Quang-Vinh Trinh
Doctoral student, Department of Educational Policy and Administration, National Chi Nan University Fwu-Yuan Weng
Professor, Department of Educational Policy and Administration, National Chi Nan University
一、 INTRODUCTION
Programme for International Student Assessment (PISA) is a 3-year cycle of international assessments organised by the Organisation for Economic Co-operation and Development (OECD) since 2000. In each cycle, PISA has assessed three key fields of knowledge and skill, namely reading, mathematics, and science literacies (OECD, 2013). PISA offers insight for education policy and practice, which helps to monitor trends in students’ acquisition of knowledge, and skills across countries, economies, and different demographic subgroups within each country. More specifically, PISA provides reliable empirical evidence to identify the strengths and weaknesses of the education systems.
PISA 2012 focused on measuring student mathematics literacy and its data collection covered 65 countries. Because the measurement of PISA is quite comprehensive and credible, this paper used this database of PISA to select the targeted samples. In addressing the paper gap, the purpose of this paper is to analyze the factors influence mathematics performance of Vietnamese
and Southeast Asian countries' students.
Three research questions were designed in agreement with the aims of this paper.
(1) How is the mathematics performance of Vietnamese and Southeast Asian countries' students?
(2) Are there significant differences between the mathematics performance and gender in ASEAN countries' students?
(3) Are there significant differences of mathematics performance, between gender and parent's academic level in ASEAN countries' students?
二、 LITERATURE REVIEW
The Organization for Economic Co-operation and Development’s (OECD) PISA also focuses on using mathematics. It is designed to assess the readiness of 15-year-olds for life beyond school, focusing on the extent to which students are able to use their knowledge and skills to meet real-life challenges.
This reflects a change in curricular goals and objectives in many countries, which are increasingly concerned with what students can do with what they learn at school (OECD, 2003).
Mathematical literacy is defined in PISA as (OECD, 1999)
“an individual’s capacity to identify and understand the role that mathematics plays in the world, to make well founded judgments and to use and engage with mathematics in ways that meet the needs of that individual’s life as a constructive, concerned and reflective citizen.”
Coben (2003) indicated that a fundamental problem for anyone reviewing the research literature in this area: there is as yet no consensus about the nature of adult numeracy. Numeracy is a deeply contested concept, beset by terminological confusion, especially when referring to adults. He argued that a plethora of similar and loosely related terms compete for attention:
mathematical literacy, techno-mathematical literacy, quantitative literacy, functional mathematics, mathemacy, and so on.
A small scale study by Hackett &
Betz (1989) discovered positive correlations among students' mathematics achievement and their levels of self-efficacy, mathematics attitudes, and their masculine sex-role orientation. In 1990, Matsui et al.
explored the mechanisms underlying mathematics self-efficacy in Japanese college students. They found that students with high frequencies of mathematics accomplishment also had higher levels of mathematics
self-efficacy than did students with fewer accomplishments. They also found that males' mathematics self-efficacy was significantly higher than that of females. In a large longitudinal study, Akinloye (2005) investigated the variables as behavior, ethnicity, and gender towards students' mathematics performance. This study attempted to find out the effects of student-made manipulatives, behavior, ethnicity and gender on the mathematics section performance of students in a Southeast Texas school district.
According to Lloyd et al. (2005) showed that girls' achievement in mathematics met or exceeded that of boys. It seems that there have been relative gains for girls in terms of attributions. More specifically, it appears that girls' success and failure attributions tended to be more self-enhancing than reported in traditional attribution research. He pointed out that boys' success and failure attributions were also relatively self-enhancing. Also, these results seem to challenge those of previous studies that claim that girls espouse more self-defeating attribution styles than boys. The research project of Solange (2013) aimed at exploring the factors affecting the performance of students in these courses and coming up with recommendations to enhance their achievement. It also studied gender differences in the performance of students in remedial mathematics
courses and revealed patterns of students' attitude towards mathematics and mathematics anxiety.
三、 METHODS
The paper presents the research methods with three sections: Samples, Research Variables, and Data Analysis.
(一) Samples
This paper uses data mining method to explore the PISA 2012 data in order to answer the research questions and achieve the research aims. The targeted students were selected from ASEAN countries, including Vietnam (4,959), Indonesia (5,622), Malaysia (5,197), Singapore (5,546), and Thailand (6,606). The total number of samples was 27,930 and their age ranged from 15.3 to 16.2.
Figure 1. Research framework (二) Research Variables
The selected variables in this paper are mathematics performance (PViMATH), gender, and highest educational level of parents (HISCED).
The definitions of the research variables are as follows:
a. Mathematics performance (OECD, 2014): This used five multi-dimensional scaling models respective five plausible values from PV1MATH to PV5MATH.
b. Highest educational level of parents (OECD, 1999 & 2014):
This index is constructed by taking the highest level of father and mother and having the following categories: (0) None (not received training), (1) Completed ISCED level 1 (primary education), (2) Completed ISCED level 2 (lower secondary education), (3) Completed ISCED levels 3B or 3C (upper secondary education providing direct access to the labour market or to ISCED 5B programmes), (4) Completed ISCED level 3A (upper secondary education providing access to ISCED 5A and 5B programmes) and/or ISCED level 4 (non-tertiary post-secondary), (5) Completed ISCED level 5B
(non-university tertiary education), (6) Completed ISCED level 5A (university
level tertiary education) or ISCED level 6 (advanced research programmes).
Table 1. Mapping of ISCED to years
Country ISCED 1 ISCED 2 ISCED 3B,
(Source: OECD, 2014)
(三) Data Analysis
Data analysis was performed with IBM – SPSS 22.0 software. Descriptive analysis was used to answer the research question 1: “How is the mathematics performance of Vietnamese and Southeast Asian countries' students?”.
Independent-Sample t-Test analysis was used to answer the research question 2:
“Are there significant differences between the mathematics performance and gender in ASEAN countries' students?”. One-Way ANOVA analysis was used to answer the research question 3: “Are there significant differences of mathematics performance, between gender and parent's academic level in ASEAN countries' students?”.
四、 RESULTS
The paper intends to explore significant differences of mathematics performance, between gender and parent's academic level in ASEAN countries' students.
(一) Student performance in mathematics in Viet Nam and Southeast Asian countries
Table 2 shows the overall statistics of student performance in mathematics in Viet Nam and Southeast Asian countries. Generally, mathematics performance in ASEAN countries’
students was 462.03 – 462.22, Singapore (568.18 – 568.82) showed the highest
Table 2. Descriptive Statistics of Student Performance in Mathematics in ASEAN Countries
Country PV1MATH PV2MATH PV3MATH PV4MATH PV5MATH Viet Nam N 4959 4959 4959 4959 4959
Mean 510.59 510.89 510.60 510.61 510.94 Median 508.34 509.59 508.97 507.72 508.97 SD 83.53 83.80 84.69 84.71 84.37 Skewness 0.09 0.04 0.08 0.04 0.10
Country PV1MATH PV2MATH PV3MATH PV4MATH PV5MATH Kurtosis -0.02 0.05 0.09 -0.03 0.03 Indonesia N 5622 5622 5622 5622 5622 Mean 376.07 375.31 375.57 375.22 375.93 Median 371.76 371.79 371.25 370.70 373.00 SD 70.40 70.19 69.95 69.87 69.87 Skewness 0.29 0.27 0.32 0.28 0.30 Kurtosis 0.25 0.17 0.29 0.17 0.26 Malaysia N 5197 5197 5197 5197 5197 Mean 422.79 422.14 421.89 421.47 421.54 Median 418.53 419.15 417.75 417.67 417.05 SD 80.76 80.72 80.44 80.62 80.63 Skewness 0.20 0.18 0.21 0.22 0.20 Kurtosis -0.19 -0.16 -0.13 -0.14 -0.11 Singapore N 5546 5546 5546 5546 5546 Mean 568.36 568.70 568.68 568.82 568.18 Median 574.63 573.73 574.67 573.50 573.77 SD 104.71 104.55 104.95 104.08 104.97 Skewness -0.19 -0.20 -0.24 -0.20 -0.23 Kurtosis -0.23 -0.27 -0.25 -0.25 -0.23 Thailand N 6606 6606 6606 6606 6606 Mean 441.15 441.53 441.19 441.80 441.41 Median 430.88 430.92 430.99 430.99 430.22 SD 91.86 92.24 92.16 92.23 92.32 Skewness 0.45 0.44 0.42 0.44 0.44 Kurtosis 0.07 0.05 0.05 0.02 0.03 Total N 27930 27930 27930 27930 27930 Mean 462.22 462.16 462.03 462.05 462.05 Median 450.47 450.66 448.99 449.69 449.61 SD 110.29 110.57 110.64 110.60 110.54 Skewness 0.41 0.40 0.41 0.40 0.41 Kurtosis -0.22 -0.24 -0.25 -0.25 -0.24
(Source: based on the PISA 2012 data)
Overall, the coefficient of Skewness in Thailand (0.42 – 0.45) showed the highest level, followed by Indonesia (0.27 – 0.32), and Malaysia (0.18 – 0.22), that means the student performance in mathematics distribution skewed to the left; only Singapore (-0.24 – -0.19) skewed to the right. The
coefficient of Kurtosis in Singapore (-0.27 – -0.23) and Malaysia (-0.19 – -0.11), had the low degree of the peakedness; only Indonesia (0.17 – 0.29) had the high degree of the peakedness.
Viet Nam was the most stable country, less affected by the coefficient of Skewness and Kurtosis.
Figure 2. Student performance in mathematics in ASEAN countries (Source: based on the PISA 2012 data)
(二) The differences between the