Huyen-Trang Luu-Thi
Thuy-Trinh Ngo-Thi*
My-Tien Nguyen-Thi
Thanh Thao-Ly
Bao-Tran Nguyen-Duong
Vinh-Long Tran-Chi- Faculty of Psychology, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam
Increasing numbers of students around the world are suffering from mathematics anxiety. The main objective of this study is to investigate the relationship between mathematics anxiety and gender, grade, career choices, and academic achievement in Grade 10, 11, and 12 students. This study used the Revised Version of the Mathematics Anxiety Rating Scale to survey 1,548 high school students (570 males and 978 females) from high schools in Vietnam. A multivariate analysis of variance (MANOVA) test, Pearson correlation and multiple linear regression were used to analyze data. The results show that there are significant differences in the influence of grade, academic achievement, and students’ career choices on mathematics anxiety. Academic coping strategies, gender, grade, and career choices are significant predictors of mathematics anxiety. Grade 12 students have higher levels of mathematics anxiety than others. Students with high average mathematics scores (9.0–10.0) have higher levels of mathematics anxiety than students with lower scores. Besides, students choosing finance and economics or industrial engineering to pursue into higher education also experienced higher levels of mathematics anxiety than others. This study contributes to the general discussion about the nature of mathematics anxiety and the relationship between mathematics anxiety and academic achievement.
1 Introduction
Industrial Revolution 4.0 has had a considerable influence on many aspects of society, including not only economic development (optimising production processes through digital and intelligent technologies) but also the transformation of education. The economy now demands new management systems and improvements in the skills of the whole population, especially students. Thus, education in Vietnam has been transformed under the Education 4.0 initiative to deliver high quality, appropriately skilled human resources to society. This transformation has introduced curricula with complex academic content that requires cognitive flexibility, critical thinking, and creativity. These requirements create numerous challenges in the learning process, including the availability of appropriate material resources and the effects of environmental conditions and, especially, psychological conditions. The resultant academic pressure, stress and anxiety cause loss of energy among students, as well as reduced motivation and difficulty concentrating in class.
Wiedemann (2015) suggested that anxiety is characterised by nervousness and fear, with physical manifestations including tremors, increased pulse rate, and muscle pain. Anxiety is a basic human emotion that generates physiological reactions to stressors when a “fight or flight” defence response is activated in order to protect the individual from a threatening object or phenomenon (Sue et al., 2015).
In the Vietnam context, Mathematics is an important and compulsory subject for all students in the educational system from primary to high school. At each different grade level, the knowledge of mathematics is continuously improved. Therefore, the pressure easily comes when students have to acquire a large amount of mathematics knowledge, take and prepare for mathematics exams. Specifically, mathematics is a compulsory subject in entrance and graduation exams. These have increased more and more pressure on high school students when they have to tackle mathematics problems in the classroom, have a good performance in mathematics and prepare for the school graduation exams at the same time. For these reasons, the authors were interested in mathematics anxiety levels of high school students in Vietnam. Mathematics anxiety includes tension and emotions that affect the ability to manipulate numbers and tackle issues associated with mathematics in day-to-day situations (Richardson and Suinn, 1972). McAnallen (2010) suggested that mathematics anxiety is a response associated with avoiding mathematics that leads to a failure to gain critical skills and, ultimately, to make appropriate decisions on career orientation.
Specific research questions posed of this study:
1) What do factors affect the levels of mathematics anxiety among high school students?
2) How do academic coping strategies influence high school students’ anxiety?
Male and females have differences in the substantial aspects of life including the attraction to the mathematics domain (Keshavarzi and Ahmadi, 2013); spatial processing ability (Maloney and Beilock, 2012); stress reactivity (Altemus et al., 2014) and social stereotypes about women and mathematical abilities (Beilock et al., 2007). Therefore, gender difference became the first variable that the authors were interested in examining. Besides, each student has the ability to learn maths and mathematics knowledge absorption differently from others, which leads to differential academic pressure on high school students. Accordingly, their academic achievements and performances in maths are not the same. This proves that mathematics average score should be the following variable affecting the levels of mathematics anxiety which the authors need to be researched on.
Studies investigating the level of mathematics anxiety in high school students have revealed contradictory findings about gender differences in the relationship between mathematics anxiety and academic performance. Keshavarzi and Ahmadi (2013) studied 834 high school students in Iran and reported a non-significant difference between the genders in mathematics anxiety. Another scientific study on mathematics test anxiety and numerical anxiety surveying the mathematics achievement of 140 pre-university students reported that males and females suffer the same level of mathematics anxiety. Akbayir (2019) also found that there is no gender difference in mathematics anxiety. Nevertheless, recent studies have shown a gender difference in the levels of mathematics anxiety. The study by Lew and Hwang (2019) on 459 high school students (124 male and 335 female) from four high schools in Seoul, Korea, showed that mathematics anxiety is significantly higher among female students than male students. The results of Lew and Hwang’s study are in agreement with several prior studies. In research on mathematics anxiety in China with students from Grade 7 to Grade 12, Luo et al. (2009) found that females suffer from a greater degree of mathematics anxiety than males. A similar conclusion was reached by Karimi and Venkatesan (2009) who conducted research on 284 Grade 10 students in Karnataka state, India, and found differences in mathematics anxiety according to gender. This finding is also supported by a study by Maloney and Beilock (2012), which showed there are gender differences in mathematics-associated competencies and that females have higher levels of mathematics anxiety. In addition, Taylor and Fraser (2013) suggested that girls experience higher anxiety levels about mathematics evaluation than boys in mathematics learning.
Several scientific studies have focused on the negative effects of mathematics anxiety. Puteh and Khalin (2016) found a negative relationship between mathematics anxiety and mathematics achievement based on the results of mid-term examinations. Zakaria et al. (2012) showed that there is a correlation between academic achievement and mathematics anxiety; the higher the anxiety score, the poorer the academic achievement. According to the authors, the reason for this finding could be a relationship between students’ comprehension of mathematics, and their confidence. A study by Karimi and Venkatesan (2009) on 284 high school students showed that there is a statistically negative relationship between mathematics anxiety and mathematics scores. This finding supports the prior study of Woodard (2002), which revealed that students with a high level of mathematics anxiety attain lower mathematics scores than those with a low level of mathematics anxiety. This result implies that students with high levels of mathematics anxiety perform less well in mathematics. Similarly, Lew and Hwang (2019) found that mathematics anxiety is negatively associated with academic achievement. Likewise, in a study involving 514 high school students, Milovanović (2020) showed that there is a significant negative correlation between students’ average grades in mathematics and mathematics anxiety.
In the Vietnam context, each grade level has different mathematical problems. In grade 10 and grade 11, students have many pressures and difficulties in adapting new mathematics concepts, principles and formulas, which leads to worries and concerns among these students. In the last year of high school, grade 12, students have to make a decision of future career orientation and attempt to study for examination entrance. Moreover, maths is the crucial requirement of each ology in university, consequently, students in this period of time have more considerable anxiety than others. Therefore, the authors examine the relationship between grade levels variable and mathematics anxiety levels. The effects of grade level (school year) on students’ mathematics anxiety have been examined by numerous educators and researchers. The study by Luo et al. (2009) of Grades 7 to 12 in China did not find evidence of statistically significant differences in mathematics anxiety between students but did find that the mean level of anxiety differed according to grade. Specifically, the level of mathematics anxiety was found to rise steadily from Grade 7 to 9, with a greater proportion of students in Grade 9 than in other years experiencing the highest anxiety level. The results showed a considerable decline in the levels of anxiety from Grade 9 to 10, but anxiety then peaked again in Grade 11 before declining again in Grade 12. Jackson and Leffingwell (1999) found a different pattern, with mathematics anxiety beginning in elementary school and growing through high school and the first year of college. Lew and Hwang (2019) found that levels of mathematics anxiety in Grade 11 students are the highest and in Grade 12 students the lowest. Likewise, with data collected from 1,352 Grade 8 to 11 students in Bahrain, Al Mutawah (2015) found that Grade 11 students suffer greater mathematics anxiety than other students.
Further studies mention other factors affecting the levels of mathematics anxiety among students. Mathematics anxiety causes students to tend to quit mathematics courses (specifically advanced mathematics courses) and adopt pessimistic attitudes towards activities that include mathematics, avoiding degree subjects and occupations that require quantitative skills (Hembree, 1990; Ma, 1999; Ho et al., 2000). Yüksel-Şahin (2008) surveyed Grade 4 and 5 students and concluded that individuals who have positive feelings about their mathematics classes experience lower levels of mathematics anxiety. Similarly, students who are positive about their mathematics teacher experience significantly lower levels of mathematics anxiety. In addition, Yüksel-Şahin (2008) suggested that students with higher levels of accomplishment in learning mathematics have lower levels of mathematics anxiety. Bursal and Paznokas (2006) found that negative experiences in school can increase the level of mathematics anxiety; criticism and intimidation by teachers might create an intimidating classroom atmosphere in which students are uncertain about asking or responding to questions. Teachers who punish students by giving mathematics assignments create negative attitudes towards mathematics (Oberlin, 1982). Mathematics anxiety could be brought about by low scores or failure in mathematics (Ma and Xu, 2004), leading to extremely negative feelings about the subject. Given the problems related to academic achievement and performance, it is essential to conduct further scientific research on students’ anxiety in general, and mathematics anxiety in particular.
Factors affecting students’ career choices have been extensively investigated by researchers. Authors suggest that there are several highly influential factors, such as self-efficacy appraisals of competencies in attaining performance, outcome expectations and desired objectives (Lent et al., 1994), gender role stereotypes (Singer and Stake, 1986), parental attitude (Mau et al., 1995), and social encouragement from friends (Lent et al., 2002).
However, few studies have investigated the influence of mathematics anxiety on students’ career choices. Ashcraft (2002), and Ashcraft and Kirk (2001), suggested that mathematics anxiety directly and indirectly affects engagement in courses related to mathematics, attitudes towards mathematics, and career orientation. Anxiety affects the selection of degree courses that depend on the manipulation of numbers or include topics associated with mathematics (Hackett, 1985). Anxiety can also lead to refusal to participate in mathematics classes and avoidance of careers in finance and other fields related to mathematics (Ma, 1999; Kyttälä and Björn, 2010). These findings demonstrate consistency with studies conducted by Betz (1978), and Zettle and Houghton (1998), who suggested that students suffering from mathematics anxiety are more likely to avoid classes and careers associated with mathematics. Specifically, mathematics anxiety could reduce confidence in students’ ability to complete mathematics-related tasks and limit students’career choices (Espino et al., 2017).
In the academic environment, although every student has the same learning environment, the same syllabus and so on, all of the students have different academic achievement and performance. This indicates that each high school student has their own coping strategies. Owing to this reason, the authors were interested in coping strategies and conducted this study to examine the effects of coping strategies on mathematics anxiety. The concept of coping strategies has been described in many previous studies. These strategies relate to cognitive and behavioural approaches to alleviating negative emotions that derive from difficult situations and stressful events, which could include academic failure, failure to complete tasks, and performing badly in tests (Lazarus, 2013; Ader and Erktin, 2010). Previous studies on academic coping relate to how students deal with academic challenges, obstacles, and difficulties; the findings of Krypel and Henderson-King (2010) show that these efforts can have either positive or negative aspects. Recent studies (Friedel et al., 2007) have indicated that coping strategies are divided into two main categories: adaptive coping strategies with positive outcomes, and maladaptive coping strategies with negative outcomes. Students with adaptive coping strategies tend to identify and analyse their own mistakes and reframe situations in a positive light. On the other hand, students with maladaptive coping strategies tend to blame their problems on everyone else and ignore their mistakes. Several instruments are available to assess students’ coping strategies in academic contexts. The items in the positive coping group are used to measure adaptive strategies when students have poor experiences with mathematics, find their mistakes and improve their performance. Conversely, items in the projective, denial, and non-coping groups measure maladaptive strategies in terms of feelings, thoughts and behaviours when students face difficult problems in school. With projective coping, students tend to blame others for their problems, especially teachers. With denial coping, students ignore their own failures and crucial negative events in mathematics. With non-coping, students tend to blame and criticise themselves for their failures and worry about what others think of their mistakes (Friedel et al., 2007). Skaalvik (2018) found that use of coping strategies among students when dealing with mathematics is significantly predictive of mathematics anxiety. Students who have problem-focused coping strategies, finding out their mistakes and resolving to do better next time, are negatively associated with mathematics anxiety.
In Vietnam, anxiety among students is seen as an important topic. Thanh Tra (2019) conducted scientific research on levels of anxiety with 500 university and college students in Ho Chi Minh City and reported a high level of anxiety, identifying factors that have a negative impact on the mental health of students. In addition, Van (2018) showed that a large number of high school students in Ho Chi Minh City suffer from academic anxiety caused by false beliefs and cognitive distortion of themselves, problems in their relationships with teachers, and academic pressure from their parents.
Increasing numbers of students around the world are suffering from mathematics anxiety, which has negative effects on learning outcomes and academic achievement. It also creates obstacles in the learning process. This current research is conducted to examine mathematics anxiety among Vietnamese high school students and test the correlation between students’ mathematics anxiety and academic coping strategies.
The research starts by reviewing the literature on mathematics anxiety in high school students. A research methodology is presented in the second section. In the following sections, the research results and discussion are introduced. Research on mathematics anxiety could make a major contribution to preventing anxiety among high school students, protecting and enhancing students’ mental health during the learning process.
The first hypothesis is that students in Grade 12 have higher levels of mathematics anxiety than others. The second hypothesis is that high school students choosing to pursue medicine at a higher education level have greater levels of mathematics anxiety than pedagogy, finance-economics, social sciences, natural sciences, engineering industry, information technology, transportation sectors and others. The third hypothesis is that female high school students suffer more mathematics anxiety than male students. The fourth hypothesis is that students in Grade 11 and Grade 12 attaining an average mathematics score 9.0–10.0 experienced higher levels of mathematics anxiety than others from the same school years. The fifth hypothesis is that positive coping strategies would be positively correlated with mathematics anxiety. The final hypothesis is that average mathematics score would be positively correlated with the levels of mathematics anxiety.
2 Materials and Methods
2.1 Participants
A recruitment process was undertaken to ensure that all the high school students participating in the research did so voluntarily. The sampling frames for the respondents were obtained from nine high schools in two city regions and three provinces in Vietnam. A total of 1,691 questionnaires were distributed, all of which were returned. The authors eliminated 143 responses that were incomplete or contained insufficient information; the final sample size was 1,548. The sample consisted of 513 10th graders (33.1%), 462 11th graders (29.8%), and 573 12th graders (37.0%); with 570 males (36.8%) and 978 females (63.2%). An overview of the socio-demographic characteristics of subjects is shown in Table 1.
TABLE 1. Socio-demographic characteristics of subjects (N = 1,548).
2.2 Instrument and Procedures
2.2.1 Instrument
RMARS is a revised version of the original scale devised by Richardson and Suinn (1972); the measurement has 24 items and includes two aspects of mathematics anxiety measured by two subscales:
1) LMA is measured by 16 items and examines the anxiety caused by activities or processes of learning mathematics or statistics (a sample item is: “watching a teacher work an algebraic equation on the blackboard”).
2) MEA is measured by 8 items and examines the anxiety caused by the assessment of mathematics or statistical learning process (a sample item is: “waiting to get a mathematics test returned in which you expected to do well”).
RMARS is based on a 5-point Likert scale, which was used for all items, ranging from one to five (1 = never; 2 = rarely; 3 = sometimes; 4 = regularly; 5 = always). Guidance for participants was provided at the top of the form.
Malhotra and Birks (2007) reported a method for ranking discrete values, as follows: (Maximum—Minimum)/n = (5−1)/5 = 0.8. Based on this method, the rankings used were never (1.00–1.80), rarely (1.81–2.60), sometimes (2.61–3.40), regularly (3.41–4.20) and always (4.21–5.00). In our total sample, Cronbach’s alpha was 0.788. This value shows that the scale is significant and reliable.
The ACI was developed by Tero and Connell (1984) to assess students’ coping strategies in academic contexts. The measurement is based on 13 items addressing four academic coping strategies: 1) positive coping comprises three items measuring adaptive strategies when students have poor experiences with mathematics; 2) projective coping comprises three items rating the extent to which students blame others for their problems; 3) denial coping comprises three items examining whether students downplay the importance of negative events or ignore failures in mathematics; and 4) non-coping comprises four items assessing whether students blame themselves for their failures in mathematics or worry about what others think of their mistakes. In our sample, Cronbach’s alpha was 0.647, indicating that the scale is significant and reliable.
2.2.2 Procedures
An informed consent process was used and participation was completely voluntary; respondents could withdraw at any time and would not be penalised. For the questionnaires, the participants were informed of the research aims and asked to provide their age, gender, school, average mathematics score, residence location, and career choices. The students then completed the self-report information, with researchers present to help the students and ensure that the questionnaires were completed correctly. It took 3 months to collect the data as part of an 8-month research project, from October 2020 to May 2021.
The 24-item RMARS was translated for Vietnamese students. Firstly, we translated and validated the scale with permission from the author to use it in our research project. We then invited a native speaker of Vietnamese, who is fluent in English, familiar with the culture, and has a research background with experience of translation, to help with the translation exercise.
A Vietnamese native speaker translator created a forward translation of the scale. Then, the initial Vietnamese translation was reconciled by all members of the research group to agree the ultimate translation for backward translation.
The back-translation from Vietnamese to English was conducted by a professional translator (native speaker of English and fluent in Vietnamese) with no information on the initial scale. Ultimately, the analysis group compared the backward translation to the original to determine whether there were any significant differences or discrepancies.
Finally, further verification of the 24-item RMARS was performed with a group of 20 people to ensure there were no issues in the interpretation of the scale. After investigating the results of applying the scale, no problems arose and the final Vietnamese version of RMARS was formally adopted.
2.2.3 Data Analysis
A multivariate analysis of variance (MANOVA) test used to determine significant differences between mathematics anxiety and the independent variables (gender, grade, career orientation, and academic achievement). Pearson correlation was conducted to establish the relationship between mathematics anxiety, academic coping strategies, and independent variables. Multiple linear regression analysis was used to examine the relationship between the predictor variables (academic coping strategies, gender, grade) and the dependent variable (mathematics anxiety).
3 Result
3.1 Factorial Validity
The data were analyzed by using Analysis of Moment Structures (AMOS) version 20.0. The two-factor model of the 24-item RMARS, which included the LMA and MEA subscales, was evaluated using confirmatory factor analysis (CFA) (Plake and Parker, 1982) with the most widely used model fit indices:
• RMSEA (root mean square error of approximation). A value of RMSEA ≤0.08 shows a good fit and between 0.08 and 0.10 provides a mediocre fit (MacCallum et al., 1996). The RMSEA value of 0.05 indicates a good fit, RMSEA 90% confidence interval (0.047, 0.053), p < 0.001.
• GFI (goodness-of-fit index). A value of GFI ≥0.90 is considered a good model fit and GFI ≥0.95 was considered a well fit (Hair et al., 2018). In the present research, the GFI value was 0.94, which was higher than 0.90 and could be considered a good model fit.
• CFI (comparative fit index). The CFI value was 0.868, which is smaller than the standard value of 0.90 and this was not acceptable.
• TLI (Tucker-Lewis index). A value of TLI ≥0.90 is a good model fit and ≥0.95 is a well-fit (Bentler and Bonett, 1980; Sharma et al., 2005). The TLI value was 0.841, which was not fit.
The appropriate fit indices RMSEA and GFI imply that the two-factor model is a good fit with the data. The measurement model should meet the criteria for the goodness of fit so that further analysis can be undertaken (Halim et al., 2018).
CMIN/DF is a calculation of the chi-square (χ2) value divided by the degree of freedom. A value of CMIN/DF ≤ 2.00 shows a good model fit and CMIN/DF ≤ 5.00 shows an acceptable fit (Hair et al., 2018). CMIN/DF value was 4.815 and could be considered an acceptable fit.
3.2 Construct Validity
The value of Average Variance Extracted (AVE) was 0.151, which was under 0.5. Therefore, in the case of AVE was less than 0.5 but the composite reliability value in this study was 0.784, which was higher than 0.6, the convergent validity was still adequate (Fornell and Larcker, 1981). Discriminant validity was acceptable by Fornell and Larcker (1981) criterion. To evaluate discriminant validity, the AVE may be compared with the square of the correlations among the latent variables (Chin, 1998). The Average Variance Extracted (AVE) estimated for the two factors (LMA & MEA) was 0.162 (LMA) and 0.129 (MEA), which were lower than the square of the correlation between the two factors to provide evidence of discriminant validity which was 0.759.
3.3 Reliability
With a two-factor model of the 24-item RMARS, the study assessed the internal consistency of subscales using Cronbach’s Alpha (Cronbach, 1951) and Composite Reliability (Wasko and Faraj, 2005). The values of Cronbach’s alpha in this study were 0.81 (LMA) and 0.67 (MEA). Although Cronbach’s alpha should be higher than 0.70 to ensure the reliability of the scale, values below 0.70 are still acceptable (Hair et al., 2007; Taber, 2018). The CR value of LMA was 0.725, which was higher than the recommended value of 0.7 (Fornell and Larcker, 1981) and the CR value of MEA was 0.505, which was lower than the recommended value. The CR of the 24-item RMARS was 0.784. Therefore, the variables in this study were considered reliable.
3.4 Descriptive Analysis
According to the norms for RMARS (Plake and Parker, 1982), the participants scored in the average range on the mathematics anxiety scale, with a mean of 3.34 (SD = 0.47) for the LMA subscale and 3.34 (SD = 0.52) for the MEA subscale. Table 2 shows descriptive statistics of the dependent variables, including LMA and MEA results by gender and grade level groups.
TABLE 2. Descriptive statistics of mathematics anxiety.
3.5 Inferential Analysis
A MANOVA was performed with gender, grade, career choices and average mathematics score as independent variables, and the LMA and MEA subscales as dependent variables. To run MANOVA, the researchers conducted a preliminary analysis to examine the absence of multicollinearity and homogeneity of covariance matrices. Pearson correlations were performed between two dependent variables to test the MANOVA assumption that dependent variables would be moderately correlated with each other (Meyers et al., 2016). The correlation value r should not be greater than 0.90 (Tabachnick et al., 2007). As can be seen in Table 3, a meaningful pattern of correlations was observed amongst the two dependent variables, suggesting the appropriateness of using MANOVA.
TABLE 3. Correlation between LMA and MEA.
The multivariate homogeneity of covariance matrices was examined using Box’s M test; the M value of 861.630 was not significant (p < 0.001), hence the assumption of homogeneity of covariance matrices was not met. As a result, Pillai’s trace value, a more robust statistic, was used to confirm the result.
A separate two-way univariate analysis of variance (ANOVA) for each of the dependent variables was conducted (Table 4). Levene’s test of equality of error variances was used to test the assumption from the MANOVA and ANOVA that the variances for each variable are equal across the groups. If Levene’s test is significant, this means that the assumption has not been satisfied. In this study, the value of Levene’s test came out to be significant for all the variables in the LMA subscale [F (278, 1269) = 1.68, p < 0.05], and MEA subscales [F (278, 1269) = 1.459, p < 0.05]. Therefore, when the follow-up ANOVAs were conducted, the results for LMA and MEA were interpreted with caution. A test of the standard deviations reported that none of the largest standard deviations was more than four times the size of the corresponding smallest, suggesting that the ANOVA was robust (Howell, 2007).
TABLE 4. The mean scores of students in MEA.
There was a significant difference in the level of mathematics anxiety between Grades 10, 11 and 12 when jointly considering LMA and MEA variables, Pillai’s trace value = 0.01; F(4, 2538) = 3.051, p = 0.016, partial η2 = 0.005. Therefore, the results suggest that the first hypothesis (H1) should be rejected. A separate ANOVA was conducted for each dependent variable, with each ANOVA evaluated at an alpha level of 0.025 (that is, 0.05/2). There was a significant difference between Grades 10, 11 and 12 for MEA; F(2, 1269) = 3.861, p = 0.021, partial η2 = 0.006, with Grade 12 (M = 3.34, SD = 0.03) scoring higher than Grade 11 (M = 3.23, SD = 0.03) and Grade 10 (M = 3.29, SD = 0.03). There was no significant difference between Grades 10, 11 and 12 for LMA; F(2, 1269) = 3.121, p = 0.044, partial η2 = 0.005.
There was a significant difference in the level of mathematics anxiety according to career choice; Pillai’s trace = 0.211, F(16, 2538) = 18.708, p = 0.000, partial η2 = 0.105. Therefore, the results suggest that the second hypothesis (H2) should be rejected. A separate ANOVA was conducted for each dependent variable. There was a significant difference between students’ career choices in terms of LMA; F(8, 1269) = 36.760, p = 0.000, partial η2 = 0.188. Students choosing finance-economics (M = 3.53, SD = 0.03), or industrial engineering (M = 3.53, SD = 0.04) experienced higher levels of LMA than others. There was a significant difference between students’ career choices on MEA; F(8, 1269) = 14.379, p = 0.000, partial η2 = 0.083. Students choosing finance-economics (M = 3.50, SD = 0.04) experienced higher levels of MEA than others.
There was no significant difference in the level of mathematics anxiety between male and female students in terms of LMA and MEA; Pillai’s trace value = 0.003; F(2, 1268) = 1.950, p = 0.143, partial η2 = 0.003. Therefore, the results suggest that the third hypothesis (H3) should be rejected. With MEA, male students (M = 3.3, SD = 0.03) scored slightly higher than female students (M = 3.27, SD = 0.02). With LMA, male students (M = 3.3, SD = 0.02) scored slightly higher than female students (M = 3.22, SD = 0.02). However, these differences is statistically non-significant.
The results reveal that there is a significant multivariate effect on LMA and MEA in the interaction between grade level and students’ average mathematics scores; Pillai’s trace = 0.026, F(20, 2538) = 1.664, p = 0.032, partial η2 = 0.013. Therefore, the results suggest that the fifth hypothesis (Ho5) should be rejected. A separate ANOVA was conducted for each dependent variable. There was a significant difference between grade level and students’ average mathematics scores on MEA; F(10, 1269) = 2.251, p = 0.015, partial η2 = 0.017. By looking at the interaction plots (Figure 1), students in Grade 11 (M = 3.39, SD = 0.11) and Grade 12 (M = 3.57, SD = 0.15) getting average mathematics scores below 5.0 had higher levels of MEA than others from the same school years. In Grade 10, students getting average mathematics scores 5.0–6.0 (M = 3.37, SD = 0.08) had higher levels of MEA than others from the same school years. The mean scores of students in MEA are presented in Table 5. There was no significant difference on LMA; F(10, 1269) = 1,868, p = 0.056, partial η2 = 0.015.
FIGURE 1. Interaction plot.
TABLE 5. Combined univariate ANOVA.
3.6 Correlation
Table 6 shows how mathematics anxiety correlates with academic coping strategies, average mathematics scores, and gender. The results are significant, with a moderately positive correlation between mathematics anxiety and positive coping (r = 0.487, p < 0.01); a weak positive correlation between mathematics anxiety and non-coping (r = 0.213, p < 0.01); gender (r = 0.050, p < 0.05); and average mathematics score (r = 0.064, p < 0.05). This finding shows that students with higher levels of positive coping and non-coping suffer mathematics anxiety to a greater extent than others. In addition, the higher the mathematics anxiety level, the higher the average mathematics score. There was a significant, weak negative correlation between mathematics anxiety and denial coping (r = −0.131, p < 0.01), indicating that the higher the level of denial coping, the lower the level of mathematics anxiety and vice versa. Mathematics anxiety was not significantly correlated with projective coping strategies.
TABLE 6. Correlation.
3.7 Regression
Multiple linear regression analysis was used to determine the factors influencing level of mathematics anxiety among high school students. Multiple linear regression analysis was performed with the following independent variables: academic coping strategies, including positive coping, projective coping, denial coping, and non-coping; students’ average mathematics score; gender; grade; and career choice. The dependent variable was the mathematics anxiety scale. The preliminary assumption of multiple linear regression was used as the basis for examining multicollinearity. Additionally, the normal P-P plot of the regression standardised residuals shows that the scattered points are principally distributed on the diagonal, which suggests that the residuals could be judged to be normally distributed, as presented in Figure 2.
FIGURE 2. Normal P-P-plot of the regression standardised residual.
Multicollinearity was examined for all independent variables using Pearson’s bivariate correlation. The correlation coefficients were less than 0.8, indicating that there was no multicollinearity between the independent variables (Allison, 1999). The variance inflation factor (VIF) and tolerance value were determined to test this assumption. The tolerance of every variable was higher than 0.1 and the VIF values for all variables were lower than 10, indicating that there was no multicollinearity between the independent variables in the multiple regression analysis (Uyanık and Güler, 2013). Therefore, the assumption was satisfied and the regression analysis was conducted.
Table 7 reveals that the corrected coefficient, Adjusted R2, was 0.384, indicating a change in the dependent variable, students’ mathematics anxiety, due to a one-unit change in the independent variable. The regression model was statistically significant (F(18, 1529) = 54.658, p < 0.001, R2 = 0.392). The results of the multiple regressions with independent factors regressed against students’ mathematics anxiety show that positive coping (β = 0.198, p < 0.001), projective coping (β = 0.032, p < 0.05), non-coping (β = 0.036, p < 0.001), gender (β = 0.066, p < 0.05), grade and career choices were significant predictors in the model.
TABLE 7. The results of multiple regression analysis of mathematics anxiety.
Specifically, the difference in mathematics anxiety between grade 10 and grade 12 was −0.034. The difference in mathematics anxiety between grade 11 and grade 12 was −0.037. According to career choices, the difference in mathematics anxiety between Pedagogy students and others was 0.488. The difference in mathematics anxiety between Finance—Economics students and others was 0.672. The difference in mathematics anxiety between Social Sciences students and other students was 0.613. The difference in mathematics anxiety between Natural Sciences students and others was 0.484. The difference in mathematics anxiety between Engineering—Industry students and others was 0.725. The difference in mathematics anxiety between Information Technology (IT) students and others was 0.553. The difference in mathematics anxiety between Transportation Sector students and others was 0.136. The difference in mathematics anxiety between Medicine students and others was 0.578.
However, mathematics anxiety shows non-significant associations with average mathematics scores and denial coping strategies. Regression analysis showed that factors including positive coping, projective coping, non-coping, gender, grade, and students’ future career choices are significant predictors of mathematics anxiety. When examining the interaction, results revealed non-significant interaction between grade and average mathematics scores in predicting mathematics anxiety among students.
4 Discussion
The main aims of this research were twofold: firstly, to examine mathematics anxiety among Vietnamese high school students, and secondly, to test the correlation between students’ mathematics anxiety and academic coping strategies.
The analysis was intriguing to the authors. It did not show any differences in mathematics anxiety between male and female high school students; this is a significant contribution to research on underlying gender differences in relation to mathematics anxiety. It was predicted that the findings would support the hypothesis that mathematics anxiety reduces the mathematics achievement of students; however, the fourth hypothesis was accepted. Although the result showed that there was a correlation between mathematics anxiety and mathematics achievement, mathematics anxiety had a non-significant effect on mathematics achievement. The other important finding is that the more students suffer from mathematics anxiety, the more they adopt positive coping strategies.
These findings reinforce the general belief that students in Grade 12 suffer more from mathematics anxiety than those in Grades 10 and 11. The education curriculum in high school, especially in Grade 12, includes demanding academic tasks and complex academic content that give rise to academic pressure. Moreover, Grade 12 students have to pass a graduation examination to enroll in their desired university. Academic pressure can also be a consequence of parental pressure (Deb et al., 2015), which impacts students’ mental health and leads to psychological problems related to mathematics anxiety. Our results do not show any significant correlation that contradicts the findings of Lew and Hwang (2019), who reported that Grade 11 students had the highest level of mathematics anxiety and Grade 12 students the lowest. Another study found that there is no significant difference in the levels of mathematics anxiety between grades (Luo et al., 2009).
This study shows that students choosing to pursue finance-economics or industrial engineering at a higher education level have greater levels of mathematics anxiety than others. This finding is in contrast with previous studies and can be explained in a number of ways. Chipman et al. (1992) reported that students with mathematics anxiety were likely to choose degree courses with the minimum requirements for knowledge related to mathematics, such as the arts and humanities, languages, politics, and psychology and sociology, instead of courses such as natural science and economics (Zettle and Raines, 2000). Finance-economics and industrial engineering are popular specialties requiring substantial mathematics knowledge. Moreover, one of the most important criteria for applying to these courses, is the score in the entrance examination, which has gradually risen each year in Vietnam. This rise could be interpreted in two ways: 1) higher levels of competence are needed for career choices, for instance, international collaboration, multidisciplinary knowledge, and subject knowledge (Tran et al., 2016); and 2) the number of students applying to finance-economics and industrial engineering courses has gradually increased which has led to greater competition. The higher level of mathematics anxiety among students pursuing finance-economics and industrial engineering may be a direct consequence of the increasing score requirement.
Gender differences in mathematics anxiety have been thoroughly studied and well documented. Specifically, mathematics anxiety has been found to be significantly higher in female students than in male students (Karimi and Venkatesan, 2009; Luo et al., 2009; Cheema and Sheridan, 2015; Lew and Hwang, 2019). However, when comparing our results to those of previous studies, the results run counter to the way in which gender difference has been debated in the fields of psychology and educational science. Several studies have found results that are consistent with our reported findings. Keshavarzi and Ahmadi (2013), and Akbayir (2019), also found no difference between male and female high school students in levels of mathematics anxiety. Until now, gender differences in mathematics anxiety have been assumed to be influenced by factors such as a student’s attraction to the mathematics field (Keshavarzi and Ahmadi, 2013); the possibility that females are more willing to disclose anxiety(Ashcraft, 2002); social stereotypes about women and mathematical abilities (Beilock et al., 2007); spatial processing ability (Maloney and Beilock, 2012); and gender differences in stress reactivity (Altemus et al., 2014). In modern Vietnamese society, underestimation of the success of females in mathematics has been transmuted into respect for their abilities and achievements. This alteration in the Vietnamese context has contributed to removing gender stereotypes and increased self-confidence among females. Thus, female anxiety has decreased. Our findings on gender differences in mathematics anxiety could be explained by this shift.
The present research found an interaction between grade level and students’ academic achievement. Specifically, students in Grade 11 and Grade 12 attaining an average score below 5.0 experienced higher levels of MEA than others from the same school years. This finding shows that students with low average scores are likely to experience greater anxiety associated with a mathematical examination or test. In the Vietnamese educational context, tests, and examinations in compulsory education, especially in high school, are widely regarded as an essential and effective tool for evaluating personal qualities. Therefore, low and poor scores lead to negative feelings, such as disappointment, embarrassment, irritation, stress and anxiety. This finding is very similar to results that have been previously reported. For example, Nicholson (2009) found that the academic achievement of Grade 11 students is negatively affected by anxiety related to tests. Another study, conducted by Karatas et al. (2013), found that the more test anxiety that students experienced, the poorer their score in university entrance examinations. Additionally, grade point averages could be affected by high levels of test anxiety (Whitaker Sena et al., 2007; Von Der Embse et al., 2013). Tests and examinations have negative effects not only on students’ academic achievement but also on their health (Zeidner, 1998). Moreover, this anxiety is a factor inhibiting students from achieving their academic goals and demonstrating their full potential in the learning process (Zoller and Ben‐Chaim, 1990).
Our data analysis reveals that there is a positive correlation between the level of mathematics anxiety and mathematics achievement among students. The result shows that individuals who have high levels of mathematics anxiety tend to have higher average scores in mathematics. Specifically, students attaining averages from 9.0 to 10.0 experience greater LMA than others. This is an interesting finding, as it is contrary to what we know from previous studies (Karimi and Venkatesan, 2009; Lew and Hwang, 2019; Milovanović, 2020), which have reported a significant negative correlation between mathematics anxiety and mathematics achievement. However, few previous studies have been conducted to explain the negative relationship between mathematics anxiety and mathematics achievement. This relationship could be associated with reduced student effort and comprehension in the learning process; reduced accomplishment of study goals (Lew and Hwang, 2019); low mathematics ability (Tobias, 1986; Hembree, 1990; Ma and Xu, 2004); and students’ understanding and self-confidence in mathematics (Zakaria et al., 2012). For a long time in the Vietnamese educational context, grade point averages were considered to be the standard for evaluating whether students accomplished academic goals and gained academic achievements. Specifically, mathematics is an important subject for all students in the educational system and a compulsory subject in entrance and graduation exams. This situation increased students’ determination to complete mathematics-related tasks and attain better performance in the field of mathematics. The higher the average score attained by a student, the greater their interest in education, and this led to anxiety about mathematics among students. Our views on the association between mathematics anxiety and mathematics achievement can be clarified by these explanations.
The relation between positive coping strategies and mathematics anxiety has been examined in detail in the studies mentioned. In particular, mathematics anxiety has been found to be negatively correlated with positive coping strategies. The higher the levels of mathematics anxiety that students experience, the less likely they are to adopt positive coping strategies (Skaalvik, 2018). The findings from the present study are the reverse. Our findings show that there is a positive relationship between mathematics anxiety and positive coping strategies among students. For decades, traditional education in Vietnam has created a favourable learning environment for enhancing students’ knowledge, self-regulated learning ability, academic self-concept, motivation, and coping strategies. Several prior studies have examined academic motivation and self-regulated learning affecting the learning process of students; for instance, encouraging students to engage in learning-related tasks more efficiently (Zeynali et al., 2019), and proactively managing students’ learning environment (Pintrich, 1999). Salomon and Ket (2007) reported that Vietnamese society is influenced by Confucian traditions. Specifically, the academic self-concept is considered particularly important in Vietnam’s education system, placing a strong emphasis on students’ willingness to persevere and their working hard. For these reasons, when students face academic challenges, obstacles and difficulties in mathematics, they tend to focus on solving problems rather than blaming others for their mistakes or ignoring negative events. Therefore, the higher the level of mathematics anxiety that students experience, the more likely they are to adopt positive coping, understanding their mistakes and working out how to do better the next time.
4.1 Limitations
This study has several limitations. The first is that participants were not distributed uniformly; specifically, female participants outnumbered male participants by a proportion of approximately two to one, and this may affect the inaccuracy of the results. Therefore, future research should be conducted to validate the findings of this study. Also, only five factors were examined in researching the influences of mathematics anxiety in high school students. Thus, other factors, such as teacher attitude, classroom environment, learning programs and teaching methods could be included in future investigations.
5 Conclusion and Implications
Mathematics anxiety is an unpleasant emotional state, with tension and anxiety that affect the ability to manipulate numbers and participate in the mathematical learning process. This research makes a substantial contribution on the issue, as follows: 1) it highlights the effects of academic achievement and students’ career choices on mathematics anxiety; 2) it provides additional evidence of non-significant effects of gender difference on mathematics anxiety; 3) it offers insights into academic coping strategies that students adopt to overcome mathematics anxiety.
Future research should be designed to study factors influencing the relationship between academic achievement, gender difference and mathematics anxiety in more depth. Additionally, further investigation is necessary to evaluate the significant impact of academic coping strategies on anxiety in the learning process.
Our study contributes to the general discussion about the relationship between mathematics anxiety and academic coping strategies (Skaalvik, 2018), grade (Lew and Hwang, 2019), academic achievement (Puteh and Khalin, 2016), and students’ career choices (Chipman et al., 1992). Besides, the results are helpful for making students aware of mathematics anxiety, protecting themselves from mental illness such as stress, anxiety disorders and depression, and enhancing students’ mental health during the learning process. From that, students could understand their mental health status and implement coping strategies to get over their academic anxiety. Our findings provide additional information for teachers, school counselors to realize student’s negative emotions, opportunely support, and find appropriate methods to alleviate student’s negative emotions during the teaching process (Asikhia et al., 2015; Olaoluwa, 2021). Moreover, educators can design mathematics curriculum integrating theories and practices, and develop psychological support programs for Vietnamese high school students (Van et al., 2019) such as organizing hands-on activities in mathematics learning, applying cooperative learning in small-group learning, providing extra tuition sessions, organizing more discussions activities and more student directed classroom, and focusing on individual mathematics improvement. It is also essential to create support groups about mathematics in school that students can share and socialize during the learning process.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Author Contributions
V-LT-C contributed to conception and design of the study. H-TL-T, T-TN-T, M-TN-T, TT-L, and B-TN-D organized the database. T-TN-T and M-TN-T performed the statistical analysis. H-TL-T, T-TN-T, M-TN-T, TT-L, and B-TN-D wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
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.
Acknowledgments
The authors acknowledge to the principals, teachers at 9 high schools in who had supported us in collecting the data for this research.
References
Ader, E., and Erktin, E. (2010). Coping as Self-Regulation of Anxiety: A Model for Math Achievement in High-Stakes Tests. Cogn. Brain Behav. 14 (4), 311.
Akbayır, K. (2019). An Investigation about High School Students' Mathematics Anxiety Level According to Gender. Jets, 7(7), 62–70. doi:10.11114/jets.v7i7.4201
Al Mutawah, M. A. (2015). The Influence of Mathematics Anxiety in Middle and High School Students Math Achievement. Ies, 8(11), 239–252. doi:10.5539/ies.v8n11p239
Altemus, M., Sarvaiya, N., and Neill Epperson, C. (2014). Sex Differences in Anxiety and Depression Clinical Perspectives. Front. Neuroendocrinol 35 (3), 320–330. doi:10.1016/j.yfrne.2014.05.004
Ashcraft, M. H., and Kirk, E. P. (2001). The Relationships Among Working Memory, Math Anxiety, and Performance. J. Exp. Psychol. Gen. 130 (2), 224–237. doi:10.1037/0096-3445.130.2.224
Ashcraft, M. H. (2002). Math Anxiety: Personal, Educational, and Cognitive Consequences. Curr. Dir. Psychol. Sci. 11 (5), 181–185. doi:10.1111/1467-8721.00196
Asikhia, O. A., and Mohangi, K. J. G. (2015). The Use of Problem-Solving Training in Reducing Mathematics Anxiety Among Nigerian Secondary School Students. 13(1), 6547–6558.
Beilock, S. L., Rydell, R. J., and McConnell, A. R. (2007). Stereotype Threat and Working Memory: Mechanisms, Alleviation, and Spillover. J. Exp. Psychol. Gen. 136 (2), 256–276. doi:10.1037/0096-3445.136.2.256
Bentler, P. M., and Bonett, D. G. (1980). Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychol. Bull. 88 (3), 588–606. doi:10.1037/0033-2909.88.3.588
Betz, N. E. (1978). Prevalence, Distribution, and Correlates of Math Anxiety in College Students. J. Couns. Psychol. 25 (5), 441–448. doi:10.1037/0022-0167.25.5.441
Bursal, M., and Paznokas, L. (2006). Mathematics Anxiety and Preservice Elementary Teachers' Confidence to Teach Mathematics and Science. Sch. Sci. Maths. 106 (4), 173–180. doi:10.1111/j.1949-8594.2006.tb18073.x
Cheema, J. R., and Sheridan, K. (2015). Time Spent on Homework, Mathematics Anxiety and Mathematics Achievement: Evidence from a US Sample. Issues Educ. Res. 25 (3), 246–259.
Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling, 295. Mahwah State, NJ: Modern methods for business research.
Chipman, S. F., Krantz, D. H., and Silver, R. (1992). Mathematics Anxiety and Science Careers Among Able College Women. Psychol. Sci. 3 (5), 292–296. doi:10.1111/j.1467-9280.1992.tb00675.x
Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika 16 (3), 297–334. doi:10.1007/BF02310555
Deb, S., Strodl, E., and Sun, H. (2015). Academic Stress, Parental Pressure, Anxiety and Mental Health Among Indian High School Students. Int. J. Psychol. Behav. Sci. 5 (1), 26–34. doi:10.5923/j.ijpbs.20150501.04
Espino, M., Pereda, J., Recon, J., Perculeza, E., and Umali, C. (2017). Mathematics Anxiety and its Impact on the Course and Career Choice of Grade 11 Students. Int. J. Educ. Psychol. Counselling 2 (5), 99–119.
Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. marketing Res. 18 (1), 39–50. doi:10.1177/00222437810180010410.2307/3151312
Friedel, J. M., Cortina, K. S., Turner, J. C., and Midgley, C. (2007). Achievement Goals, Efficacy Beliefs and Coping Strategies in Mathematics: The Roles of Perceived Parent and Teacher Goal Emphases. Contemp. Educ. Psychol. 32 (3), 434–458. doi:10.1016/j.cedpsych.2006.10.009
Hackett, G. (1985). Role of Mathematics Self-Efficacy in the Choice of Math-Related Majors of College Women and Men: A Path Analysis. J. Couns. Psychol. 32 (1), 47–56. doi:10.1037/0022-0167.32.1.47
Hair, J., Black, W., Babin, B., and Anderson, R. (2018). Multivariate Data Analysis. 8th Edn.. London: Cengage.
Hair, J. F., Money, A. H., Samouel, P., and Page, M. (2007). Research Methods for Business. Chichester, United Kingdom: Education+ Training.
Hembree, R. (1990). The Nature, Effects, and Relief of Mathematics Anxiety. Jrme, 21(1), 33–46. doi:10.5951/jresematheduc.21.1.0033
Ho, H.-Z., Senturk, D., Lam, A. G., Zimmer, J. M., Hong, S., Okamoto, Y., et al. (2000). The Affective and Cognitive Dimensions of Math Anxiety: A Cross-National Study. J. Res. Math. Educ., 31(3), 362–379. doi:10.2307/749811
Jackson, C. D., and Leffingwell, R. J. (1999). The Role of Instructors in Creating Math Anxiety in Students from Kindergarten through College. Mt, 92(7), 583–586. doi:10.5951/MT.92.7.0583
Karatas, H., Alci, B., and Aydin, H. (2013). Correlation Among High School Senior Students Test Anxiety, Academic Performance and Points of university Entrance Exam. Educ. Res. Rev., 8(13), 919–926. doi:10.5897/ERR2013.1462
Karimi, A., and Venkatesan, S. (2009). Mathematics Anxiety, Mathematics Performance and Academic Hardiness in High School Students. Int. J. Educ. Sci., 1(1), 33–37. doi:10.1080/09751122.2009.11889973
Keshavarzi, A., and Ahmadi, S. (2013). A Comparison of Mathematics Anxiety Among Students by Gender. Proced. - Soc. Behav. Sci., 83, 542–546. doi:10.1016/j.sbspro.2013.06.103
Krypel, M. N., and Henderson-King, D. (2010). Stress, Coping Styles, and Optimism: Are They Related to Meaning of Education in Students' Lives? Soc. Psychol. Educ., 13(3), 409–424. doi:10.1007/s11218-010-9132-0
Kurniawan Halim, J., Margaretha, S., and Rahardja Honantha, C. (2018). Consumer's Response to E-Mail Advertisement from Tour and Travel agency in Indonesia. Adv. Soc. Sci. Edu. Humanities Res., 186, 162–165. doi:10.2991/insyma-18.2018.40
Kyttälä, M., and Björn, P. M. (2010). Prior Mathematics Achievement, Cognitive Appraisals and Anxiety as Predictors of Finnish Students' Later Mathematics Performance and Career Orientation. Educ. Psychol., 30(4), 431–448. doi:10.1080/01443411003724491
Lazarus, R. S. (2013). Fifty Years of the Research and Theory of R.S. Lazarus. In Fifty Years of the Research and Theory of RS Lazarus: An Analysis of Historical and Perennial Issues (pp. 366–388). New York City, NY: Psychology Press. doi:10.4324/9780203774595
Lent, R. W., Brown, S. D., and Hackett, G. (1994). Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance. J. Vocational Behav. 45 (1), 79–122. doi:10.1006/jvbe.1994.1027
Lent, R. W., Brown, S. D., Talleyrand, R., McPartland, E. B., Davis, T., Chopra, S. B., et al. (2002). Career Choice Barriers, Supports, and Coping Strategies: College Students' Experiences. J. Vocational Behav., 60(1), 61–72. doi:10.1006/jvbe.2001.1814
Lew, K., and Hwang, S. (2019). A Study on the Correlation between Mathematics Anxiety and Mathematics Achievement in High School Students. Math. Edu., 58(3), 337–346. doi:10.7468/mathedu.2019.58.3.337
Luo, X., Wang, F., and Luo, Z. (2009). Investigation and Analysis of Mathematics Anxiety in Middle School Students. J. Math. Edu., 2(2), 12–19. doi:10.7468/mathedu.2019.58.3.337
Ma, X., Xu, J., and Xu, J. 2004). The Causal Ordering of Mathematics Anxiety and Mathematics Achievement: a Longitudinal Panel Analysis. J. Adolesc., 27(2), 165–179. doi:10.1016/j.adolescence.2003.11.003
Ma, X. (1999). A Meta-Analysis of the Relationship between Anxiety toward Mathematics and Achievement in Mathematics. J. Res. Math. Educ., 30(5), 520–540. doi:10.2307/749772
MacCallum, R. C., Browne, M. W., and Sugawara, H. M. (1996). Power Analysis and Determination of Sample Size for Covariance Structure Modeling, Psychol. Methods. 1(2), 130–149. doi:10.1037/1082-989X.1.2.130
Malhotra, N. K., and Birks, D. (2007). Marketing Research. in Essex. European Edition (Essex, United Kingdom: An Applied Approach).
Maloney, E. A., and Beilock, S. L. (2012). Math Anxiety: Who Has it, Why it Develops, and How to Guard against it. Trends Cogn. Sci., 16(8), 404–406. doi:10.1016/j.tics.2012.06.008
Mau, W.-C., Domnick, M., and Ellsworth, R. A. (1995). Characteristics of Female Students Who Aspire to Science and Engineering or Homemaking Occupations. Career Dev. Q., 43(4), 323–337. doi:10.1002/j.2161-0045.1995.tb00437.x
McAnallen, R. R. (2010). Examining Mathematics Anxiety in Elementary Classroom Teachers. Connecticut: University of Connecticut.
Meyers, L. S., Gamst, G., and Guarino, A. J. (2016). Applied Multivariate Research: Design and Interpretation. Thousand Oaks, CA: Sage publications.
Milovanović, I. (2020). Math Anxiety, Math Achievement and Math Motivation in High School Students: Gender Effects. Croat. J. Edu., 22(1), 175–206. doi:10.15516/cje.v22i1.3372
Minsoo, E., Zain, N. M., Ahmad, N. A., and Erlina, A. (2012). Mathematics Anxiety and Achievement Among Secondary School Students. Am. J. Appl. Sci., 9(11), 1828–1832. doi:10.3844/ajassp.2012.1828.1832
M., P., and S. Z., K. (2016). Mathematics Anxiety and its Relationship with the Achievement of Secondary Students in Malaysia. Ijssh, 6(2), 119–122. doi:10.7763/IJSSH.2016.V6.630
Nicholson, A. M. (2009). Effects of Test Anxiety on Student Achievement (ACT) for College Bound students Doctoral Dissertation. Tennessee: Trevecca Nazarene University. School of Education.
Oberlin, L. (1982). How to Teach Children to Hate Mathematics. Sch. Sci. Maths., 82(3), 261. doi:10.1111/j.1949-8594.1982.tb17187.x
Olaoluwa, S. A. (2021). Applying Cognitive Therapy for Depression and Anxiety in Mathematics Education for Students' Sustainable Performance, Ce. 12(6), 1407–1418. doi:10.4236/ce.2021.126107
Pintrich, P. R. (1999). The Role of Motivation in Promoting and Sustaining Self-Regulated Learning. Int. J. Educ. Res., 31(6), 459–470. doi:10.1016/S0883-0355(99)00015-4
Plake, B. S., and Parker, C. S. (1982). The Development and Validation of a Revised Version of the Mathematics Anxiety Rating Scale. Educ. Psychol. Meas., 42(2), 551–557. doi:10.1177/001316448204200218
Salomon, M., and Kêt, V. D. (2007). i M I, Education and Identity Formation in Contemporary Vietnam. Compare: A J. Comp. Int. Edu., 37(3), 345–363. doi:10.1080/03057920701330222
Sena, J. D., Lowe, P. A., and Lee, S. W. (2007). Significant Predictors of Test Anxiety Among Students with and without Learning Disabilities. J. Learn. Disabil., 40(4), 360–376. doi:10.1177/00222194070400040601
Sharma, S., Mukherjee, S., Kumar, A., and Dillon, W. R. (2005). A Simulation Study to Investigate the Use of Cutoff Values for Assessing Model Fit in Covariance Structure Models. J. Business Res., 58(7), 935–943. doi:10.1016/j.jbusres.2003.10.007
Singer, J. M., and Stake, J. E. (1986). Mathematics and Self-Esteem: Implications for Women's Career Choice. Psychol. women Q., 10(4), 339–352. doi:10.1111/j.1471-6402.1986.tb00759.x
Skaalvik, E. M. (2018). Mathematics Anxiety and Coping Strategies Among Middle School Students: Relations with Students' Achievement Goal Orientations and Level of Performance. Soc. Psychol. Educ., 21(3), 709–723. doi:10.1007/s11218-018-9433-2
Sue, D., Sue, D. W., Sue, S., and Sue, D. M. (2015). Understanding Abnormal Behavior. New York City, NY: Cengage Learning.
Suinn, R. M., Edie, C. A., Nicoletti, J., and Spinelli, P. R. 1972). The MARS, a Measure of Mathematics Anxiety: Psychometric Data. J. Clin. Psychol., 28(6), 373–375. doi:10.1037/h0033456doi:10.1002/1097-4679(197207)28:3+<373:aid-jclp2270280320>3.0.co;2-c
Tabachnick, B. G., Fidell, L. S., and Ullman, J. B. (2007). Using Multivariate Statistics, 5. MA: Pearson Boston.
Taber, K. S. (2018). The Use of Cronbach's Alpha when Developing and Reporting Research Instruments in Science Education. Res. Sci. Educ., 48(6), 1273–1296. doi:10.1007/s11165-016-9602-2
Taylor, B. A., and Fraser, B. J. (2013). Relationships between Learning Environment and Mathematics Anxiety. Learn. Environ Res, 16(2), 297–313. doi:10.1007/s10984-013-9134-x
Tero, P., and Connell, J. (1984). When Children Think They’ve Failed: An Academic Coping Inventory. Unpublished manuscript. New Orleans, LA: University of Rochester.
Tobias, S. (1986). Anxiety and Cognitive Processing of Instruction. Self-related Cogn. anxiety Motiv., 35–54. doi:10.4324/9780203781609
Tran, L., Marginson, S., Do, H., Le, T., Nguyen, N., Vu, T., et al. (2016). Higher Education in Vietnam: Flexibility, Mobility and Practicality in the Global Knowledge Economy. Palgrave Macmillan. doi:10.1057/9781137436481
Uyanık, G. K., and Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Proced. - Soc. Behav. Sci., 106, 234–240. doi:10.1016/j.sbspro.2013.12.027
Van, N. T. (2018). Mức Độ Lo Âu Của Học Sinh Trung Học Phổ Thông Tại Thành Phố Hồ Chí Minh. Tạp chí Khoa học 15 (1), 117–127.
Van, S. H., Thien, V. G., Tat, T. D., Tran, L., and Duc, H. D. J. E. J. o. E. R. (2019). The Stress Problems and the Needs for Stress Counseling of High School Students in Vietnam. Eur. J Ed Res 8 (4), 1053–1061. doi:10.12973/eu-jer.8.4.1053
Von Der Embse, N., Barterian, J., and Segool, N. (2013). Test Anxiety Interventions for Children and Adolescents: A Systematic Review of Treatment Studies from 2000-2010. Psychol. Schs., 50(1), 57–71. doi:10.1002/pits.21660
Wasko, M. M., and Faraj, S. J. M. q. (2005). Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice. MIS Q., 29(1), 35–57. doi:10.2307/25148667
Wiedemann, K. (2001). Anxiety and Anxiety Disorders. Int. Encyclopedia Soc. Behav. Sci., 1, 560–567. doi:10.1016/B0-08-043076-7/03760-8
Woodard, T. (2004). The Effects of Math Anxiety on Post-Secondary Developmental Students as Related to Achievement, Gender, and Age. Inquiry, 9 (1).
Yüksel-Şahin, F. (2008). Mathematics Anxiety Among 4th and 5th Grade Turkish Elementary School Students. Int. Electron. J. Maths. Edu. 3 (3), 179–192.
Zettle, R. D., and Houghton, L. L. (1998). The Relationship between Mathematics Anxiety and Social Desirability as a Function of Gender. Coll. Student J. 32 (1), 81–86.
Zettle, R. D., and Raines, S. J. (2000). The Relationship of Trait and Test Anxiety with Mathematics Anxiety. Coll. Student J. 34 (2), 246–259.
Zeynali, S., Pishghadam, R., and Hosseini Fatemi, A. (2019). Identifying the Motivational and Demotivational Factors Influencing Students' Academic Achievements in Language Education. Learn. Motiv., 68, 101598. doi:10.1016/j.lmot.2019.101598
Keywords: mathematics anxiety, high school student, academic achievement, academic coping strategies, average mathematics score, gender, career choice, grade
Citation: Luu-Thi H-T, Ngo-Thi T-T, Nguyen-Thi M-T, Thao-Ly T, Nguyen-Duong B-T and Tran-Chi V-L (2021) An Investigation of Mathematics Anxiety and Academic Coping Strategies Among High School Students in Vietnam: A Cross-Sectional Study. Front. Educ. 6:742130. doi: 10.3389/feduc.2021.742130
Received: 15 July 2021; Accepted: 19 October 2021;
Published: 04 November 2021.
Edited by:
María Jesús Luque Rojas, University of Malaga, SpainReviewed by:
Ibrahim Burak Ölmez, University of Southern California, United StatesVera Monteiro, University Institute of Psychological, Social and Life Sciences (ISPA), Portugal
Copyright © 2021 Luu-Thi, Ngo-Thi, Nguyen-Thi, Thao-Ly, Nguyen-Duong and Tran-Chi. 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: Thuy-Trinh Ngo-Thi, thuytrinhgeirlys@gmail.com