TY - GEN
T1 - Multilevel Analysis of Programme for International Student Assessment (PISA) 2018 in Indonesia
AU - Diaprina, Sistya Rosi
AU - Prastyo, Dedy Dwi
AU - Rahayu, Santi Puteri
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/12/15
Y1 - 2023/12/15
N2 - One goal of education is to produce competent and competitive human beings to realize the nation’s welfare. An assessment is essential to determine whether educational goals are achieved. Effective evaluation needs to be carried out sustainably to obtain overview information on student achievement through a global assessment called the Program for International Student Assessment (PISA) survey. In PISA, the student data nested in schools, resulting in the observed responses being taken tending to be not independent and having variations between schools that are not homogeneous. If modeling is done using linear regression analysis, it can lead to deviations in assumptions, and information related to groups will be neglected. Thus, multilevel modeling is needed to overcome this drawback. This study analyzes the 2018 PISA data in Indonesia, which consists of 3 test domains (Mathematics, Reading, and Science) using multilevel regression analysis. Modeling was carried out using the same predictor variables at the student and school levels with various modeling scenarios for each domain. In PISA, educational achievement is measured through the plausible value so that the modeling will be related to the formulated plausible value. In line with one of the goals of PISA, to encourage the equality of education, several variables related to educational equity are selected. The empirical results show that the factors that affect student achievement in three domains (Reading, Mathematics, and Science) are ESCS (index of economic, social, and culture status), grade repetition, mean ESCS in school, and type of school for significant level 5%. Not all categories of language at home, immigration status, and grade significantly affect student achievement, for the science domain, all category of immigration status has a significant effect. For the mathematics and science domain, the gender factor has no significant effect, but the interaction of ESCS and mean ESCS has a significant effect.
AB - One goal of education is to produce competent and competitive human beings to realize the nation’s welfare. An assessment is essential to determine whether educational goals are achieved. Effective evaluation needs to be carried out sustainably to obtain overview information on student achievement through a global assessment called the Program for International Student Assessment (PISA) survey. In PISA, the student data nested in schools, resulting in the observed responses being taken tending to be not independent and having variations between schools that are not homogeneous. If modeling is done using linear regression analysis, it can lead to deviations in assumptions, and information related to groups will be neglected. Thus, multilevel modeling is needed to overcome this drawback. This study analyzes the 2018 PISA data in Indonesia, which consists of 3 test domains (Mathematics, Reading, and Science) using multilevel regression analysis. Modeling was carried out using the same predictor variables at the student and school levels with various modeling scenarios for each domain. In PISA, educational achievement is measured through the plausible value so that the modeling will be related to the formulated plausible value. In line with one of the goals of PISA, to encourage the equality of education, several variables related to educational equity are selected. The empirical results show that the factors that affect student achievement in three domains (Reading, Mathematics, and Science) are ESCS (index of economic, social, and culture status), grade repetition, mean ESCS in school, and type of school for significant level 5%. Not all categories of language at home, immigration status, and grade significantly affect student achievement, for the science domain, all category of immigration status has a significant effect. For the mathematics and science domain, the gender factor has no significant effect, but the interaction of ESCS and mean ESCS has a significant effect.
KW - Educational Achievement
KW - Multilevel Modeling
KW - Nested Data
KW - PISA Indonesia 2018
KW - Plausible Value
UR - http://www.scopus.com/inward/record.url?scp=85180324548&partnerID=8YFLogxK
U2 - 10.1063/5.0177462
DO - 10.1063/5.0177462
M3 - Conference contribution
AN - SCOPUS:85180324548
T3 - AIP Conference Proceedings
BT - AIP Conference Proceedings
A2 - Sambas, Aceng
A2 - Sukono, null
A2 - Vaidyanathan, Sundarapandian
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Applied Sciences, Technology, Engineering and Mathematics, ICASTEM 2021
Y2 - 2 November 2021 through 3 November 2021
ER -
