What are the factors contributing to the success of Asian students in science learning

Updated: 3:18pm, 8 Nov, 2022
27 May 2016 (Fri)
Room 101, 1/F., Runme Shaw Bldg., HKU
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Based on the archival data extracted from 2012 Programme for International Student Assessment (PISA), the speaker will present malleable factors contributing to the success of Asians in science learning. Numerous studies have been conducted with the goal of learning from the best practices of Asian education. However, most of these studies concentrate on specific areas only (e.g. affective factors, parenting style, teaching methods…etc.) due to the limitations of regression modeling. Additionally, although several theories exist to explain why Asian students excel in science, some of them do not necessarily lead to practical applications. Hence, this presenter adopted a new approach as follows: the 2012 PISA data of science test scores, student variables, home-environment variables, and school-environment variables of Hong Kong, Singapore, Japan, South Korea, Vietnam (top performers in 2012 PISA science test), and USA are analyzed by the bootstrap forest approach, which is a much more flexible and powerful approach than regression modeling. When 399 variables are taken into account simultaneously, the most crucial predictors of success in science learning can be identified and ranked. The top factors of each of the preceding nations and regions will be discussed. For example, student usage of technology out of school is found to be significant predictors of test performance in all Asian samples, except Vietnam.

About the speaker(s):

<p>Chong Ho (Alex) Yu is an Associate Professor of Psychology and the University Quantitative Consultant at Azusa Pacific University, USA. He has a Ph.D. in educational psychology (Arizona State University, ASU) with a concentration on measurement, statistics, and methodological studies, as well as a Ph.D. in philosophy (ASU) with a specialization in history and philosophy of science. His research interests include, but are not limited to, alternate research methods (e.g. exploratory data analysis, data visualization, and data mining), philosophical aspects of research methodologies (e.g. causal inferences), instructional psychology and technology (e.g. multimedia learning), cross-cultural comparison (e.g. PISA, TIMSS), and psychology of religion (e.g. secularization and religiosity).<br /><br /></p>

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