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Examining Student, Parent, and School Factors Predicting Science Achievement Using a Multilevel Approach: The Case of Hong Kong from the Program for International Student Assessment 2015
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Benjamin Yuet Man Li; Yejun Bae; Yi-Jhen Wu; Chia-Wen Chen; Yi-Jung Wu – Asia Pacific Education Review, 2025
This study utilizes the 2015 data from the Hong Kong sample of the Program for International Student Assessment to examine the relative importance of various factors on the science learning performance of Hong Kong students. Using hierarchical linear modeling (HLM), we examined the effect of students' affective characteristics, involvement, and…
Descriptors: Predictor Variables, Science Achievement, Foreign Countries, Achievement Tests
Machine Learning Procedures for Predictor Variable Selection for Schoolwork-Related Anxiety: Evidence from PISA 2015 Mathematics, Reading, and Science Assessments
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Immekus, Jason C.; Jeong, Tai-sun; Yoo, Jin Eun – Large-scale Assessments in Education, 2022
Large-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized…
Descriptors: Foreign Countries, Secondary School Students, Artificial Intelligence, Educational Technology
Cross-National Study on Students and School Factors Affecting Science Literacy
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Altun, Aysegül; Kalkan, Ömür Kaya – Educational Studies, 2021
This study aimed to comparatively review student-level and school-level factors affecting scientific literacy in Singapore, Italy and Turkey. Two-level hierarchical linear modelling (HLM) was applied to the 2015 Programme for International Student Assessment data set. The findings show that (a) the enjoyment of science, interest in broad science…
Descriptors: Scientific Literacy, Scientific Attitudes, Science Interests, Test Anxiety
Comparison of Artificial Neural Networks and Logistic Regression Analysis in PISA Science Literacy Success Prediction
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Bozak, Ali; Aybek, Eren Can – International Journal of Contemporary Educational Research, 2020
The present study aims to determine which analysis technique-Artificial Neural Networks (ANNs) or Logistic Regression (LR) Analysis-is better at predicting the science literacy success of the 15-year Turkish students who participated in PISA research carried out in 2015 by using learning time spent on science, test anxiety, environmental…
Descriptors: Artificial Intelligence, Networks, Regression (Statistics), Achievement Tests