Educational Data Mining with R and Rattle.
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 in...
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| Format: | Electronic eBook |
| Language: | English |
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Aalborg :
River Publishers,
2016.
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| Series: | River Publishers series in information science and technology.
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- Cover; Half Titlle page; River Publishers Series Page; Title Page
- Educational Data Mining with R and Rattle; Copyright Page; Contents; Foreword; Preface; Acknowledgment; List of Figures; List of Tables; List of Abbreviations; Chapter 1
- Introduction; 1.1 Introduction; 1.2 Data Mining; 1.2.1 System Architecture; 1.2.2 Mining Process; 1.2.3 Functions and Products; 1.2.4 Significance and Applications; 1.3 Educational Data Mining-An Area under the Umbrella of Data Mining; 1.3.1 EDMTasks; 1.3.2 Techniques; 1.4 Research Problem; 1.4.1 Research Motivation; 1.4.2 Problem Statement.
- 1.4.3 Objectives1.5 R Data Mining Tool; 1.5.1 R Installation; 1.5.2 R Mining; 1.6 Rattle Data Mining Tool; 1.6.1 Rattle Installation; 1.6.2 Loading Rattle Package; 1.7 Reason for R and Rattle; Chapter 2
- Emerging Research Directions in Educational Data Mining; 2.1 Introduction; 2.2 Prior Art Vis-à-vis of Research; 2.2.1 Educational Data Mining; 2.2.2 Data Mining Using R; 2.2.3 Mining Students' Academic Performance; 2.2.4 Factors Affecting on Students' Academic Performance; 2.2.5 Evaluation of Student Performance; 2.2.6 Knowledge Management System; 2.2.7 Placement Chance Prediction.
- 2.2.8 Mining Association Rules in Student's Data2.2.9 Clustering Data Mining; 2.2.10 Prediction for Student's Performance Using Classification Method; 2.2.11 Classification Techniques; 2.2.12 Educational Data Mining Model Using Rattle; 2.3 Conclusion; Chapter 3
- Design Aspects and Developmental Framework of the System; 3.1 Introduction; 3.2 EDM Phases and Research Framework; 3.3 Methods of Educational Data Mining; 3.4 Algorithms and Tools; 3.5 Data Mining Process; 3.5.1 Data Collection; 3.5.2 Data Preprocessing and Transformation; 3.5.3 R Packages and Functions for Data Mining.
- 3.5.4 Result Evaluation and Knowledge Presentation3.6 Working with Data; 3.7 Research Methodology; 3.8 Loading and Exploring Data-Exploratory Data Analysis; 3.9 Interactive Graphics and Data Visualization; 3.10 Conclusion; Chapter 4
- Model Development-Building Classifiers; 4.1 Introduction-Descriptive and Predictive Analytics; 4.2 Predictive Analytics; 4.3 Dataset and Class Labels; 4.4 Classification Framework and Process; 4.5 Predicting Students' Performance; 4.6 Classification and Predictive Modeling in R and Rattle; 4.7 Decision Tree Modeling; 4.7.1 Decision Tree Implementation in R.
- 4.7.2 Decision Tree in Rattle4.8 Artificial Neural Network Classifier; 4.9 Naive Bayes Classifier; 4.10 Random Forest Modeling; 4.10.1 Random Forest Model in R; 4.10.2 Random Forest Implementation in Rattle; 4.11 Model Selection and Deployment; 4.11.1 Model Evaluation in R; 4.11.2 Model Evaluation in Rattle; 4.12 Conclusion; Chapter 5
- Educational Data Analysis: Clustering Approach; 5.1 Introduction; 5.2 Clustering in Educational Data Mining; 5.3 Experimental Setup; 5.4 Clustering Techniques; 5.5 Classification via Clustering-Design Framework; 5.6 Cluster Analysis in R and Rattle.
