TY - JOUR
T1 - First cycle of pisa (2000-2006)-international perspectives on successes and challenges
T2 - Research and policy directions
AU - Anderson, John O.
AU - Chiu, Mei Hung
AU - Yore, Larry D.
N1 - Funding Information:
The Guest Editors wish to thank the Editors of the International Journal of Science and Mathematics Education for the opportunity to do this special issue. Furthermore, we wish to recognise the financial support from the Natural Sciences and Engineering Research Council, Canada and the National Science Council of Taiwan. The contents of the special issue are not an official position of these funding agencies. The Guest Editors want to thank the reviewers for this special issue: Len Annetta, North Carolina State University Andy Cavagnetto, New York State University, Binghamton Robert Crocker, Memorial University of Newfoundland Justin Dillon, King’s College, London Susan Everett, University of Michigan, Dearborn Leslee Francis Pelton, University of Victoria Murat Gunel, Ahi Evran University Laura Henriques, California State University, Long Beach Don Klinger, Queen’s University Yew-Jin Lee, National Institute of Education, Singapore Jonathan Osborne, Stanford University Tim Pelton, University of Victoria Mack Shelley II, Iowa State University Christine Tippett, University of Victoria Bruce Waldrip, Monash University, Gippsland Finally, the Guest Editors want to recognise and thank Sharyl A. Yore for mentoring the authors and technical editing the manuscripts for this special issue.
Funding Information:
The Correlates of Leaning Outcomes (COLO) project of the Centres of Research in Youth, Science Teaching and Learning (CRYSTAL) Pacific funded by the Natural Sciences and Engineering Research Council, Canada have used these datasets to explore national and international relationships, models and comparisons of mathematical and scientific literacies, and various student, school, country, and culture attributes (Anderson, Milford & Ross, 2009). Gu (2006) examined and compared the relationships among students’ self-beliefs in mathematics, learning environment at school, and mathematics achievement at student and school levels in Canada and Hong Kong-China. Hierarchical linear modeling (HLM) of the PISA 2003 data revealed that school learning environment has more effect on school mathematics achievement in Hong Kong than in Canada. The Canada model has stronger relationships between students’ self-beliefs in mathematics and their mathematics performance than the Hong Kong model. School variations of self-efficacy and self-concept effects are accountable by school learning environment in Hong Kong but not in Canada. Hsu (2007) investigated and compared the effects of student characteristics (i.e. socioeconomic status, gender, family structure, and immigration background) on mathematics achievement in Canada and Hong Kong. The HLM results showed that schools in Canada and Hong Kong accounted for 20% and 49% of the variance in mathematics achievement, respectively. All student-level variables except family structure were significant in the Canada model whereas only gender and immigration background were significant in the Hong Kong model. At school level, the significant school aggregate variables had much larger effects on school average mathematics achievement in Hong Kong than in Canada. The findings suggest that school composition has an effect on mathematics achievement over and above that of individual student characteristics.
PY - 2010
Y1 - 2010
N2 - This special edition of IJMSE focuses on the Programme of International Student Assessment (PISA) project now that it has completed a full cycle of administration-reading, mathematics, and science-to look at ways in which PISA has been used in participating countries and with what consequences, and to identify potential research and policy directions emanating from this initiative. Articles were invited to (a) reflect international perspectives on the uses and consequences of PISA to date and (b) speculate on future directions for research, curriculum, and policy using the PISA datasets. The introductory article provides a brief overview of common aspects of PISA: Evolving definitions of reading literacy, mathematics literacy, and science literacy; technical design of the instruments and data analysis procedures; the changing emphasis of administrations; and recent research using the datasets. PISA, unlike other international assessments in reading, mathematics, and science, has provided a fresh perspective on 'what might be' by decoupling the assessment from mandated curricula to focus on literacies needed for a 21st century economy. This unique feature of PISA brings with it possibilities and cautions for policy makers.
AB - This special edition of IJMSE focuses on the Programme of International Student Assessment (PISA) project now that it has completed a full cycle of administration-reading, mathematics, and science-to look at ways in which PISA has been used in participating countries and with what consequences, and to identify potential research and policy directions emanating from this initiative. Articles were invited to (a) reflect international perspectives on the uses and consequences of PISA to date and (b) speculate on future directions for research, curriculum, and policy using the PISA datasets. The introductory article provides a brief overview of common aspects of PISA: Evolving definitions of reading literacy, mathematics literacy, and science literacy; technical design of the instruments and data analysis procedures; the changing emphasis of administrations; and recent research using the datasets. PISA, unlike other international assessments in reading, mathematics, and science, has provided a fresh perspective on 'what might be' by decoupling the assessment from mandated curricula to focus on literacies needed for a 21st century economy. This unique feature of PISA brings with it possibilities and cautions for policy makers.
KW - Contemporary literacies
KW - Hierarchical linear modelling
KW - Policy directions
KW - Programme of International Student Assessment (PISA)
KW - Secondary analyses
KW - Technical aspects
UR - https://www.scopus.com/pages/publications/77952320207
UR - https://www.scopus.com/pages/publications/77952320207#tab=citedBy
U2 - 10.1007/s10763-010-9210-y
DO - 10.1007/s10763-010-9210-y
M3 - Article
AN - SCOPUS:77952320207
SN - 1571-0068
VL - 8
SP - 373
EP - 388
JO - International Journal of Science and Mathematics Education
JF - International Journal of Science and Mathematics Education
IS - 3
ER -