can account for ethnic differences in mathematical achievement.

Current Investigation

Different from previous studies that focused on socioeconomic status, educational resources, parental involvement, or social emotions, the current study explored the cognitive mechanisms for the ethnic differences in mathematical ability. As far as we know, this is the first study to investigate this question. We hypothesized that processing abilities for an official language (in this case Chinese) could fully account for the ethnic differences in mathematics achievements. Language competence in this study refers to the ability to use the official written language of one’s own country.

The reason for this hypothesis is that language is an important resource in mathematics education (e.g., Barwell, 2018; Planas, 2018), and mathematics knowledge is transmitted to students through language expression. Therefore, teaching in different languages may bring different effects, and different language environments may bring students different priori knowledge and cognitive reserves. Two aspects of empirical evidences can indirectly support this view.

First, crucial language elements including phonetics and semantics are involved in math processing. For example, representation of mathematical terms and operational symbols requires coding of phonetic information (Hecht et al., 2001; Simmons and Singleton, 2008). Reading comprehension and mathematics is highly correlated in students [r = 0.55, see a review by Singer and Strasser (2017)]. Evidence from brain lesion studies indicate that serious semantic deficits due to atrophy in the left temporal cortex are accompanied by deficits in arithmetical computation and principle understanding (Julien et al., 2008; Butterworth et al., 2011).

Second, studies have found that students whose mother tongue is not the national language of a country, or the language used in broader educational communication, usually exhibit poorer academic performance. For example, language barriers were among the major factors for the relative low math performance of non-English-speaking minority groups in the United States (Serratrice et al., 2006; August et al., 2009). The differences in math achievement between monolingual German students and German students who spoke a linguistic minority language were related to differences in their language levels (Haag et al., 2014). In Vietnam schools, linguistic barriers can also decrease educational achievement and attainment for ethnic minorities (Taylor, 2007). Similar phenomena also appear in China. Ethnic minority students in western China, especially those whose mother tongue is not Mandarin, scored significantly lower on standardized math tests than did Han students (0.62 standard deviations lower, see Yang et al., 2015). In math class, students taught in Chinese language performed better than those taught in Mongolian (p < 0.01, see Zhong, 2013, in Chinese Version). Moreover, when both had Chinese as their classroom language, students whose at-home language was Chinese performed better than those whose at-home language was Mongolian (p = 0.026; Zhong, 2013, in Chinese Version).

In order to study the influence of language ability on ethnic differences in mathematics, we controlled for the influence of socioeconomic status factors and educational factors on mathematical achievement. We selected Han and ethnic minority students as participants from several schools, with the same number of Han and ethnic minority students being selected from each school. Because of the proximity policy, these students live in the same or adjacent blocks and thus have similar socioeconomic status. At the same time, they study in the same schools and receive the same education, which controls for the influence of educational factors. In addition, we controlled for some general cognitive variables related to math, such as reaction time, general intelligence, and spatial ability.

Materials and Methods

Participants

Participants were 508 Han and minority Chinese students in elementary schools (222 fourth graders and 286 fifth graders). The fourth graders (106 male and 116 female; 125.64 ± 9.5 months) comprised 118 Han students (64 male and 54 female) and 104 minority students (42 male and 62 female) and the fifth graders (152 male and 134 female; 138.23 ± 8.6 months) comprised 118 Han students (72 male and 46 female) and 168 minority students (80 male and 88 female).

All children were recruited voluntarily from public inner-city primary school in Xinjiang province. Each elementary school has roughly the same number of Han students and minority students, and each class of these schools has both minority and majority students. Therefore, all children should have equal access to the same education. The minority children (including Uygur and Kazak) are bilingual (with Uyghur or Kazakh as their first language, respectively), and have been exposed to bilingual environment since birth (Tsung and Cruickshank, 2009). They began their formal learning of Chinese since the preschool education stage, and their compulsory education stage is completely in the educational environment with the National common language as the communication medium (Jia, 2020, in Chinese version). All children had normal or corrected-to-normal vision, and no children were diagnosed with intellectual, behavioral or sensory impairments. Obtain parental consent before the classroom testing.

Tests

A total of seven tests were used. All tests were implemented through web-based applications in the Online Psychological Experiment System (OPES)1. Figure 1 shows the schematic of these seven tests. In addition to the self-adaptive mathematical achievement and self-adaptive reading achievement tests, each test had a training session followed by an official testing session. Six of these tests have been reported formerly (Zhou et al., 2015; Wang et al., 2016; Cui et al., 2019) and they displayed acceptable half-split reliabilities, ranging from 0.80 to 0.96. The self-adaptive reading achievement test is the only test we used that has not been previously reported. However, its design is essentially identical to that of the self-adaptive mathematical achievement test. Therefore, we speculate that it would also have an acceptable half-split reliability.

FIGURE 1
www.frontiersin.org

Figure 1. Schematic representation of tests used in the current study.

Simple Subtraction

This test is used to measure arithmetic fluency. It included 92 single-digit subtraction problems. A subtraction problem (e.g., 13–6) was appeared on the screen, with two candidate response displayed underneath. Every minuend ranged from 2 to 18, and answers ranged from 2 to 9. The incorrect choice deviated from true answer by ±3. The formal testing was restricted to 2 min.

The adjusted number of correct trials was utilized to control for the impact of speculating in multiple choice tests. The test grade was determined by deducting the quantity of erroneous reactions from the quantity of right reactions following the Guilford correction formula: S = RW/(n – 1), where S is the adjusted quantity of correct items without the help of possibility, R is the quantity of correct reactions, W is the number of mistakes, and n is the quantity of alternative responses for every item (Guilford, 1936). This correction methodology has been used as of late in researches of mathematical cognition (Cirino, 2011; Zhou et al., 2015; Cui et al., 2019) and cognition in general (Salthouse, 1994; Putz et al., 2004; Hedden and Yoon, 2006).

Self-Adaptive Mathematical Achievement

This test was used to measure overall mathematical achievement. It was self-adapting and had a time limit of 18 min. The questions were chosen from typical exercise books and test papers used by Chinese students, which were related to the school textbooks that were used by students throughout their education (12 grades: 6 in primary school, 3 in junior high school, and 3 in senior high school). All questions were mathematical problems of graded difficulty levels. For the primary school problems, the questions were relatively easy to solve [e.g., () + 7.4 = 16; (a) 4, (b) 5, (c) 6, (d) 7. Junior high school level questions were harder to solve [e.g., Which of the following is NOT a functional relationship? (a) y±X(x0), (b) y=X(x0), (c) y=-X(x0), (d) y=-X(x<0), (e) none of the above]. Additional examples can be seen in Figure 1. Problems for senior high school students were the hardest to solve (e.g., If the set A = 1, 2, 3, 4, B = x| x = n2, n∈A, then A∩B = (); (a) {1,4}, (b) {2,3}, (c) {9,6}, (d) {1,2}). The questions for each grade were randomly chosen and grouped into five sets. Each set included three questions that came from different chapters in the textbooks. The experimental procedure was as follows. Participants were first given a set of questions from the first grade. For each set, if they correctly solved at least two of the three problems, the difficulty level advanced to the next grade, if they answered two of the three problems incorrectly, the difficulty level remained at the same grade, and if they answered all three questions incorrectly, the difficulty level dropped a grade (unless it was already at the first grade). In addition, if participants failed to solve all five sets of questions in a grade, the difficulty level dropped a grade. The test stopped at the end of the allocated time or if all five sets of questions in the current grade were completed. The final score was calculated as the average value of the sum of weighted scores in each grade, which was the number of correctly answered questions multiplied by the grade level (1–12). A total of 1,701 problems were included in our test database.

Sentence Completion

The task resembled which utilized by previous research (So and Siegel, 1997) and was utilized to estimate reading comprehension fluency (e.g., Elbeheri et al., 2011; Träff et al., 2018). Materials of the task were based on the test materials utilized in Chinese schools. The vocabulary included in this test and the next one is all chosen according to the national curriculum standards of China to ensure that minority students will not encounter strange scenes and words. Moreover, the language materials are related to the content of language learning, but not to the content of national culture. For each experiment, a sentence with one word missing was appeared in in the middle of the computer screen. Participants required to choose one of the two candidate words at the bottom of the screen to fill the sentence. There were 120 questions, set from simple to hard. Participants need to choose answers fast and precisely, and to finish the trials as many as possible. The task’s formal test was restricted to 5 min.

Self-Adaptive Reading Achievement

Self-adaptive reading achievement was used to assess Chinese language comprehension ability. The test was self-adapted and had a time limit of 20 min. Task rules and procedures were the same as the self-adaptive math achievement test. The questions in the test mainly come from the school’s final exams of each semester, testing the students’ language ability, including language knowledge, understanding, discrimination and so on (e.g., The old grandpa smiled and said “Kid, you are honest. I will give you these two axes as well”). The child said: “old grandpa, they are not mine, I don’t want these,” then the child went away with his axe. The question: why does the old grandpa want to give the child axes? (a) The old grandpa has many axes, (b) The child doesn’t have axes, (c) The child is honest, (d) It has no cause.

Choice Reaction Time

The basic reaction time task was used to control for the influence of manual reaction and psychological processing speed. For each trial, a fixed cross is placed in the center of the screen. After fixating the cross for a period of time, a white dot, appeared on the left or right of the fixed cross. Participants were asked to press a key on the keyboard as soon as the white dot appeared. When the dot appeared on the left, they pressed “Q,” and when the dot appeared on the right, they pressed “P.” There were 30 trials. The inter-trial interval was chosen between 1,500 and 3,000 ms randomly.

The median reaction time and error rate were recorded, but the overall average error rate of the selected response time task was very low (4.72%), so we didn’t further analyzed.

Non-verbal Matrix Reasoning

Nonverbal matrix reasoning was utilized to assess general IQ. The test was adapted from Raven’s Progressive Matrices (Raven, 2000). A picture with a missing section was presented to the participants. The task was to choose the missing portion of the image from six candidate images. The test had 36 trials and stopped when five total trials were incorrect. The number of correct trials was used as the test score.

Mental Rotation

The mental rotation test was based from Shepard and Metzler (1971). The modified version had only two choices, and the time limit is 3 min. Each trial had three 3-D images: one of the images was at the top of the screen and the remaining two were at the bottom of the screen. Participants were asked to do a mental spin and decide which of the following two choices was the same as the one above. Rotated the image from the original image with a rotation angle ranging from 15° to 345° (at intervals of 15°). The other image was a mirror image of the target. If the selection was on the left, participants press the “Q” key and if the selection was on the right, participants press the “P” key. The adjusted number of correct trials was utilized as the test score (see the simple subtraction test).

Procedure

In the computer classroom, students (one class at a time) perform computerized tasks. Each class was supervised by two or three experimenters and the teacher of the class. The experimenter used slides to explain the description of each task. The teacher was present only for the purpose of discipline (e.g., remaining silence during the formal testing). After all students completed a test, the experimenter began to manage the following test. For each test, students were first instructed to complete an training session, and then continued the formal test. The training test contained 4–6 trials that were similar to those used in the formal test. In the training stage, for correct answers the feedback of all tasks was “Right! Can you do faster?”, and for incorrect answers was “Incorrect, another attempt, and you’ll succeed.” The formal test began after all the students completed the training stage. When all students understood the procedure during training stage, they could start the formal test. When the experimenter said “Go ahead!”, all students press a key to start the formal test. They all carry out their tasks in the same order. Responses and reaction times were automatically recorded.

Data Analysis

Descriptive statistics and pairwise correlations (with Bonferroni correction) were obtained for all tasks. Then, a series of multivariate analyses were carried out to test the difference in mathematics ability between Han majority and ethnic minority Chinese elementary school students. A stratified regression analysis was then performed, controlling for age (the variation in months within an age group), gender, three kinds of general cognitive processes (choice reaction time, non-verbal matrix reasoning, mental rotation), sentence completion, and self-adaptive reading achievement.

Results

Table 1 shows the means and standard deviations of scores and the ethnic differences for all seven tasks in the research.

TABLE 1
www.frontiersin.org

Table 1. Descriptive statistics for all the measures used in the study.

Table 2 reveals the correlations between all measures used in the research, with minority Chinese above the diagonal and Han Chinese below the diagonal. Bonferroni correction (p-value × 21) was used adjust for the 21 comparisons. A corrected p-value < 0.05 was considered statistically significant.

TABLE 2
www.frontiersin.org

Table 2. Correlations between the measures used in the study for Minority (above the diagonal) and Han (below the diagonal).

Table 3 reveals the partial correlations between all measures used in the research, with minority Chinese above the diagonal and Han Chinese below the diagonal (controlling for age and gender). Bonferroni correction (p-value × 21) was used adjust for the 21 comparisons. A corrected p-value < 0.05 was considered statistically significant. The relationships between measures for mathematics achievement and language ability were consistent across the two grades (4th and 5th), indicating stability over time.

TABLE 3
www.frontiersin.org

Table 3. Partial correlations between the measures used in the study for Minority (above the diagonal) and Han (below the diagonal) (controlling for age and gender).

Table 4 shows the contribution of ethnicity to ethnic differences after controlling for age, gender, and the three general cognitive factors. The two sets of data display consistent results for children in fourth and fifth grade, again indicating that ethnic differences are stable over time.

TABLE 4
www.frontiersin.org

Table 4. Hierarchical regression models predicting simple subtraction and self-adaptive mathematical achievement from age, gender, general cognitive processing, and ethnicity.

Table 5 shows the unique contribution of language ability to ethnic differences after controlling for demographic variables (age and gender), the three general cognitive factors, the two types of language ability (self-adaptive reading achievement and sentence completion), and ethnicity. The two panels show consistent results for Han children and minority children in fourth and fifth grade; after controlling for language, ethnic differences in math scores cease to exist.

TABLE 5
www.frontiersin.org

Table 5. Hierarchical regression models predicting simple subtraction and self-adaptive mathematical achievement from age, gender, general cognitive processing, sentence completion, self-adaptive reading achievement, and ethnicity.

The regression results in Tables 4, 5 displayed that the ethnic differences in math scores can be explained by language ability (measured by sentence completion and self-adaptive reading achievement); when ethnicity was added to the regression (in the fourth step), it did not significantly contribute to simple subtraction or self-adaptive mathematical achievement for children of any age group. It can also be seen from Figure 2 that the ethnic differences in simple subtraction and self-adaptive mathematical achievement were significantly reduced after controlling for language factors.

FIGURE 2
www.frontiersin.org

Figure 2. Differences in the mean of the standardized residuals for subtraction and self-adapted math achievement between Han and minority elementary school students for fourth grade (top) and fifth grade (bottom) after controlling for age, gender (Step 1), choice reaction time, non-verbal matrix reasoning, and mental rotation (Step 2), sentence completion, and self-adapted reading comprehension (Step 3).

Discussion

The current investigation examined whether skill at the Chinese language, as an official language, could explain mathematical achievement difference among ethnic groups in China. After controlling for age, gender, and general cognitive factors, we found that a significant difference in mathematical processing remained between minority pupils and Han pupils. Further controlling for language processing (measured by sentence completion and self-adaptive reading achievement), these ethnic differences in mathematical achievement disappeared. This means that language ability could account for the ethnic differences in mathematical achievement.

The current study repeatedly verified the findings of most previous studies; academic achievement in ethnic minorities is significantly lower than that of the ethnic majority, and both Chinese and international studies have found similar phenomenon (Potter and Morris, 2016; Sonnenschein and Sun, 2016; Kuhfeld et al., 2018). First, mathematical performance in ethnic minorities is lower, in both overall mathematical achievement and in special types of mathematical ability, such as arithmetic computation and problem solving (Kong and Orosco, 2015; Lonigan et al., 2015; Spörlein and Schlueter, 2018; Saw et al., 2018; Assari et al., 2020). Second, language skills in the county’s official language is lower in ethnic minorities than in the ethnic majority (Potter and Morris, 2016; Sonnenschein and Sun, 2016; Kuhfeld et al., 2018). This is reflected in the poorer ability of ethnic minorities to use the official language to understand mathematical terms and to express mathematical knowledge (Cummins, 1979, 2000; Guan, 2020, in Chinese version).

The current study further found that the degree of mathematical disadvantage observed in ethnic minorities can differ depending on the type of math. In mathematics, the ethnic minority performance was better on simple problems than it was on complex problems. Thus, their disadvantage for calculation ability was less than that for problem-solving. This may be because calculation is relatively simple in that it can be completed through memory retrieval and improved through frequent practice (Calderón, 2016). In contrast, problem solving is more complex and difficult, requires a wide range of knowledge, and a deeper conceptual understanding (Passolunghi et al., 2018; Villeneuve et al., 2019). Supporting this idea, one study found that ethnic minority students at risk for math difficulties struggle with word problems for various reasons beyond procedural or calculation challenges (Kong and Orosco, 2015).

We also found that ethnic differences in language depending on the type of language skill being tested. Unlike the situation with math, we found that the disadvantage for reading comprehension of relatively simple sentences was far greater than that for more complex text. This may be because the sentence itself contains fewer words, so it provides less information. If a student has difficulty in the orthography or semantics of just one or two key words, it will affect understanding of the whole sentence (Scott, 2009; Poulsen and Gravgaard, 2016). When texts are longer and more information is provided, if individual words cannot be understood clearly, the overall meaning might still be understood based on the remaining text. Additionally, students can also use reasoning and other strategies to solve problems on reading comprehension tests (Segers and Verhoeven, 2016).

We confirmed our hypothesis that the low mathematical ability observed in ethnic minorities is caused by their low language ability. This is consistent with the circumstantial evidence found in previous studies (Vukovic and Lesaux, 2013; Wilkinson, 2018). For example, among Latinos, Limited English Proficiency (LEP) has generally been linked to lower achievement outcomes in mathematics (Padilla and Gonzalez, 2001; Eamon, 2005). North American immigrants also performed at lower math levels than non-immigrants, and it is speculated that this may be because immigrants are less proficient in English (Barrett et al., 2012).

The low mathematical ability observed in ethnic minorities might also come from the influence of their mother tongue thinking. Though the word formation of numerals is similar among Chinese, Uyghur and Kazakh languages (Pan, 1982, in Chinese version; Liu et al., 2012, in Chinese Version), there is linguistic difference in word order between Uyghur and Chinese, that is, Uyghur has “subject + object + verb” structure, while has “subject + verb + object” structure. The psychological stereotype of word order of mother tongue would also influence mathematics processing, especially arithmetic. Students sometimes can’t figure out the operation sequence when doing the four operations. For example, when calculating “144–960 ÷ 48”, influenced by their mother tongue, they might first calculate “960–144”, and then “÷ 48”; when fourth-graders reading decimals, they are distracted by this grammatical discrepancy and often pronounce “11.01” as “one zero point one one”, and “0.32” will be read as “twenty-three point zero” (Zhang and Gao, 2016, in Chinese version). The main reason for this is that minority students’ Chinese ability is not proficient enough, which makes them subconsciously choose their own language to deal with mathematical problems. However, mutual interference between the two languages that minority bilinguals master (e.g., Kroll et al., 2014) would cause a slower speed than Han students to solve mathematical problems. This has also been confirmed in previous studies that bilinguals performed lower than first language learners in early calculation (Bonifacci et al., 2016).

The results of this study further support the theory that mathematics and language are highly correlated (Vukovic and Lesaux, 2013; Foster et al., 2018; Schmitt et al., 2019). Previous studies have also speculated that disadvantages in mathematics seen in minority groups are related to their poorer ability to use the official language in the classroom. It is difficult for both teachers and students of minority populations to express mathematical knowledge in the official language. For example, German minority student math scores are usually lower than those of native students. Moreover, math problems with high linguistic requirements are more difficult for students who use native ethnic minority languages, especially when they include special academic vocabulary (such as “denominator”) (Haag et al., 2014). Our research shows that the poor official language ability of ethnic minorities is not only reflected in their mathematical knowledge or the scientific language used in textbooks, but also in the general language ability used in daily life. Therefore, general language ability itself can explain the relatively poorer mathematical ability seen in ethnic minorities.

Though minority students usually has lower scores in language tests, their listening and speaking ability may be far better than their reading and writing ability. Indeed, on the ethnic minority preparatory Chinese level test, their listening comprehension ability is better than their reading comprehension and writing abilities (Guan, 2020, in Chinese version). Previous studies have also reported that ethnic minorities can communicate with others skillfully in official languages and communicate without obstacles in daily life (Yu, 2012, in Chinese version; Shi, 2018, in Chinese version). In China, most ethnic minority students have no problems in daily communication, but their scores on Chinese exams are poor (Yang, 2015, in Chinese version), which indicates that daily Chinese is not exactly the same as the Chinese used on exams or in mathematics. Mathematics usually uses academic language (Haag et al., 2014), which is frequently assumed to be especially challenging for minority students (Heppt et al., 2014). For second-language learners, mastering academic language is also expected to be more challenging than mastering daily language (Cummins, 1979, 2000). Their lack of communication in the classroom could be due to the lack of scientific and mathematical vocabulary in their native minority languages, whereas the vocabulary of everyday life is generally translatable between all languages. This shows that the development of the two functions of language as a tool of social communication and as a vehicle of imparting knowledge may be asynchronous.

Although relatively poor official language ability of ethnic minority students may affect their math performances, this does not mean that they should abandon their national language. Minority students are generally able to master multiple languages, and making good use of their multilingual advantages in mathematics education may be a more economical and efficient method (e.g., Erath et al., 2021). Prediger et al. (2019) provided an example of multilingual resources to help seventh graders deepen their concept understanding of fractions. In short, language should be the perfect carrier for establishing fair and high-quality mathematics education and teaching.

This study also examined the general cognitive level of ethnic minorities and as expected, found it does not significantly differ from that of ethnic majority groups. This could be because education has a limited impact on general cognitive ability, or it could be that the education minorities receive is sufficient to support normal cognitive development. This study cannot verify which hypothesis is correct, but nevertheless, we can say that intelligence levels between ethnic minorities and the majority are comparable and cannot explain differences in mathematical achievement.

The current study also found that language processing could make ethnic differences disappear. This verifies the importance of language in mathematics, and is consistent with previous findings that language factors, such as well-developed vocabulary knowledge, listening comprehension, and content specific language (i.e., providing direct and explicit instruction with math terminology and concepts), play independent and significant roles in mathematical ability (Rupley and Nichols, 2005).

Although this paper did investigate parental involvement in education or social emotional attitudes, these are variables to be studied in the future. However, the existing literature has shown that the degree of parental education involvement and student social emotional attitudes are related to socioeconomic status (Liu et al., 2015, in Chinese version). As we limited the socioeconomic impact on the results through the balanced selection of participants, the impact of these two variables might also have been controlled for. Nevertheless, for a complete accounting, we should add social factors such as socioeconomic status, educational resources, parental involvement, and social emotion perspective to future research. These four variables have been shown to be related to ethnic differences in academic performance (Marks, 2005; Yan and Lin, 2005; Brinbaum and Cebolla-Boado, 2007; Lee, 2012; Brandmiller et al., 2020; Triventi et al., 2021). The impact of language on mathematic should be re-examined after strictly controlling these variables.

Our results can help improve mathematics education for ethnic minorities. In the future, mathematics education for minority students should focus on the consistency between the language used when teaching mathematics and the official language. Language, especially mathematical language, is a significant predictor of children’s numeracy performance (Purpura and Reid, 2016), and we can consider strengthening Chinese language ability and improving the Chinese language level of minorities, which will help them achieve more academically. Therefore, the next research plan is to carry out an intervention study that employs verbalized mathematics.

Moreover, language intervention might also be useful to improve mathematical ability for majority group students. The difference in language ability can explain not only the ethnic differences in mathematical abilities between minority group students and majority group students, but also the individual differences in mathematical abilities among majority students (e.g., Singer et al., 2019; Peng et al., 2020; Wang et al., 2020; Lin et al., 2021). Previous studies usually paid attention to the effect of mathematical language training on mathematics performances (e.g., Gibson et al., 2020; Purpura et al., 2021), the intervention effect of pure natural language itself should also be into our sight in future.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

The studies involving human participants were reviewed and approved by the Beijing Normal University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

JC and XZ designed and performed the research. JC, HD, and LL analyzed the data. JC and LL wrote the manuscript. JC, LL, ZC, and XZ revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the grant from the National Natural Science Foundation of China (Project No. 31671151) and 111 Project (Project No. BP0719032).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

  1. ^ www.dweipsy.com/lattice

References

Adams, M., and Hanna, P. (2012). Your past is not their present: time, the other, and ethnocentrism in cross-cultural personality psychology. Theory Psychol. 22, 436–451. doi: 10.1177/0959354311412107

CrossRef Full Text | Google Scholar

Ali, S., and Frederickson, N. (2010). The parenting dimensions of British Pakistani and White mothers of primary school children. Infant Child Dev. 20, 313–329. doi: 10.1002/icd.696

CrossRef Full Text | Google Scholar

Alonso-Arbiol, I., van de Vijver, F. J. R., Fernandez, I., Paez, D., Campos, M., and Carrera, P. (2011). Implicit theories about interrelations of anger components in 25 countries. Emotion 11, 1–11. doi: 10.1037/a0020295

PubMed Abstract | CrossRef Full Text | Google Scholar

An, S. H., and Cheng, Y. (2012). Yin du shao shu min zu jiao yu ji hui ping deng zheng ce yan jiu[A study on the policy of equality of school opportunities for ethnic minorities in India]. Compar. Educ. Res. 34, 12–16.

Google Scholar

Arikan, S., van de Vijver, F. J. R., and Yagmur, K. (2020). Mainstream and immigrant students’ primary school mathematics achievement differences in European countries. Eur. J. Psychol. Educ. 2020:2. doi: 10.1007/s10212-019-00456-2

CrossRef Full Text | Google Scholar

Arouri, M., Ben-Youssef, A., and Nguyen, C. V. (2019). Ethnic and racial disparities in children’s education: comparative evidence from Ethiopia, India, Peru and Viet Nam. Child. Youth Serv. Rev. 100, 503–514. doi: 10.1016/j.childyouth.2019.03.031

CrossRef Full Text | Google Scholar

Assari, S., Boyce, S., Bazargan, M., and Caldwell, C. H. (2020). Mathematical Performance of American Youth: diminished Returns of Educational Attainment of Asian-American Parents. Educ. Sci. 10:32. doi: 10.3390/educsci10020032

CrossRef Full Text | Google Scholar

August, D., Shanahan, T., and Escamilla, K. (2009). English Language Learners: Developing Literacy in Second-Language Learners—Report of the National Literacy Panel on Language-Minority Children and Youth. J. Liter. Res. 41, 432–452. doi: 10.1080/10862960903340165

CrossRef Full Text | Google Scholar

Azmitia, M., Cooper, C. R., and Brown, J. R. (2008). Support and Guidance from Families, Friends, and Teachers in Latino Early Adolescents’ Math Pathways. J. Early Adolesc. 29, 142–169. doi: 10.1177/0272431608324476

CrossRef Full Text | Google Scholar

Bares, C. B., and Andrade, F. H. (2012). Racial/ethnic differences in the longitudinal progression of co-occurring negative affect and cigarette use: From adolescence to young adulthood. Addict. Behav. 37, 632–640. doi: 10.1016/j.addbeh.2012.01.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Barrett, A. N., Barile, J. P., Malm, E. K., and Weaver, S. R. (2012). English proficiency and peer interethnic relations as predictors of math achievement among Latino and Asian immigrant students. J. Adolesc. 35, 1619–1628. doi: 10.1016/j.adolescence.2012.08.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Bartel-Radic, A., and Giannelloni, J.-L. (2017). A renewed perspective on the measurement of cross-cultural competence: an approach through personality traits and cross-cultural knowledge. Eur. Manag. J. 35, 632–644. doi: 10.1016/j.emj.2017.02.003

CrossRef Full Text | Google Scholar

Barwell, R. (2018). From language as a resource to sources of meaning in multilingual mathematics classrooms. J. Mathemat. Behav. 50, 155–168. doi: 10.1016/j.jmathb.2018.02.007

CrossRef Full Text | Google Scholar

Basant, R., and Sen, G. D. (2014). Access to Higher Education in India: an Exploration of Its Antecedents. SSRN Electr. J. 2014:2535644. doi: 10.2139/ssrn.2535644

CrossRef Full Text | Google Scholar

Baulch, B., Nguyen, H. T. M., Phuong, P. T. T., and Pham, H. T. (2010). Ethnic minority poverty in Vietnam. Manchester: Chronic Poverty Research Centre (CPRC).

Google Scholar

Baxter, S. D., Guinn, C. H., Tebbs, J. M., and Royer, J. A. (2013). There Is No Relationship between Academic Achievement and Body Mass Index among Fourth-Grade, Predominantly African-American Children. J. Acad. Nutr. Diet. 113:4. doi: 10.1016/j.jand.2013.01.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Benítez, I., He, J., Van de Vijver, F. J. R., and Padilla, J.-L. (2016). Linking extreme response style to response processes: a cross-cultural mixed methods approach. Internat. J. Psychol. 51, 464–473. doi: 10.1002/ijop.12379

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhui, K., Mohamud, S., Warfa, N., Curtis, S., Stansfeld, S., and Craig, T. (2012). Forced residential mobility and social support: impacts on psychiatric disorders among Somali migrants. BMC Internat. Health Hum. Rights 12:1. doi: 10.1186/1472-698x-12-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Bonifacci, P., Tobia, V., Bernabini, L., and Marzocchi, G. M. (2016). Early Literacy and Numeracy Skills in Bilingual Minority Children: toward a Relative Independence of Linguistic and Numerical Processing. Front. Psychol. 7:1020. doi: 10.3389/fpsyg.2016.01020

PubMed Abstract | CrossRef Full Text | Google Scholar

Brandmiller, C., Dumont, H., and Becker, M. (2020). Teacher Perceptions of Learning Motivation and Classroom Behavior: the Role of Student Characteristics. Contemp. Educ. Psychol. 2020:101893. doi: 10.1016/j.cedpsych.2020.101893

CrossRef Full Text | Google Scholar

Brinbaum, Y., and Cebolla-Boado, H. (2007). The school careers of ethnic minority youth in France. Ethnicities 7, 445–474. doi: 10.1177/1468796807080237

CrossRef Full Text | Google Scholar

Butterworth, B., Reeve, R., and Reynolds, F. (2011). Using mental representations of space when words are unavailable: studies of enumeration and arithmetic in indigenous Australia. J. Cross-Cult. Psychol. 42, 630–638. doi: 10.1177/0022022111406020

CrossRef Full Text | Google Scholar

Calderón, C. O. (2016). Mathematical development: the role of broad cognitive processes. Educ. Psychol. Practice 32, 107–121. doi: 10.1080/02667363.2015.1114468

CrossRef Full Text | Google Scholar

Cardel, M., Willig, A. L., Dulin-Keita, A., Casazza, K., Mark Beasley, T., and Fernández, J. R. (2012). Parental feeding practices and socioeconomic status are associated with child adiposity in a multi-ethnic sample of children. Appetite 58, 347–353. doi: 10.1016/j.appet.2011.11.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Caughy, M. O., Brinkley, D. Y., Smith, E. P., and Owen, M. T. (2020). Fathering quality in early childhood and kindergarten achievement in low-income racial-ethnic minority children. J. Family Psychol. 34, 215–225. doi: 10.1037/fam0000611

PubMed Abstract | CrossRef Full Text | Google Scholar

Ceballo, R., Maurizi, L. K., Suarez, G. A., and Aretakis, M. T. (2014). Gift and sacrifice: parental involvement in Latino adolescents’ education. Cult. Div. Ethnic Minor. Psychol. 20, 116–127. doi: 10.1037/a0033472

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, L. P., and Zhong, D. D. (2018). Gai ge kai fang 40 nian lai wo guo min zu jiao yu li fa de hui gu yu fan si[Retrospection and Reflection on Ethnic Education Legislation in China in the Past 40 Years of the Reform and Opening-up]. J. Res. Educ. Ethnic Minorit. 05:001. doi: 10.15946/j.cnki.1001-7178.2018.05.001

CrossRef Full Text | Google Scholar

Chen, T., and Xu, R. (2018). Zhong guo shao shu min zu shu xue jiao yu de “xian zhuang”,“wen ti”, yu“dui ce”[“Status Quo”, “Problems” and “Countermeasures” of Mathematics Education of Ethnic Minorities in China – Proceedings of 2017 National Mathematics Education Symposium for Ethnic Minorities and Tibet Mathematics Teaching Reform Symposium]. J. Mathem. Educ. 27, 96–98.

Google Scholar

Chen, X., and Graham, S. (2015). Cross-Ethnic Friendships and Intergroup Attitudes Among Asian American Adolescents. Child Dev. 86, 749–764. doi: 10.1111/cdev.12339

PubMed Abstract | CrossRef Full Text | Google Scholar

Cirino, P. T. (2011). The interrelationships of mathematical precursors in kindergarten. J. Exp. Child Psychol. 108, 713–733. doi: 10.1016/j.jecp.2010.11.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Cook, W. K., and Caetano, R. (2014). Ethnic Drinking Cultures, Gender, and Socioeconomic Status in Asian American and Latino Drinking. Alcoholism: Clin. Exp. Res. 38, 3043–3051. doi: 10.1111/acer.12573

PubMed Abstract | CrossRef Full Text | Google Scholar

Cui, J., Zhang, Y., Wan, S., Chen, C., Zeng, J., and Zhou, X. (2019). Visual form perception is fundamental for both reading comprehension and arithmetic computation. Cognition 189, 141–154. doi: 10.1016/j.cognition.2019.03.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Cummings, J. R., Wen, H., and Druss, B. G. (2011). Racial/Ethnic Differences in Treatment for Substance Use Disorders Among U.S. Adolescents. J. Am. Acad. Child Adolesc. Psychiatry 50, 1265–1274. doi: 10.1016/j.jaac.2011.09.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Cummins, J. (1979). Cognitive/academic language proficiency, linguistic interdependence, the optimum age question and some other matters. Work. Papers Bilingual. 19, 121–129.

Google Scholar

Cummins, J. (2000). Language, power and pedagogy: bilingual children in the crossfire. Clevedon: Multilingual Matters LTD.

Google Scholar

Dahl, G. B., and Lochner, L. (2012). The Impact of Family Income on Child Achievement: evidence from the Earned Income Tax Credit. Am. Econ. Rev. 102, 1927–1956. doi: 10.1257/aer.102.5.1927

CrossRef Full Text | Google Scholar

Das, B. (1982). Untouchability, scheduled castes and nation building. Soc. Act. 32, 269–282.

Google Scholar

Dolean, D., and Cãlugãr, A. (2020). How Reliably Can We Measure a Child’s True IQ? Socio-Economic Status Can Explain Most of the Inter-Ethnic Differences in General Non-verbal Abilities. Front. Psychol. 11:2000. doi: 10.3389/fpsyg.2020.02000

PubMed Abstract | CrossRef Full Text | Google Scholar

Eamon, M. K. (2005). Social-Demographic, School, Neighborhood, and Parenting Influences on the Academic Achievement of Latino Young Adolescents. J. Youth Adolesc. 34, 163–174. doi: 10.1007/s10964-005-3214-x

CrossRef Full Text | Google Scholar

Elbeheri, G., Everatt, J., Mahfoudhi, A., Al-Diyar, M. A., and Taibah, N. (2011). Orthographic processing and reading comprehension among arabic speaking mainstream and ld children. Dyslexia 17, 123–142. doi: 10.1002/dys.430

PubMed Abstract | CrossRef Full Text | Google Scholar

Erath, K., Ingram, J., Moschkovich, J., and Prediger, S. (2021). Designing and enacting instruction that enhances language for mathematics learning: a review of the state of development and research. ZDM – Mathemat. Educ. 53, 245–262. doi: 10.1007/s11858-020-01213-2

CrossRef Full Text | Google Scholar

Feilhauer, J., Cima, M., and Arntz, A. (2012). Assessing callous–unemotional traits across different groups of youths: further cross-cultural validation of the Inventory of Callous–Unemotional Traits. Internat. J. Law Psychiatry 35, 251–262. doi: 10.1016/j.ijlp.2012.04.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Foster, M. E., Anthony, J. L., Zucker, T. A., and Branum-Martin, L. (2018). Prediction of English and Spanish kindergarten mathematics from English and Spanish cognitive and linguistic abilities in Hispanic dual language learners. Early Childh. Res. Q. 46, 213–227. doi: 10.1016/j.ecresq.2018.02.007

CrossRef Full Text | Google Scholar

Gallanter, M. (1984). Competing lnequalities: Law and the Backward Classes in India. California: University of California Press.

Google Scholar

Ghafoor, H., Ahmad, R. A., Nordbeck, P., Ritter, O., Pauli, P., and Schulz, S. M. (2019). A cross-cultural comparison of the roles of emotional intelligence, metacognition, and negative coping for health-related quality of life in German versus Pakistani patients with chronic heart failure. Br. J. Health Psychol. 2019:12381. doi: 10.1111/bjhp.12381

PubMed Abstract | CrossRef Full Text | Google Scholar

Giacchino-Baker, R. (2007). Educating Ethnic Minorities in Vietnam: policies and Perspectives. Kappa Delta Pi Record 43, 168–173. doi: 10.1080/00228958.2007.10516476

CrossRef Full Text | Google Scholar

Gibson, D. J., Gunderson, E. A., and Levine, S. C. (2020). Causal effects of parent number talk on preschoolers’ number knowledge. Child Dev. 91, e1162–e1177. doi: 10.1111/cdev.12423

CrossRef Full Text | Google Scholar

Gonzales, P., Guzmán, J. C., Partelow, L., Pahlke, E., Jocelyn, L., Kastberg, D., et al. (2004). Highlights from the Trends in International Mathematics and Science Study (TIMSS) 2003 (NCES 2005-005). Washington, DC: U.S. Department of Education, National Center for Education Statistics. U.S. Government Printing Office.

Google Scholar

Gorelick, M. H., Gould, L., Nimmer, M., Wagner, D., Heath, M., Bashir, H., et al. (2011). Perceptions About Water and Increased Use of Bottled Water in Minority Children. Archiv. Pediatr. Adolesc. Med. 165:928. doi: 10.1001/archpediatrics.2011.83

PubMed Abstract | CrossRef Full Text | Google Scholar

Greene, K. M., and Maggs, J. L. (2020). Longitudinal Change in Alcohol Use and Motivations for Drinking Among Asian American College Students. Alcoholism 2020:14436. doi: 10.1111/acer.14436

PubMed Abstract | CrossRef Full Text | Google Scholar

Greene, K. M., Eitle, T. M., and Eitle, D. (2014). Adult social roles and alcohol use among American Indians. Addict. Behav. 39, 1357–1360. doi: 10.1016/j.addbeh.2014.04.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Guan, H. (2020). Shao shu min zu han yu shui ping deng ji kao shi cheng ji fen xi ji dui jiao xue de fan si: yi ru xue fen ban kao shi wei li[Analysis of Ethnic Minority Chinese Proficiency Test scores and Reflection on Teaching: a case study of entrance Placement test]. J. Kaifeng Vocat. Coll. Cult. Art 02, 96–98.

Google Scholar

Guilford, J. P. (1936). The determination of item difficulty when chance success is a factor. Psychometrika 1, 259–264. doi: 10.1007/BF02287877

CrossRef Full Text | Google Scholar

Haag, N., Heppt, B., Roppelt, A., and Stanat, P. (2014). Linguistic simplification of mathematics items: effects for language minority students in Germany. Eur. J. Psychol. Educ. 30, 145–167. doi: 10.1007/s10212-014-0233-6

CrossRef Full Text | Google Scholar

Halpern-Manners, A., Warren, J. R., and Brand, J. E. (2009). Dynamic measures of primary and secondary school characteristics: implications for school effects research. Soc. Sci. Res. 38, 397–411. doi: 10.1016/j.ssresearch.2008.11.002

CrossRef Full Text | Google Scholar

Han, D., Park, H., and Rhee, S.-Y. (2021). The Role of Regulatory Focus and Emotion Recognition Bias in Cross-Cultural Negotiation. Sustainability 13:2659. doi: 10.3390/su13052659

CrossRef Full Text | Google Scholar

He, W., and Sun, X. T. (2019). Min zu di qu xue sheng shu xue cheng ji de guan jian ying xiang yin su fen xi[An Analysis of the Key Influencing Factors on Mathematics Achievement of Students in Ethnic Regions]. J. Res. Educ. Ethnic Minorit. 02:006. doi: 10.15946/j.cnki.1001-7178.2019.02.006

CrossRef Full Text | Google Scholar

Hecht, H., Vogt, S., and Prinz, W. (2001). Motor learning enhances perceptual judgment: a case for action-perception transfer. Psychol. Res. 65, 3–14. doi: 10.1007/s004260000043

PubMed Abstract | CrossRef Full Text | Google Scholar

Hedden, T., and Yoon, C. (2006). Individual differences in executive processing predict susceptibility to interference in verbal working memory. Neuropsychology 20, 511–528. doi: 10.1037/0894-4105.20.5.511.supp

CrossRef Full Text | Google Scholar

Heppt, B., Haag, N., Böhme, K., and Stanat, P. (2014). The Role of Academic-Language Features for Reading Comprehension of Language-Minority Students and Students From Low-SES Families. Read. Res. Q. 50, 61–82. doi: 10.1002/rrq.83

CrossRef Full Text | Google Scholar

Hu, Y. Y., and Zhou, G. T. (2018). Wo guo shao shu min zu shu xue jiao yu yan jiu de re dian he fa zhan qu shi[The hot spot and development trend of Chinese minority mathematics education research]. J. Mathem. Educ. 27, 88–91.

Google Scholar

Huang, K.-Y., O’Brien Caughy, M., Genevro, J. L., and Miller, T. L. (2005). Maternal knowledge of child development and quality of parenting among White, African-American and Hispanic mothers. J. Appl. Dev. Psychol. 26, 149–170. doi: 10.1016/j.appdev.2004.12.001

CrossRef Full Text | Google Scholar

Huang, R. Q. (1998). Yin du de wen mang wei he yue sao yue duo[Why is Illiteracy increasing in India]. J. Fuzhou Teach. Coll. 04, 69–71.

Google Scholar

Jia, Y. (2020). Guo jia tong yong yu yan zai xin jiang tui guang he pu ji de you xiao xing yan jiu[Study on the effectiveness of the popularization and popularization of the National Lingua franca in Xinjiang]. Front. China 02:3–16.

Google Scholar

Jia, Y. L. (2007). Yin du shao shu min zu zheng ce ji qi dui zhong guo de qi shi[Indian Minority Policy and its Enlightenment to China]. J. Hubei Univ. National. 2007, 27–32.

Google Scholar

Julien, C. L., Thompson, J. C., Neary, D., and Snowden, J. S. (2008). Arithmetic knowledge in semantic dementia: is it invariably preserved? Neuropsychologia 46, 2732–2744. doi: 10.1016/j.neuropsychologia.2008

CrossRef Full Text | Google Scholar

Kant, A. K., and Graubard, B. I. (2011). 20-Year Trends in Dietary and Meal Behaviors Were Similar in U.S. Children and Adolescents of Different Race/Ethnicity. The Journal of Nutrition 141, 1880–1888. doi: 10.3945/jn.111.144915

PubMed Abstract | CrossRef Full Text | Google Scholar

Karuna, C. (1993). Accessing higher education: the dilemma of schooling women, minorities, Scheduled Castes and Scheduled Tribes in contemporary India. High. Educ. 26, 69–92. doi: 10.1007/bf01575107

CrossRef Full Text | Google Scholar

Kemme, S. (2010). Die kulturelle Sozialisation als Determinante delinquenten Verhaltens und Suchtmittelumgangs bei westlichen und muslimischen Jugendlichen. Monatsschrift für Kriminologie und Strafrechtsreform 93, 126–146. doi: 10.1515/mks-2010-930203

CrossRef Full Text | Google Scholar

Klebanov, P. K., Brooks-Gunn, J., McCarton, C., and McCormick, M. C. (1998). The Contribution of Neighborhood and Family Income to Developmental Test Scores over the First Three Years of Life. Child Dev. 69:1420. doi: 10.2307/1132275

CrossRef Full Text | Google Scholar

Kobayashi, E., and Farrington, D. P. (2019). Influence of Peer Reactions and Student Attitudes on Student Deviance: differences Between Japan and the United States. Internat. J. Offend. Ther. Comp. Criminol. 2019:0306624X1983216. doi: 10.1177/0306624x19832168

PubMed Abstract | CrossRef Full Text | Google Scholar

Kong, J. E., and Orosco, M. J. (2015). Word-Problem-Solving Strategy for Minority Students at Risk for Math Difficulties. Learn. Disab. Q. 39, 171–181. doi: 10.1177/0731948715607347

CrossRef Full Text | Google Scholar

Kroll, J. F., Bobb, S. C., and Hoshino, N. (2014). Two Languages in Mind. Curr. Direct. Psychol. Sci. 23, 159–163. doi: 10.1177/0963721414528511

PubMed Abstract | CrossRef Full Text | Google Scholar

Kuhfeld, M., Gershoff, E., and Paschall, K. (2018). The development of racial/ethnic and socioeconomic achievement gaps during the school years. J. Appl. Dev. Psychol. 57, 62–73. doi: 10.1016/j.appdev.2018.07.001

CrossRef Full Text | Google Scholar

Kuśnierz, C., Rogowska, A. M., and Pavlova, I. (2020). Examining Gender Differences, Personality Traits, Academic Performance, and Motivation in Ukrainian and Polish Students of Physical Education: a Cross-Cultural Study. Internat. J. Env. Res. Public Health 17:5729. doi: 10.3390/ijerph17165729

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J. (2012). Educational Equity and Adequacy for Disadvantaged Minority Students: School and Teacher Resource Gaps Toward National Mathematics Proficiency Standard. J. Educ. Res. 105, 64–75. doi: 10.1080/00220671.2010.519409

CrossRef Full Text | Google Scholar

Lemke, M., Sen, A., Pahlke, E., Partelow, L., Miller, D., Williams, T., et al. (2004). International Outcomes of Learning in Mathematics Literacy and Problem Solving: PISA 2003 Results From the U.S. Perspective. (NCES 2005–003). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

Google Scholar

Lin, X., Peng, P., and Zeng, J. (2021). Understanding the Relation between Mathematics Vocabulary and Mathematics Performance. Element. Sch. J. 121, 504–540. doi: 10.1086/712504

CrossRef Full Text | Google Scholar

Link, T. C. (2008). Youthful Intoxication: a Cross-Cultural Study of Drinking Among German and American Adolescents. J. Stud. Alcoh. Drugs 69, 362–370. doi: 10.15288/jsad.2008.69.362

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, B. Z., Zhang, Y. Y., and Li, J. X. (2015). Jia ting she hui jing ji di wei yu qing shao nian jiao yu qi wang fu mu can yu de zhong jie zuo yong[Family socioeconomic status and adolescents’ educational expectations: the Mediating role of parental participation]. Peking Univ. Educ. Rev. 13, 158–176+192.

Google Scholar

Liu, C., Zhang, Q., and Razati, N. (2012). Xin jiang ha sa ke zu shu xue wen hua diao cha fen xi[Investigation and analysis of mathematical culture of Xinjiang Kazak ethnic group]. J. Corps Coll. Educ. 04, 17–25.

Google Scholar

Liu, J., Cui, P., Luo, H. Y., Yang, J. M., Zhu, B., and Qian, L. K. (2019). Yun nan zhong xiao xue shao shu min zu yu han zu xue sheng shu xue xue xi de bi jiao yan jiu[A Comparative Study of Mathematics Learning between Minority and Han Students in Primary and Secondary Schools of Yunnan]. J. Qujing Norm. Univ. 06, 7–10.

Google Scholar

Lonigan, C. J., Allan, D. M., Goodrich, J. M., Farrington, A. L., and Phillips, B. M. (2015). Inhibitory Control of Spanish-Speaking Language-Minority Preschool Children: measurement and Association With Language, Literacy, and Math Skills. J. Learn. Disab. 50, 373–385. doi: 10.1177/0022219415618498

PubMed Abstract | CrossRef Full Text | Google Scholar

Lus, G., and Mukaddes, N. M. (2009). Co-morbidity of bipolar disorder in children and adolescents with attention deficit/hyperactivity disorder (ADHD) in an outpatient Turkish sample. World J. Biol. Psychiatry 10, 488–494. doi: 10.1080/15622970902929876

PubMed Abstract | CrossRef Full Text | Google Scholar

Mancinelli, E., Liberska, H. D., Li, J.-B., Espada, J. P., Delvecchio, E., Mazzeschi, C., et al. (2021). A Cross-Cultural Study on Attachment and Adjustment Difficulties in Adolescence: the Mediating Role of Self-Control in Italy, Spain, China, and Poland. Internat. J. Env. Res. Public Health 18:8827. doi: 10.3390/ijerph18168827

PubMed Abstract | CrossRef Full Text | Google Scholar

Marks, G. N. (2005). Accounting for immigrant non-immigrant differences in reading and mathematics in twenty countries. Ethn. Rac. Stud. 28, 925–946. doi: 10.1080/01419870500158943

CrossRef Full Text | Google Scholar

Matlasz, T. M., Frick, P. J., and Clark, J. E. (2020). Understanding the Social Relationships of Youth with Callous-Unemotional Traits Using Peer Nominations. Journal of Clinical Child & Adolescent Psychology 2020, 1–13. doi: 10.1080/15374416.2020.1823847

PubMed Abstract | CrossRef Full Text | Google Scholar

Ministry of Tribal Affairs (2008). Govt. of India. Annual Report 2007-2008[EB/OL]. Available online at: https://www.india.gov.in/annual-reports-ministry-tribal-affairs-0 (accessed December 02, 2008).

Google Scholar

Mpango, R. S., Kinyanda, E., Rukundo, G. Z., Gadow, K. D., and Patel, V. (2017). Cross-cultural adaptation of the Child and Adolescent Symptom Inventory-5 (CASI-5) for use in central and south-western Uganda: the CHAKA project. Trop. Doct. 47, 347–354. doi: 10.1177/0049475517724688

PubMed Abstract | CrossRef Full Text | Google Scholar

Muller, P. A., Stage, F. K., and Kinzie, J. (2001). Science Achievement Growth Trajectories: understanding Factors Related to Gender and Racial–Ethnic Differences in Precollege Science Achievement. Am. Educ. Res. J. 38, 981–1012. doi: 10.3102/00028312038004981

CrossRef Full Text | Google Scholar

Munniksma, A., and Juvonen, J. (2012). Cross-ethnic Friendships and Sense of Social-Emotional Safety in a Multiethnic Middle School: an Exploratory Study. Merrill-Palmer Q. 58, 489–506. doi: 10.1353/mpq.2012.0023

PubMed Abstract | CrossRef Full Text | Google Scholar

Nielsen, T. R., Segers, K., Vanderaspoilden, V., Beinhoff, U., Minthon, L., Pissiota, A., et al. (2018). Validation of a European Cross-Cultural Neuropsychological Test Battery (CNTB) for evaluation of dementia. Internat. J. Geriat. Psychiatry 2018:5002. doi: 10.1002/gps.5002

PubMed Abstract | CrossRef Full Text | Google Scholar

OECD (2010). PISA 2009 results: Overcoming social background equity in learning opportunities and outcomes (volume II): Equity in learning opportunities and outcomes. Berlin: OECD Publishing.

Google Scholar

Özel, E., Cantarero-Arevalo, L., and Jacobsen, R. (2020). Vitamin D Knowledge, Attitudes, and Behaviors in Young Danish Women with a Non-Western Ethnic Minority Background—A Questionnaire Survey. International Journal of Environmental Research and Public Health 17, 8053. doi: 10.3390/ijerph17218053

PubMed Abstract | CrossRef Full Text | Google Scholar

Padilla, A. M., and Gonzalez, R. (2001). Academic Performance of Immigrant and U.S.-Born Mexican Heritage Students: effects of Schooling in Mexico and Bilingual/English Language Instruction. Am. Educ. Res. J. 38, 727–742. doi: 10.3102/00028312038003727

CrossRef Full Text | Google Scholar

Pan, Z. Y. (1982). Han yu he wei yu shu ci de bi jiao[Comparison of Numerals in Chinese and Uygur]. Ethnic Chinese 01, 53–63.

Google Scholar

Passolunghi, M. C., Cargnelutti, E., and Pellizzoni, S. (2018). The relation between cognitive and emotional factors and arithmetic problem-solving. Educ. Stud. Mathem. 2018:9863. doi: 10.1007/s10649-018-9863-y

CrossRef Full Text | Google Scholar

Peng, P., Lin, X., Ünal, Z. E., Lee, K., Namkung, J., Chow, J., et al. (2020). Examining the mutual relations between language and mathematics: a meta-analysis. Psychol. Bull. 146, 595–634. doi: 10.1037/bul0000231

PubMed Abstract | CrossRef Full Text | Google Scholar

Perie, M., Grigg, W. S., and Dion, G. S. (2005). The Nation’s Report Card: Mathematics 2005 (NCES 2006-453). U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics. Washington, DC: Government Printing Office.

Google Scholar

Pethtel, O., and Chen, Y. (2010). Cross-cultural aging in cognitive and affective components of subjective well-being. Psychol. Aging 25, 725–729. doi: 10.1037/a0018511

PubMed Abstract | CrossRef Full Text | Google Scholar

Planas, N. (2018). Language as resource: a key notion for understanding the complexity of mathematics learning. Educ. Stud. Mathem. 98, 215–229. doi: 10.1007/s10649-018-9810-y

CrossRef Full Text | Google Scholar

Potter, D., and Morris, D. S. (2016). Family and Schooling Experiences in Racial/Ethnic Academic Achievement Gaps: a Cumulative Perspective. Sociolog. Perspect. 60, 132–167. doi: 10.1177/0731121416629989

CrossRef Full Text | Google Scholar

Potthoff, S., Garnefski, N., Miklósi, M., Ubbiali, A., Domínguez-Sánchez, F. J., Martins, E. C., et al. (2016). Cognitive emotion regulation and psychopathology across cultures: a comparison between six European countries. Person. Indiv. Diff. 98, 218–224. doi: 10.1016/j.paid.2016.04.022

CrossRef Full Text | Google Scholar

Poulsen, M., and Gravgaard, A. K. D. (2016). Who Did What to Whom? The Relationship Between Syntactic Aspects of Sentence Comprehension and Text Comprehension. Scient. Stud. Read. 20, 325–338. doi: 10.1080/10888438.2016.1180695

CrossRef Full Text | Google Scholar

Powell, D. R., Son, S.-H., File, N., and Froiland, J. M. (2012). Changes in Parent Involvement Across the Transition from Public School Prekindergarten to First Grade and Children’s Academic Outcomes. Element. Sch. J. 113, 276–300. doi: 10.1086/667726

CrossRef Full Text | Google Scholar

Prediger, S., Kuzu, T., Schüler-Meyer, A., and Wagner, J. (2019). One mind, two languages – separate conceptualisations? A case study of students’ bilingual modes for dealing with language-related conceptualisations of fractions. Res. Mathem. Educ. 21, 188–207. doi: 10.1080/14794802.2019.1602561

CrossRef Full Text | Google Scholar

Priest, N., Perry, R., Ferdinand, A., Paradies, Y., and Kelaher, M. (2014). Experiences of Racism, Racial/Ethnic Attitudes, Motivated Fairness and Mental Health Outcomes Among Primary and Secondary School Students. J. Youth Adolesc. 43, 1672–1687. doi: 10.1007/s10964-014-0140-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Purpura, D. J., and Reid, E. E. (2016). Mathematics and language: individual and group differences in mathematical language skills in young children. Early Childh. Res. Q. 36, 259–268. doi: 10.1016/j.ecresq.2015.12.020

CrossRef Full Text | Google Scholar

Purpura, D. J., Schmitt, S. A., Napoli, A. R., Dobbs-Oates, J., King, Y. A., Hornburg, C. B., et al. (2021). Engaging caregivers and children in picture books: a family- implemented mathematical language intervention. J. Educ. Psychol. 113, 1338–1353. doi: 10.1037/edu0000662

CrossRef Full Text | Google Scholar

Putz, D. A., Gaulin, S. J. C., Sporter, R. J., and McBurney, D. H. (2004). Sex hormones and finger length. Evol. Hum. Behav. 25, 182–199. doi: 10.1016/j.evolhumbehav.2004.03.005

CrossRef Full Text | Google Scholar

Raccanello, D., Brondino, M., Trifiletti, E., and Shamloo, S. E. (2020). Emotions at primary school: negative achievement emotions and their relation with emotional competencies in ethnic minority and majority students. J. Comm. Appl. Soc. Psychol. 30, 441–458. doi: 10.1002/casp.2451

CrossRef Full Text | Google Scholar

Raven, J. (2000). The Raven’s progressive matrices: change and stability over culture and time. Cogn. Psychol. 41, 1–48. doi: 10.1006/cogp.1999.0735

PubMed Abstract | CrossRef Full Text | Google Scholar

Ren, Y. D., and Wei, X. M. (2018). Gai ge kai fang 40 nian lai wo guo min zu jiao yu zheng ce yan jiu de ke shi hua fen xi[A Visualized Analysis of the Research of Ethnic Education Policy in the Past 40 Years of the Reform and Opening-up in China]. J. Res. Educ. Ethnic Minorit. 05:004. doi: 10.15946/j.cnki.1001-7178.2018.05.004

CrossRef Full Text | Google Scholar

Reyna, V. F., and Brainerd, C. J. (2007). The importance of mathematics in health and human judgment: numeracy, risk communication, and medical decision making. Learn. Indiv. Diff. 17, 147–159. doi: 10.1016/j.lindif.2007.03.010

CrossRef Full Text | Google Scholar

Richter, J., Sagatun, A., Heyerdahl, S., Oppedal, B., and Røysamb, E. (2011). The Strengths and Difficulties Questionnaire (SDQ) - Self-Report. An analysis of its structure in a multiethnic urban adolescent sample. J. Child Psychol. Psychiatry 52, 1002–1011. doi: 10.1111/j.1469-7610.2011.02372.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Rupley, W. H., and Nichols, W. D. (2005). Vocabulary Instruction for the Struggling Reader. Read. Writ. Q. 21, 239–260. doi: 10.1080/10573560590949368

CrossRef Full Text | Google Scholar

Safavian, N., and Conley, A. (2016). Expectancy-Value Beliefs of Early-Adolescent Hispanic and Non-Hispanic Youth. AERA Open 2:233285841667335. doi: 10.1177/2332858416673357

CrossRef Full Text | Google Scholar

Salthouse, T. A. (1994). The nature of the influence of speed on adult age differences in cognition. Dev. Psychol. 30, 240–259.1649.30.2.240 doi: 10.1037/0012-

CrossRef Full Text | Google Scholar

Saw, G., Chang, C.-N., and Chan, H.-Y. (2018). Cross-Sectional and Longitudinal Disparities in STEM Career Aspirations at the Intersection of Gender, Race/Ethnicity, and Socioeconomic Status. Educ. Res. 47, 525–531. doi: 10.3102/0013189X18787818

CrossRef Full Text | Google Scholar

Scheidell, J. D., Thorpe, L. E., Adimora, A. A., Caniglia, E. C., Lejuez, C. W., Troxel, A. B., et al. (2020). Perceived Stress, Sexually Transmitted Infection, and Pelvic Inflammatory Disease. Sex. Trans. Dis. 2020:1232. doi: 10.1097/olq.0000000000001232

PubMed Abstract | CrossRef Full Text | Google Scholar

Schlesinger, L. B. (2010). Sex Offenders, Juvenile Offenders, and Cross-Cultural Crime Research. Internat. J. Offend. Ther. Comp. Criminol. 54, 147–149. doi: 10.1177/0306624x09354114

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmitt, S. A., Purpura, D. J., and Elicker, J. G. (2019). Predictive links among vocabulary, mathematical language, and executive functioning in preschoolers. J. Exp. Child Psychol. 180, 55–68. doi: 10.1016/j.jecp.2018.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Schneider, B., and Saw, G. (2016). Racial and Ethnic Gaps in Postsecondary Aspirations and Enrollment. Russell Sage Found. J. Soc. Sci. 2, 58–82. doi: 10.7758/RSF.2016.2.5.04

CrossRef Full Text | Google Scholar

Scott, C. M. (2009). A Case for the Sentence in Reading Comprehension. Lang. Speech Hear. Serv. Sch. 40:184. doi: 10.1044/0161-1461(2008/08-0042)

CrossRef Full Text | Google Scholar

Segers, E., and Verhoeven, L. (2016). How logical reasoning mediates the relation between lexical quality and reading comprehension. Read. Writ. 29, 577–590. doi: 10.1007/s11145-015-9613-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Serratrice, L., Genesee, F., Paradis, J., and Crago, M. B. (2006). Dual language development and disorders. A handbook on bilingualism and second language learning. Baltimore: Paul Brookes Publishing, 2004. ISBN 1-55766-686-5, Pbk, pp. 233. J. Child Lang. 33:899. doi: 10.1017/S0305000906217823

CrossRef Full Text | Google Scholar

Shepard, R. N., and Metzler, J. (1971). Mental Rotation of Three-Dimensional Objects. Science 171, 701–703. doi: 10.1126/science.171.3972.701

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, L. (2018). Jing zhun fu pin shi jiao xia shao shu min zu di qu guo jia tong yong yu yan wen zi pu ji shen hua de ce lue[Strategies for deepening the popularization of national common language in ethnic minority areas from the perspective of targeted poverty alleviation]. Soc. Sci. 2018, 150–156.

Google Scholar

Shin, Y. (2012). Do Black Children Benefit More From Small Classes? Multivariate Instrumental Variable Estimators With Ignorable Missing Data. J. Educ. Behav. Statist. 37, 543–574. doi: 10.3102/1076998611431083

CrossRef Full Text | Google Scholar

Simmons, F. R., and Singleton, C. (2008). Do weak phonological representations impact on arithmetic development? A review of research into arithmetic and dyslexia. Dyslexia 14, 77–94. doi: 10.1002/dys.341

PubMed Abstract | CrossRef Full Text | Google Scholar

Singer, V., and Strasser, K. (2017). The Association Between Arithmetic and Reading Performance in School: a Meta-Analytic Study. Sch. Psychol. Q. 2017:197. doi: 10.1037/spq0000197

PubMed Abstract | CrossRef Full Text | Google Scholar

Singer, V., Strasser, K., and Cuadro, A. (2019). Direct and indirect paths from linguistic skills to arithmetic school performance. J. Educ. Psychol. 111, 434–445. doi: 10.1037/edu0000290

CrossRef Full Text | Google Scholar

So, D., and Siegel, L. S. (1997). Learning to read Chinese: Semantic, syntactic, phonological and working memory skills in normally achieving and poor Chinese readers. Read. Writ. 9, 1–21. doi: 10.1023/A:1007963513853

CrossRef Full Text | Google Scholar

Sonnenschein, S., and Sun, S. (2016). Racial/ethnic differences in kindergartners’ reading and math skills: parents’ knowledge of children’s development and home-based activities as mediators. Infant Child Dev. 26:5. doi: 10.1002/icd.2010

CrossRef Full Text | Google Scholar

Sørlie, T., Hansen, K. L., and Friborg, O. (2018). Do Norwegian Sami and non-indigenous individuals understand questions about mental health similarly? A SAMINOR 2 study. Internat. J. Circump. Health 77:1481325. doi: 10.1080/22423982.2018.1481325

PubMed Abstract | CrossRef Full Text | Google Scholar

Spörlein, C., and Schlueter, E. (2018). How education systems shape cross-national ethnic inequality in math competence scores: moving beyond mean differences. PLoS One 13:e0193738. doi: 10.1371/journal.pone.0193738

PubMed Abstract | CrossRef Full Text | Google Scholar

Srirangarajan, T., Oshio, A., Yamaguchi, A., and Akutsu, S. (2020). Cross-Cultural Nomological Network of Gratitude: findings From Midlife in the United States (MIDUS) and Japan (MIDJA). Front. Psychol. 11:571. doi: 10.3389/fpsyg.2020.00571

PubMed Abstract | CrossRef Full Text | Google Scholar

Stark, T. H., Mäs, M., and Flache, A. (2015). Liking and disliking minority-group classmates: explaining the mixed findings for the influence of ethnic classroom composition on interethnic attitudes. Soc. Sci. Res. 50, 164–176. doi: 10.1016/j.ssresearch.2014.11.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Susino, M., and Schubert, E. (2016). Cross-cultural anger communication in music: towards a stereotype theory of emotion in music. Musicae Scientiae 21, 60–74. doi: 10.1177/1029864916637641

CrossRef Full Text | Google Scholar

Taylor, P. (2007). Poor policies, wealthy peasants: alternative trajectories of rural development in Vietnam. J. Viet. Stud. 2, 3–56. doi: 10.1525/vs.2007.2.2.3

CrossRef Full Text | Google Scholar

Träff, U., Olsson, L., Skagerlund, K., and Östergren, R. (2018). Cognitive mechanisms underlying third graders’ arithmetic skills: expanding the pathways to mathematics model. J. Exp. Child Psychol. 167, 369–387. doi: 10.1016/j.jecp.2017.11.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Tran, N. T. (2013). Original research: Factors associated with low educational motivation among ethnic minority students in Vietnam. Ritsumeik. J. Asia Pacif. Stud. 32, 124–136.

Google Scholar

Trieu, Q., and Jayakody, R. (2018). Ethnic Minority Educational Success: Understanding Accomplishments in Challenging Settings. Soc. Indic. Res. 15:2. doi: 10.1007/s11205-018-1900-9

CrossRef Full Text | Google Scholar

Triventi, M., Vlach, E., and Pini, E. (2021). Understanding why immigrant children underperform: evidence from Italian compulsory education. Journal of Ethnic and Migration Studies 2021, 1–23. doi: 10.1080/1369183x.2021.1935656

CrossRef Full Text | Google Scholar

Tsung, L. T. H., and Cruickshank, K. (2009). Mother tongue and bilingual minority education in China. Internat. J. Biling. Educ. Biling. 12, 549–563. doi: 10.1080/13670050802209871

CrossRef Full Text | Google Scholar

Ungvary, S., McDonald, K. L., and Benish-Weisman, M. (2017). Identifying and Distinguishing Value Profiles in American and Israeli Adolescents. J. Res. Adolesc. 28, 294–309. doi: 10.1111/jora.12330

PubMed Abstract | CrossRef Full Text | Google Scholar

Vander Zanden, T. W. (1983). American Minority Relations. New York, NY: Knopf.

Google Scholar

Villeneuve, E. F., Hajovsky, D. B., Mason, B. A., and Lewno, B. M. (2019). Cognitive ability and math computation developmental relations with math problem solving: an integrated, multigroup approach. Sch. Psychol. Q. 34, 96–108. doi: 10.1037/spq0000267

PubMed Abstract | CrossRef Full Text | Google Scholar

Vukovic, R. K., and Lesaux, N. K. (2013). The language of mathematics: Investigating the ways language counts for children’s mathematical development. J. Exp. Child Psychol. 115, 227–244. doi: 10.1016/j.jecp.2013.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J. L., and Yang, L. H. (2019). Yin du shao shu min zu jiao yu zhi li jin cheng te zheng ji qi shi[Education governance of ethnic minorities in India: process, characteristics and enlightenment]. Research of higher education for nationalities 7, 1–10.

Google Scholar

Wang, L., Sun, Y., and Zhou, X. (2016). Relation between approximate number system acuity and mathematical achievement: the influence of fluency. Front. Psychol. 7:1966. doi: 10.3389/fpsyg.2016.01966

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., Ye, X., and Deng, C. (2020). Exploring mechanisms of rapid automatized naming to arithmetic skills in Chinese primary schoolers. Psychol. Sch. 2020:22349. doi: 10.1002/pits.22349

CrossRef Full Text | Google Scholar

Wilkinson, L. C. (2018). Learning language and mathematics: a perspective from Linguistics and Education. Linguist. Educ. 2018:005. doi: 10.1016/j.linged.2018.03.005

CrossRef Full Text | Google Scholar

Wilson, C. A., Plouffe, R. A., Saklofske, D. H., Di Fabio, A., Prince-Embury, S., and Babcock, S. E. (2017). Resiliency Across Cultures: a Validation of the Resiliency Scale for Young Adults. J. Psychoeduc. Assess. 2017:073428291774001. doi: 10.1177/0734282917740017

CrossRef Full Text | Google Scholar

Wilson, D. K., Lawman, H. G., Segal, M., and Chappell, S. (2011). Neighborhood and Parental Supports for Physical Activity in Minority Adolescents. Am. J. Prev. Med. 41, 399–406. doi: 10.1016/j.amepre.2011.06.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Wong, D. F. K., Cheng, C.-W., Zhuang, X. Y., Ng, T. K., Pan, S.-M., He, X., et al. (2017). Comparing the mental health literacy of Chinese people in Australia, China, Hong Kong and Taiwan: Implications for mental health promotion. Psychiatry Res. 256, 258–266. doi: 10.1016/j.psychres.2017.06.032

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, W.-Y., and Bodigerel-Koehler, M. (2013). The mediating effects of cross-cultural dynamic competencies on the relationship between multicultural personality and cross-cultural adjustment. Internat. J. Hum. Resour. Manag. 24, 4026–4045. doi: 10.1080/09585192.2013.781518

CrossRef Full Text | Google Scholar

Xi, J. H., and Zhang, S. Z. (2008). Bu tong min zu xue sheng shu xue xue xi xin li cha yi tan xi[Psychological Differences in mathematics learning among Students of different ethnic groups]. Health Vocat. Educ. 17, 109–111.

Google Scholar

Yan, W., and Lin, Q. (2005). Parent Involvement and Mathematics Achievement: contrast Across Racial and Ethnic Groups. J. Educ. Res. 99, 116–127. doi: 10.3200/joer.99.2.116

CrossRef Full Text | Google Scholar

Yang, H. (2009). Yin du shao shu min zu jiao yu yan jiu[Research on the Education of Indian Minorities]. Res. Ethnic Educ. 20, 114–119.

Google Scholar

Yang, J. (2015). Xin jiang yu yan ban shao shu min zu xue sheng shu xue xue xi kun nan de yuan yin ji dui ce yan jiu[Research on the Causes and Countermeasures of Mathematics Learning Difficulties of Ethnic Minority Students in Xinjiang Chinese Language Class]. Xinjiang Normal Univ. 2015:2015.

Google Scholar

Yang, Y., Wang, H., Zhang, L., Sylvia, S., Luo, R., Shi, Y., et al. (2015). The Han-Minority Achievement Gap, Language, and Returns to Schools in Rural China. Econ. Dev. Cult. Change 63, 319–359. doi: 10.1086/679070

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, L. J. (2012). Nei meng gu shao shu min zu da xue sheng han yu wen yue du jiao xue de ji dian gai jin[Some Improvements of Chinese Reading Teaching for Inner Mongolia Minority College Students]. J. Inner Mong. Norm. Univ. 41, 124–126.

Google Scholar

Yun, H.-J., and Cui, M. (2019). The Effects of Parental Warmth on Adolescent Delinquency in the United States and South Korea: a Cross-Cultural Perspective. J. Youth Adolesc. 2019:1078. doi: 10.1007/s10964-019-01078-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Zeng, J. X., Long, W. J., Pang, X. P., Shi, Y. J., Luo, R. F., and Zhang, L. X. (2015). Xi bu shao shu min zu pin kun di qu nong cun xiao xue jiao yu min zu cha yi yan jiu jian kang shi jiao[A Study on Ethnic Discrepancies in Rural Primary Education in Poor Ethnic Minority Areas in the West-From the Perspective of Health]. J. China Execut. Leadership Acad. Yan’an 03, 111–117. doi: 10.16223/j.cnki.cn61-1456/c.2015.03.018

CrossRef Full Text | Google Scholar

Zhang, J., and Gao, Q. Z. (2016). Han yu shou ke dui wei wu er zu xue sheng shu xue xue xi ying xiang de an li fen xi[A case study of the influence of Chinese teaching on Mathematics learning of Uyghur Students]. Sci. Trib. 26, 87–88. doi: 10.16400/j.cnki.kjdkz.2016.09.043

CrossRef Full Text | Google Scholar

Zhong, Z. Y. (2013). Min zu di qu chu zhong sheng shu xue xue ye cheng ji yu yu yan guan xi de shi zheng yan jiu yi meng gu zu xue sheng wei li[An Empirical Study on the Relationship between Mathematics Academic Achievement and Language of Junior Middle School Students in Minority Areas – A Case Study of Mongolian Students]. Ethnic Educ. Res. 24, 80–84.

Google Scholar

Zhou, P., Critchley, H., Garfinkel, S., and Gao, Y. (2021). The conceptualization of emotions across cultures: a model based on interoceptive neuroscience. Neurosci. Biobehav. Rev. 125, 314–327. doi: 10.1016/j.neubiorev.2021.02.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, X., Wei, W., Zhang, Y., Cui, J., and Chen, C. (2015). Visual perception can account for the close relation between numerosity processing and computational fluency. Front. Psychol. 6:1364. doi: 10.3389/fpsyg.2015.01364

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: ethnic minority, ethnic difference, mathematical achievement, language ability, mathematical cognition

Citation: Cui J, Lv L, Du H, Cui Z and Zhou X (2022) Language Ability Accounts for Ethnic Difference in Mathematics Achievement. Front. Psychol. 13:929719. doi: 10.3389/fpsyg.2022.929719

Received: 27 April 2022; Accepted: 22 June 2022;
Published: 22 July 2022.

Edited by:

Ann Dowker, University of Oxford, United Kingdom

Reviewed by:

Violeta Pina, University of Granada, Spain
Sheyla Blumen, Pontifical Catholic University of Peru, Peru

Copyright © 2022 Cui, Lv, Du, Cui and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhanling Cui, myselfczl@163.com; Xinlin Zhou, zhou_xinlin@bnu.edu.cn

These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.