Educational Data Mining with R and Rattle
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A01=R.K. Kamat
A01=R.S. Kamath
academic data analysis
Age Group_Uncategorized
Age Group_Uncategorized
Applying Association Rule Mining
ARFF
Artificial Neural Network
Author_R.K. Kamat
Author_R.S. Kamath
automatic-update
Bayes Classifier
Category1=Non-Fiction
Category=UN
Category=UY
Classifiers
Clustering
Clustering Algorithm
clustering algorithms
Confusion Matrices
COP=Denmark
Data Exploration
Data Items
Data Mining
Data Mining Model
Data Mining Process
Data Mining Project
Data Mining System
Data Mining Techniques
Data Mining Tool
Decision Tree
Delivery_Pre-order
Descriptive and Predictive Analytics
Discriminant Plot
EDM Process
Educational Data Mining
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Hierarchical Clustering
K-Means
Language_English
learning analytics
machine learning education
Mutual Cluster
Na?ve Bayes Classifier
open source data tools
PA=Not yet available
predictive modeling for student outcomes
Price_€20 to €50
PS=Forthcoming
R Systems
Random Forest
Random Forest Classifier
Random Forest Model
Rattle
softlaunch
Student Dataset
student performance prediction
Student Segmentation
Support Vector Machine
Symbolic Data Analysis
Weka Tool
Product details
- ISBN 9788770044738
- Weight: 453g
- Dimensions: 156 x 234mm
- Publication Date: 21 Oct 2024
- Publisher: River Publishers
- Publication City/Country: DK
- Product Form: Paperback
- Language: English
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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
