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

Pritam Ganguly, PhD, is an Assistant Professor-cum-Junior Scientist in the Department of Soil Science & Agricultural Chemistry, Bihar Agricultural University, India. His expertise is in analyzing trace amounts of organic pollutants in the environment. His key research areas are the monitoring of pesticide residue in food items and prediction of safety risk. He has published research and review articles in reputed journals.

Jajati Mandal, PhD, received an Indian Council of Agricultural Research Netaji Subhas International Fellowship toward his degree at the School of Science Engineering and Environment, University of Salford, UK. His area of research is heavy metal contamination (arsenic) and micronutrients and modeling with machine learning algorithms. He has been the principal investigator of a nationally funded project from the Science Engineering and Research Board (SERB), Department of Science and Technology, Government of India, and two state-funded projects.

Mariappan Paramasivam, PhD, is an Assistant Professor (Soil Science & Agricultural Chemistry) at Tamil Nadu Agricultural University, Coimbatore, India. He has published over 40 research papers, several books, and 10 book chapters. He is listed by TNAU as a top scientist and has also been named Best Participant at the ICAR-sponsored Winter School. He has reviewed research articles for peer-reviewed journals of reputed publishers.

Sandip Patra, PhD, is a Scientist (Agricultural Entomology) in the Division of Crop Science, ICAR Research Complex for North Eastern Hill Region, Meghalaya, India. He has published over 50 research papers and two edited books. He has also authored many popular articles, book chapters, and training manuals. He has received scholarships and several awards for his work. He is a member of various professional societies and reputed journals.