Educational data mining with R and Rattle Vol. 21 R. S. Kamath, R. K. Kamat
Por: Kamath, R. S.
Colaborador(es): Kamat, R. K.
Tipo de material: LibroSeries Information science and technology v. 21. Editor: Aalborg River Publishers 2016Descripción: 106 p. gráf. tablas.ISBN: 9788793379312 (Hardback); 9788793379305 (Ebook).Tema(s): MINERIA DE DATOS | EDUCACION | LENGUAJE DE PROGRAMACION | DATA MINING | EDUCATION | PROGRAMMING LENGUAGE | R (lenguaje de parogramación) | RATTLE (lenguaje de programación)Resumen: 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.Tipo de ítem | Ubicación actual | Biblioteca de origen | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
---|---|---|---|---|---|---|---|---|
Libros | Biblioteca Silvina Ocampo (Junín) | Biblioteca Silvina Ocampo (Junín) | GENERAL | 004.6 K152 J05460 (1) (Navegar estantería) | Disponible | J05460 |
Navegando Biblioteca Silvina Ocampo (Junín) Estantes , Código de colección: GENERAL Cerrar el navegador de estanterías
No hay imagen de cubierta disponible | No hay imagen de cubierta disponible | No hay imagen de cubierta disponible | ||||||
004:519.87 K291 J03782 Simulation with Arena | 004.6 A2869 J00601 Estructuras de datos y algoritmos | 004.6 H233 J03759 (1) Data mining | 004.6 K152 J05460 (1) Educational data mining with R and Rattle | 004.6 K932 J04933 (1) Procesamiento de bases de datos: | 004.6 K932 J04934 (2) Procesamiento de bases de datos: | 004.6 M3642 J05030 (1) Organización de las bases de datos |
Contents, list of figures, list of tables, list of abbreviations, references
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.
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