Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Yalçın, Seher

doi:10.21890/ijres.328089

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…For example, students with internet access in 2009 and who use e-mail can be considered at a high socio-economic level (SEL). The finding that SEL plays an important role in student success was also found in several other studies (Finch & Marchant, 2013;Wolfram, 2005;Yalcin, 2017).…”

Section: Discussionsupporting

confidence: 78%

Comparison of PISA and PIAAC Participants’ Reading Habits and Strategies in Terms of Reading Achievement

Yalçın

Bayraktar

2022

Int. J. of Cont. Edu. Res.

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This study aimed to compare reading strategies and habits regarding reading achievement of selected age-group participants from the Programme for International Student Assessment (PISA) 2009 and the International Assessment of Adult Competencies (PIAAC) 2015 within Turkey. A cohort design was utilized because it was assumed that reading literacy from PISA and PIAAC were comparable, as well as that participants from PISA 2009 would be in the age range of participants at the time of PIAAC in 2015. The participant population selected from PISA 2009 for the current study consisted of 4719 Turkish students (2334 girls, 2385 boys) aged 15 years old. Those chosen from PIAAC 2015 consisted of 308 Turkish individuals (141 men, 167 women) aged 20-22 years old. Data analysis was conducted through latent class analysis based on participants' gender, the number of books in the home, and reading achievement. Results revealed that female participants were more successful than male participants in PISA, whereas male participants outperformed female participants in PIAAC.

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Section: Discussionsupporting

confidence: 78%

Comparison of PISA and PIAAC Participants’ Reading Habits and Strategies in Terms of Reading Achievement

Yalçın

Bayraktar

2022

Int. J. of Cont. Edu. Res.

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“…The learner's school achievement and performance is a primary gauge of a society's efficient education status both in the developed and developing countries. As such, international assessments and evaluation of the academic index largely affects the policies and decisions of countries around the world to further enhance their communities and to achieve their collective goals (Yalcin, 2017). Thus, the existence of the regular national achievement tests, and other international standardized evaluations such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS).…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Social Learning-Related Variables on Academic Achievement

Magsino¹

2021

IJEMDS

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This study determined the relationship between the social learning-related variables and the skills achievement in Mathematics of the 200 Grade 8 students in a public school in the Philippines. Descriptive research design was used through a self-constructed questionnaire and an assessment test. The data gathered were presented in a tabular mean while the relationship between variables was tested using Pearson r correlation coefficient. The students' social learning-related variables were highly manifested (WAM = 3.44, SD = 1.11). However, only 11.5% of the students have satisfactory performance in Mathematics. Among the demographic profile, only mothers' educational attainment has shown significant relationship with mathematics achievement in terms of estimation (r = 0.184). Among the social learning-related variables, attitude of the students indicated significant relationship with both estimation (r = 0.184) and problem solving (r = 0.196). Peer support also revealed significant relationship with both representation (r = 0.167) and estimation (r = 0.159). The mathematics teacher must find effective ways on how to overcome the students' difficulties and thus develop their mathematical skills. Efforts to thresh out problems in vocabulary development especially solving worded problems may be exerted. A parallel study may be conducted using variables not considered in this research.

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“…To remediate the shortcomings of chi-square based tests, a series of alternative fit indices (with corresponding cut-off criteria) has been developed. West, Taylor and Wu (2012 [13]), for example, suggest relying on the root mean square error of approximation (RMSEA) and the comparative fit index (CFI). Simulations suggest that well-fitting models should provide RMSEA values which are smaller than 0.06 and CFI values which are higher than 0.95 (Hu and Bentler, 1999[14]).…”

Section: Equation 23mentioning

confidence: 99%

“…This can be done by plugging Equation 4. 13 In this marginal model, the latent response variable no longer depends on the group-specific item parameter and one item difficulty parameter (i.e. the measurement invariant item difficulty) applies to all groups.…”

Section: The Marginal Modelling Approachmentioning

confidence: 99%

Invariance analyses in large-scale studies

Vijver¹,

Avvisati

Davidov

et al. 2019

OECD Education Working Papers

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OECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed herein are those of the author(s). Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcome, and may be sent to the Directorate for Education and Skills, OECD,

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Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Abstract: Article History

Cited by 7 publications

References 27 publications

Comparison of PISA and PIAAC Participants’ Reading Habits and Strategies in Terms of Reading Achievement

Comparison of PISA and PIAAC Participants’ Reading Habits and Strategies in Terms of Reading Achievement

The Effect of Social Learning-Related Variables on Academic Achievement

Invariance analyses in large-scale studies

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