Assessing Students' Digital Reading Performance

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A01=Jie Hu
Author_Jie Hu
Category=CFDM
Category=JNA
Category=JNLC
Category=JNM
Classroom Level Factors
Country Level Factors
Digital Education Resources
Digital Reading
Digital Reading Environment
Dynamic Text
Educational Data Mining
educational effectiveness model
Educational Technology
EER
eq_bestseller
eq_dictionaries-language-reference
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Extra-curricular
hierarchical linear modelling
High SES School
ICT Resource
IDI
international education assessment
Low SES School
machine learning in reading research
mediation analysis
Optimal Factor Set
Optimal Feature Set
PISA
PISA Cycle
PISA Dataset
PISA Reading
Reading Education
Reading Literacy Assessment
Reading Self-efficacy
Reconciliation Role
School Level Factors
socioeconomic factors in education
Student Level Factors
student performance analytics
SVM
SVM Model
SVM RFE

Product details

  • ISBN 9781032397306
  • Weight: 480g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Dec 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

Jie HU is Professor of the Department of Linguistics at the School of International Studies at Zhejiang University, China. Her research interests include PISA/PIRLS Reading Test, Digital Reading, and Educational Technology.

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