Modern Approach to Educational Data Mining and Its Applications (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Soni Sweta

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 117
• Category: Library

✦ Synopsis

This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life’s problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners’ preferences minutely and provide adaptive learning materials to them in a way most appropriately.

✦ Table of Contents

Foreword
Preface
Acknowledgments
Contents
About the Author
Abbreviations
Symbols
1 Educational Data Mining in E-Learning System
1.1 Introduction to Educational Data Mining (EDM)
1.2 Technology Enhancement Educational Data Mining
1.3 Applications of EDM [6, 7]
1.4 Data Mining Outlook in Business and Educational Domain
1.5 Terminology Used in EDM
1.6 E-Learning System
1.7 Evolution of Education System
1.8 Research Area in E-Learning
1.9 Limitation of E-Learning
References
2 Adaptive E-Learning System
2.1 Overview of Adaptive E-Learning System
2.2 Adaptive E-Learning
2.3 Adaptive E-Learning Systems
2.4 Intelligent Versus Adaptive
2.5 Adaptive E-Learning in Terms of Data Mining
2.6 Application Area of Adaptive E-Learning
2.7 Adaptive Parameters in E-Learning?
2.8 Adaptive LMS System Functionality (SF)
2.9 Learning Management System
2.10 Adaptivity in Learning Management Systems
2.11 Kinds of Adaptivity
2.12 Why Adaptation is Required?
2.13 Personalization with Adaptivity
2.14 Adaptive E-Learning Its Scope and Challenges
2.15 Scope—A Tool to Manage Shortcomings of E-Learning
2.16 Some Existing Challenges
2.17 Process of Adaptation
References
3 Educational Data Mining Techniques with Modern Approach
3.1 Introduction
3.2 Data Mining in Terms of Adaptive E-Learning and Web Personalization
3.2.1 Soft Computing Techniques in Data Mining
3.2.2 Advantages of Soft Computing
3.3 Soft Computing Techniques in Personalized Adaptive E-Learning System
3.3.1 Comparative Analysis of Expert Systems, Fuzzy Systems, Neural Networks, and Genetic Algorithms
3.3.2 Intelligent Hybrid System
3.3.3 Neuro-Fuzzy Approaches
3.3.4 Advantages of Combination of Neuro-Fuzzy
3.4 Fuzzy Cognitive Map (FCM)
3.4.1 Application of Fuzzy Logic in Education
3.4.2 Advantages and Disadvantages of Fuzzy Logic
3.5 Data-Driven Approach Versus Literature-Based Approach
3.5.1 The Data-Driven Approach
3.5.2 The Literature-Based Approach
3.6 Learner Modeling Techniques for Personalized and Adaptive E-Learning
3.6.1 Bayesian Belief Network
3.6.2 Fuzzy Logic-Based Technique
3.6.3 Neural Network-Based Techniques
3.6.4 Fuzzy Clustering-Based Techniques
3.7 Learning Style-Based Individualized Adaptive E-Learning
References
4 Learning Style with Cognitive Approach
4.1 Learning Style with Learning Theory
4.1.1 Learning Style
4.1.2 Learning Styles Theories
4.1.3 Six Prominent Learning Style Models
4.1.4 Discussion on Existing Personalized Adaptive E-Learning System
4.2 Comparative Analysis of Learner Modeling Techniques
References
5 Framework for Adaptive E-Learning System
5.1 Introduction
5.2 An Adaptive Framework
5.2.1 Learner Profile and Interface Module (LPIM)
5.2.2 Behavior Monitoring Module-BMM
5.2.3 Learning Style Diagnostic Module-LSDM
5.2.4 Personalized Adaptive Module-PAM
5.3 Workflow of Adaptive System Components
5.4 Adaptable Characteristics for Learner Model with System Process
5.5 Factors Affecting on Personalization
5.6 Factors Affecting Adaptation
References
6 Personalization Based on Learning Preference
6.1 Introduction
6.2 Fuzzy Cognitive Maps
6.2.1 FCM Outlines and Narration
6.2.2 Knowledge Representation with FCM
6.2.3 Different Mathematical Representation of Fuzzy Cognitive Maps
6.2.4 Structure and Learning from FCM Method I
6.2.5 FCM: Method II
6.2.6 FCM: Method III
6.3 The Felder–Silverman Learning Style Model
6.3.1 Scales of the Dimensions
6.3.2 Learner Preferences in Five Dimensions
6.3.3 Index of Learning Style (ILS) Scale
6.3.4 Combination of Learning Style [19]
6.3.5 Annotating Learning Objects
6.4 Learner Modeling Based on Fuzzy Cognitive Map (FCM)
6.4.1 Diagnose Learner Profile
6.4.2 Collecting and Processing Information
6.4.3 Input Layer
6.4.4 Output Layer
6.5 Labeling Learning Objects
6.6 Automatic Detection of Learner’s Characteristics in E-Learning
6.6.1 Behavior Monitoring Module-BMM
6.6.2 Auto-Diagnosis of Learner’s LS in E-Learning
6.7 Measurements of Concepts Values and Their Strengthens Casual Relationship
6.8 Fuzzy Rule Base in Terms of Adaptive Rules
References
7 Recommender System to Enhancing Efficacy of E-Learning System
7.1 Introduction
7.2 Personalized Adaptive Module-PAM
7.2.1 Calculating Motivation (High/Low)
7.2.2 Calculating Knowledge Ability (Low/Medium/High)
7.3 Overview of Implementation of a Novel Framework (NAPF)
7.4 Fuzzy Inference Systems (FIS)
7.4.1 Sugeno Fuzzy Inference Method
7.5 ANFIS Editor and Training
7.6 Model Training
7.7 Validation of Model
References