Educational Data Mining with R and Rattle

R.S. Kamath, R. K. Kamat

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English

River Publishers
21 October 2024

Databases

Educational Data Mining (EDM) is one of the emerging fields in the pedagogy and andragogy paradigm, it concerns the techniques which research data coming from the educational domain. EDM is a promising discipline which has an imperative impact on predicting students' academic performance. It includes the transformation of existing, and the innovation of new approaches derived from multidisciplinary spheres of influence such as statistics, machine learning, psychometrics, scientific computing etc.

An archetype that is covered in this book is that of learning by example. The intention is that reader will easily be able to replicate the given examples and then adapt them to suit their own needs of teaching-learning. The content of the book is based on the research work undertaken by the authors on the theme ""Mining of Educational Data for the Analysis and Prediction of Students' Academic Performance"". The basic know-how presented in this book can be treated as guide for educational data mining implementation using R and Rattle open source data mining tools. .

Technical topics discussed in the book include:• Emerging Research Directions in Educational Data Mining• Design Aspects and Developmental Framework of the System• Model Development - Building Classifiers• Educational Data Analysis: Clustering Approach

By: R.S. Kamath, R. K. Kamat
Imprint: River Publishers
Country of Publication: Denmark
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9788770044738
ISBN 10: 8770044732
Pages: 126
Publication Date: 21 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Foreword Preface Acknowledgment List of Figures List of Tables List of Abbreviations 1 Introduction 2 Emerging Research Directions in Educational Data Mining 3 Design Aspects and Developmental Framework of the System 4 Model Development—Building Classifiers 5 Educational Data Analysis: Clustering Approach 6 Epilogue and Further Directions References Index About the Authors