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Positive student outcomes
Office of Education Research, National Institute of Education, Singapore
Chan, M., & Tan, J. (2020). Secondary quantitative analysis of core research data (2004-2010): A multilevel study of academic achievement and 21st century competencies. National Institute of Education (Singapore), Office of Education Research.
The Core Research Programme is a large-scale representative study of teaching, learning and cognitive assessment practices and student outcomes. Within this major project, survey and assessment data were collected across three subsidiary projects. Core 1 Panel 2 (2004) and Core 2 Panel 2 (2010) are two unique datasets that focus on how school, classroom and student level factors contribute to individual variation in student achievement and other key 21st century (21C) learning outcomes. Core 1 Panel 6 (2008), on the other hand, is another study that captures a broader range of affective, educational and psychosocial factors.
The overall objective of this proposed study is to undertake secondary quantitative analyses of student achievement and selected 21C (non-academic) learning outcomes using existing datasets from the Core Research Programme. First, the proposed study will investigate and compare the proportion of variation in student achievement data and background characteristics across Singapore classrooms and schools. Next, we will investigate, identify and compare the extent to which the variability in student achievement is influenced by student, classroom and school level factors. Third, by linking two datasets, the proposed study will investigate the longitudinal impact of Primary Six students’ psychosocial and educational characteristics on their Secondary Three academic achievement and 21C (non-academic) learning outcomes.
The proposed study is important for a number of reasons. First, knowledge of the proportion of variance distributions provides important information for school effectiveness regarding expected shifts in the proportion of variation that can be attributed to different levels of analysis. In broader terms, shifts in variation also provide important information that addresses issues of social and educational equity, given practical difficulties in assessing the direct impact of educational policies. Second, since relationships among different educational variables are often interactional in nature, modelling key contributions of student, class and school level effects separately and simultaneously provides important information about which variable matters most for explaining student achievement, controlling for all other variables considered. Third, observations of individual characteristics and the prospective academic achievement and 21C (non-academic) learning outcomes among the same students over time can provide more robust information about important educational input and process factors.
OER 20/14 CCY
Education Research Funding Programme (ERFP)
Ministry of Education, Singapore
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checked on Nov 24, 2022
checked on Nov 24, 2022
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