Examining Science Achievement in Chile: A Multilevel Model Approach

Diaz, Noelia Pacheco; Rocconi, Louis M.

doi:10.51355/jstem.2021.100

Cited by 2 publications

(1 citation statement)

References 36 publications

(46 reference statements)

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“…This segregation has consequences for students' academic life in Chile, especially in the distribution of access to higher education. Focusing on science, Diaz and Rocconi [40] conducted a multilevel analysis of the PISA test scores of 15-year-old students and observed significant gaps. Socioeconomic status correlates to science achievement, expectations, and self-efficacy.…”

Section: B Inequalities In Computational Thinking and Stemmentioning

confidence: 99%

Inequalities in Computational Thinking Among Incoming Students in an STEM Chilean University

González-Pizarro,

López,

Vásquez

et al. 2024

IEEE Trans. Educ.

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While computational thinking arises as an essential skill worldwide, formal primary and secondary education in Latin America rarely incorporates mechanisms to develop it in their curricula. The extent to which students in the region acquire computational thinking skills remains largely unknown. To start addressing this void, this article presents findings from a cross sectional study that characterizes the computational thinking abilities of incoming students at a Chilean university with a strong emphasis on STEM disciplines. Based on more than 500 responses, this study provides evidence of significant inequalities in computational thinking across gender, type of school (private or no), and prior programming knowledge. The discussion offers insights into how these disparities relate to contextual factors of the country, such as a highly socio-economically segregated educational system, public policies focused mainly on technology access, and heavy reliance on voluntary initiatives, to develop computational thinking. The findings can enlighten upcoming research endeavors and formulate strategies to create a more equitable field for students entering STEM degrees in nations facing similar circumstances.

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Section: B Inequalities In Computational Thinking and Stemmentioning

confidence: 99%

Inequalities in Computational Thinking Among Incoming Students in an STEM Chilean University

González-Pizarro,

López,

Vásquez

et al. 2024

IEEE Trans. Educ.

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The Effect of Sample Weighting on Hierarchical Linear Modeling in the Large-Scale Assessment Data

Güngör,

Bekmezci,

Doğan

2024

Erzincan Üniversitesi Eğitim Fakültesi Dergisi

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This study examines how the different uses of sampling weights in the analysis of TIMSS 2019 data affects the proportion of variance in student achievement explained by schools and the estimation of standard errors. The research sample comprises 227,345 8th-grade students from 7,636 schools in 39 countries. Mathematics achievement and science achievement are considered separately as output variables in all 39 countries. All plausible values are included in the analysis. Four weighting scenarios are examined: no weighting, weighting at only level 1, weighting at only level 2, and weighting at both levels. In total, 312 models are established and examined. According to the research results, the coefficients, standard errors, reliabilities, and Chi-square estimations change depending on how the weighting variable is handled in the models, and as a result, the portion of variance in the output variable arising from the differences between schools also changes. The portion attributable to between-school differences can reach up to 20% in some countries. Therefore, researchers modeling hierarchical data using HLM are advised to plan how they handle the weighting variable prior to conducting the study.

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Examining Science Achievement in Chile: A Multilevel Model Approach

Cited by 2 publications

References 36 publications

Inequalities in Computational Thinking Among Incoming Students in an STEM Chilean University

Inequalities in Computational Thinking Among Incoming Students in an STEM Chilean University

The Effect of Sample Weighting on Hierarchical Linear Modeling in the Large-Scale Assessment Data

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