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Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data

Artigo2022

Fani Yunida Anggraheni; Kismiantini Kismiantini; Fajar Ediyanto

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Resumo

This study aims to examine the relationship between predictor variables at the student and school levels and the interaction between variables in predicting mathematics achievement in Indonesia. Stratified analysis was implemented in Indonesia’s Programme for International Student Assessment (PISA) 2018 data. The variables of student level encompassed gender, economic, social, and cultural status (ESCS), metacognition, and learning time. This study revealed that the variables of ESCS, metacognition and learning time possessed a significant positive effect on mathematics achievement. The variables of school level are class size, school type, school size, and student-teacher ratio. This study demonstrated that only the data of class size produced a significant effect on mathematics achievement. Furthermore, the interaction between the learning time and class size also significantly affected learning achievement in mathematics. Therefore, variables increasing students’ mathematics achievement are ESCS, metacognition, learning time, class size, and interaction of learning time and class size.

Dados da obra

Tipo
Artigo
Periódico
Southeast Asian Mathematics Education Journal, v. 12, n. 2, p. 95-104
Editora
SEAMEO Regional Centre for QITEP in Mathematics
Ano
2022 (2022-12)
Idioma
en
Palavras-chave
multilevel modelling; mathematics achievement; pisa data; student level; school level
Temas
Avaliação
DOI
10.46517/seamej.v12i2.184
ISSN
2089-4716, 2721-8546
Acesso
Acesso aberto
Fontes
DOAJ — Directory of Open Access Journals
Na Biblioteca desde
01/10/2026 · verificada em 03/10/2026

Como citar

ABNT
ANGGRAHENI, Fani Yunida; KISMIANTINI, Kismiantini; EDIYANTO, Fajar. Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data. Southeast Asian Mathematics Education Journal, v. 12, n. 2, p. 95-104, 2022. DOI: https://doi.org/10.46517/seamej.v12i2.184.

APA

Anggraheni, F. Y., Kismiantini, K., & Ediyanto, F. (2022). Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data. Southeast Asian Mathematics Education Journal, 12(2), 95-104. https://doi.org/10.46517/seamej.v12i2.184

BibTeX
@article{anggraheni2022,
  author = {Anggraheni, Fani Yunida and Kismiantini, Kismiantini and Ediyanto, Fajar},
  title = {Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data},
  year = {2022},
  journal = {Southeast Asian Mathematics Education Journal},
  volume = {12},
  number = {2},
  pages = {95-104},
  publisher = {SEAMEO Regional Centre for QITEP in Mathematics},
  doi = {10.46517/seamej.v12i2.184},
  language = {en}
}
RIS
TY  - JOUR
AU  - Anggraheni, Fani Yunida
AU  - Kismiantini, Kismiantini
AU  - Ediyanto, Fajar
TI  - Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data
PY  - 2022
JO  - Southeast Asian Mathematics Education Journal
VL  - 12
IS  - 2
SP  - 95
EP  - 104
DO  - 10.46517/seamej.v12i2.184
LA  - en
ER  -

Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS

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