Affective characteristics and mathematics performance in Indonesia, Malaysia, and Thailand: what can PISA 2012 data tell us?

被引:13
|
作者
Thien, Lei Mee [1 ]
Darmawan, I. Gusti Ngurah [2 ]
Ong, Mei Yean [1 ]
机构
[1] SEAMEO Reg Ctr Sci & Math Educ RECSAM, Res & Dev Div, Jalan Sultan Azlan Shah, Gelugor 11700, Penang, Malaysia
[2] Univ Adelaide, Sch Educ, Adelaide, SA 5005, Australia
关键词
Affective characteristics; Hierarchical linear modeling (HLM); Mathematics performance; Programme for International Student Assessment (PISA); Southeast Asian countries;
D O I
10.1186/s40536-015-0013-z
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Background: The results of the Programme for International Student Assessment (PISA) 2012 showed that Indonesia, Malaysia, and Thailand underperformed and were positioned in the bottom third out of 65 participating countries for mathematics, science, and reading literacies. The wide gap between these three countries and the top performing countries has prompted this study to examine the influence of students' affective characteristics on their performance in mathematics literacy using a multilevel analysis. The purpose of this study is to examine the relationships among affective characteristics-related variables at the student level, the aggregated school-level variables, and mathematics performance by using the Programme for International Student Assessment (PISA) 2012 dataset. Method: The data used for the analysis were retrieved from the official PISA website. The student samples from Indonesia, Malaysia and Thailand were 5, 622, 5, 192 and 6, 602, respectively. The data were analysed using descriptive statistics, and a hierarchical linear modeling (HLM) approach with the HLM version 7.0 computer programme. Results: Different patterns of relationships were found between student- and school-level variables and mathematics performance in the three countries. The common student-level variable is attitudes towards learning outcomes, which predicted an increase in scores for the Indonesian, Malaysian, and Thai models. At the student level, the strongest predictor on mathematics literacy performance was mathematics self-efficacy for both Indonesian and Malaysian models, and perseverance for the Thai Model. At the school level, school average mathematics self-efficacy was the strongest predictor of mathematics performance in the Indonesian model; average openness to problem-solving in the Thai model; and school average instrumental motivation, mathematics behaviour, and attitudes towards learning outcomes predicted a decrease in scores for the Malaysian model. Conclusion: The inconclusive results of the multilevel analysis has demonstrated some interesting points for discussion, though the results could be attributed to the differences in education system and a diversity of cultural context in each individual country. This study contributes to providing evidence-based policy making in addition to informing the mathematics teachers the particular students' affective characteristics, which should be strengthened to ensure better mathematics learning outcomes in Indonesia, Malaysia, and Thailand. Implications of the findings and limitations are discussed.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] What Can Business History Tell Us About Business Performance?
    Gourvish, Terry
    COMPETITION & CHANGE, 2006, 10 (04) : 375 - 392
  • [22] Characteristics and emerging patterns of forest conflicts in Europe - What can they tell us?
    Nousiainen, Daniela
    Mola-Yudego, Blas
    FOREST POLICY AND ECONOMICS, 2022, 136
  • [23] What can US city price data tell us about purchasing power parity?
    Chen, LL
    Devereux, J
    JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2003, 22 (02) : 213 - 222
  • [24] What does social performance data tell us? Apparently, not enough
    Burns, Carolyn
    Canadian Mining Journal, 2022, 143 (08):
  • [25] What catch data can tell us about the status of global fisheries
    Rainer Froese
    Dirk Zeller
    Kristin Kleisner
    Daniel Pauly
    Marine Biology, 2012, 159 : 1283 - 1292
  • [26] What can grocery basket data tell us about health consciousness?
    Prasad, Ashutosh
    Strijnev, Andrei
    Zhang, Qin
    INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING, 2008, 25 (04) : 301 - 309
  • [27] What catch data can tell us about the status of global fisheries
    Froese, Rainer
    Zeller, Dirk
    Kleisner, Kristin
    Pauly, Daniel
    MARINE BIOLOGY, 2012, 159 (06) : 1283 - 1292
  • [28] What can paramedic data tell us about the opioid crisis in Canada?
    Do, Minh T.
    Furlong, Greg
    Rietschlin, Micah
    Leyenaar, Matthew
    Nolan, Michael
    Poirier, Pierre
    Field, Brian
    Thompson, Wendy
    HEALTH PROMOTION AND CHRONIC DISEASE PREVENTION IN CANADA-RESEARCH POLICY AND PRACTICE, 2018, 38 (09): : 339 - 342
  • [29] What Can the Data Tell Us about the Equilibrium Real Interest Rate?
    Kiley, Michael T.
    INTERNATIONAL JOURNAL OF CENTRAL BANKING, 2020, 16 (03): : 181 - 209
  • [30] What can data science tell us about finding new superconductors?
    Lookman, Turab
    Lopez-Bezanilla, Alejandro
    PATTERNS, 2022, 3 (11):