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.

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.

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.