Gender Differences in Large-Scale Math Assessments: PISA Trend 2000 and 2003

Liu, Ou Lydia; Wilson, Mark

doi:10.1080/08957340902754635

Cited by 68 publications

(59 citation statements)

References 47 publications

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“…Achievement differences have traditionally been visible in large-scale studies and high-stakes achievement tests such as examinations and national tests (e.g., Liu and Wilson 2009;OECD 2013a). Male students usually outperform female students, although the reverse pattern is visible in some instances.…”

Section: Equity Issuesmentioning

confidence: 99%

Assessment in mathematics education: responding to issues regarding methodology, policy, and equity

Nortvedt

Buchholtz

2018

ZDM Mathematics Education

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In educational contexts, assessments may be designed to target students, preservice teachers, or teachers, either as individuals or as representatives of a group, and for a multitude of purposes. One key aim of assessment in mathematics education is to provide evidence that can be used to make decisions about or improve mathematics education, which then raises questions about which aspects of mathematical competence should be assessed-as well as how and for what purpose. This review paper addresses three related themes: (1) issues related to the assessment process and to the development of assessments that can validly assess mathematical competence in all its complexity; (2) issues related to educational policy and policy-making based on assessment data, in particular the reciprocal relationship between assessment and policy; and (3) issues related to equity, such as gender issues or the achievement gap between majority and minority students. Awareness of the relation between assessment, teaching, and learning is shown throughout the paper. Strong relationships between the three focus areas are found, that impact assessment validity and call for further development of assessment practices in mathematics education.

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Section: Equity Issuesmentioning

confidence: 99%

Assessment in mathematics education: responding to issues regarding methodology, policy, and equity

Nortvedt

Buchholtz

2018

ZDM Mathematics Education

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“…Propensity scoring provided us with a new tool to better control sources of item bias. In our case, controlling gender, socioeconomic status and opportunity to learn were effective in identifying sources, as they were important predictors of mathematics performance in PISA (Areepattamannil, 2014;Kilic, Cene, & Demir, 2012;Liu & Wilson, 2009;Machin & Pekkarinen, 2008;Perry & McConney, 2010;Schmidt, Zoido, & Cogan, 2014). At a conceptual level, the findings suggest that diminished DIF items are induced by these background variables and not by other cultural differences.…”

Section: Discussionmentioning

confidence: 99%

“…For example, different studies showed that boys are more successful than girls in PISA mathematics test (Areepattamannil, 2014;Liu & Wilson, 2009;Machin & Pekkarinen, 2008). In PISA 2012, boys performed better than girls by 11 points; out of 65 countries, in 38 countries boys performed better than girls whereas in 5 countries girls performed better than boys (OECD, 2014a).…”

Section: Differential Item Functioningmentioning

confidence: 99%

Propensity Score Matching Helps to Understand Sources of DIF and Mathematics Performance Differences of Indonesian, Turkish, Australian, and Dutch Students in PISA

Arıkan¹,

Vijver²,

Yağmur³

2018

International Journal of Research in Education and Science

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We examined Differential Item Functioning (DIF) and the size of cross-cultural performance differences in the Programme for International Student Assessment (PISA) 2012 mathematics data before and after application of propensity score matching. The mathematics performance of Indonesian, Turkish, Australian, and Dutch students on released items was compared. Matching variables were gender, an index of economic, social and cultural status, and opportunity to learn, in exact, nearest neighbor, and optimal matching. Logistic regression and structural equation modeling were used to identify DIF. If propensity scores were used in the DIF analyses as performance predictors, much less DIF was found than in the original data; similarly, when in tests of country differences in mathematics performance, propensity scores were used as covariates, effect sizes of tests of country differences were reduced substantially. We concluded that propensity scoring provided us with a new tool to better control sources of DIF and country differences in PISA mathematics performance.

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“…Gerek Türkiye gerekse uygulamaya katılan diğer ülkelerde öğrencilerin matematik, fen ve okuma alanlarındaki akademik başarılarını etkileyen faktörleri belirlemek (Anıl, 2009;Demir ve Kılıç, 2010;Sun, Bradley ve Akers, 2012;Şengül, 2011;Yıldırım, 2012), birtakım değişkenler açısından bazı ülkeler arasında karşılaştırmalar yapmak (Aydın, Erdağ ve Taş, 2011;Brozo, Shiel ve Topping, 2007;Cromley, 2009;Guo, 2014;İş Güzel, 2006), madde yanlılığı çalışmaları ile uygulamaların geçerliğini ortaya koymak (Ayan, 2011;Yıldırım ve Berberoğlu, 2009;Le, 2009;Liu ve Wilson, 2009;Lyons-Thomas, Sandilands ve Ercikan, 2014) ve ülkelerin benzerlik ve farklılıklarını tespit etmek (Acar, 2012;Kjaernsli ve Lie, 2004;Soh, 2014;Zhang, Khan ve Tahirsylaj, 2015) amacıyla çok sayıda araştırma yapılmıştır. PISA uygulamalarındaki değerlendirme çerçevesinin 21.…”

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Evaluation of Reading Skills Subtest Results of Countries Participating in the PISA 2009 Application via Profile Analysis

Saatçioğlu

Gülleroğlu

2017

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Gender Differences in Large-Scale Math Assessments: PISA Trend 2000 and 2003

Cited by 68 publications

References 47 publications

Assessment in mathematics education: responding to issues regarding methodology, policy, and equity

Assessment in mathematics education: responding to issues regarding methodology, policy, and equity

Propensity Score Matching Helps to Understand Sources of DIF and Mathematics Performance Differences of Indonesian, Turkish, Australian, and Dutch Students in PISA

Evaluation of Reading Skills Subtest Results of Countries Participating in the PISA 2009 Application via Profile Analysis

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