This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses.

Rev. ed. of: PISA 2003 data analysis manual. SPSS users. c2005.

This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It includes worked examples providing full syntax in SAS®.

This edited collection challenges the common preoccupation with knowledge acquisition and academic achievement by comparing the aims and cultural beliefs which drive education in different countries throughout the world. Through case studies from countries in Asia, Latin America, Africa, and Europe, the authors present how education can be approached holistically to foster student happiness and well-being. The book illustrates wide-ranging interpretations of what it means to provide a "good education," and how student-centered, holistic approaches to learning can be effective in promoting creativity, tolerance, student well-being, and an appreciation of environmental and societal responsibilities. Based on rigorous mixed-method empirical research, it highlights how the integration of happiness in education can not only enhance academic excellence but can also have a positive impact on the students’ overall well-being. This cutting-edge book focuses on the holistic development and well-being of students and will be a relevant reading for educators, researchers, and students in such diverse fields as psychology, the sociology and philosophy of education, intercultural education, education policy and politics, leadership/management, mental health, and international and comparative education.

This volume offers a critical examination of the Programme for International Students Assessment (PISA), focusing on its origins and implementation, relationship to other international large-scale assessments, and its impacts on educational policy and reform at national and cross-national levels. Using empirical data gathered from a research project carried out by the CeiED at Lusofona University, Lisbon, the text highlights connections between PISA and emergent issues including the international circulation of big science, expertise and policy, and identifies its conceptual and methodological limits as a global governance project. The volume ultimately provides a novel framework for understanding how OECD priorities are manifested through a regulatory instrument based on Human and Knowledge Capital Theory, and so makes a powerful case to search for new humanistic approaches. This text will benefit researchers, academics and educators with an interest in education policy and politics, international and comparative education, and the sociology of education more broadly. Those interested in the history of education will also benefit from this volume.

This book provides a systematic study of the Programme for International Student Assessment (PISA) based on big data analysis, aiming to examine the contextual factors relevant to students’ digital reading performance. The author first introduces the research landscape of educational data mining (EDM) and reviews the PISA framework since its launch and how it has become an important metric to assess the knowledge and skills of students from across the globe. With a focus on methodology and its applications, the book explores extant scholarship on the dynamic model of educational effectiveness, multi-level factors of digital reading performance, and the application of EDM approaches. The core chapter on the methodology examines machine learning algorithms, hierarchical linear modeling, mediation analysis, and data extraction and processing for the PISA dataset. The findings give insights into the influencing factors of students’ digital reading performance, allowing for further investigations on improving students’ digital reading literacy and more attention to the advancement of education effectiveness. The book will appeal to scholars, professionals, and policymakers interested in reading education, educational data mining, educational technology, and PISA, as well as students learning how to utilize machine learning algorithms in examining the mass global database.