Data analytics in e-learning : approaches and applications /

Data analytics in e-learning : approaches and applications /

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This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting fr...

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Bibliographic Details
Corporate Authors: Alumni and Friends Memorial Book Fund, EBSCOhost, ProQuest ebook central
Other Authors: Mihăescu, Marian Cristian (Editor), Mihăescu, Marian Cristian (Editor)
Format: Book
Language:English
Published: Cham : Springer, [2022]
Cham, Switzerland : [publisher not identified], [2022]
[Place of publication not identified] : [2022]
Series:Intelligent Systems Reference Library
Intelligent systems reference library ; v. 220
Intelligent systems reference library ; v. 220
Subjects:
Artificial intelligence > Educational applications
Machine learning
Apprentissage automatique
Intelligence artificielle > Applications en éducation
Table of Contents:
  • Intro
  • Preface
  • Contents
  • Introduction to Data Analytics in e-Learning
  • 1 What is Data Analytics?
  • 1.1 Types of Data Analytics
  • 2 Data Analytics and Learning
  • 3 Limitations of Learning Analytics
  • 4 Future Challenges
  • 5 Conclusions
  • References
  • Public Datasets and Data Sources for Educational Data Mining
  • 1 Introduction
  • 2 Related Work: EDM Review Papers
  • 2.1 General EDM Review Papers
  • 2.2 Specific EDM Review Papers
  • 2.3 Findings on EDM Review Papers
  • 3 Review on Other Public Educational Datasets
  • 4 Proposed Methodology for Building Datasets
  • Intro
  • Preface
  • Contents
  • Introduction to Data Analytics in e-Learning
  • 1 What is Data Analytics?
  • 1.1 Types of Data Analytics
  • 2 Data Analytics and Learning
  • 3 Limitations of Learning Analytics
  • 4 Future Challenges
  • 5 Conclusions
  • References
  • Public Datasets and Data Sources for Educational Data Mining
  • 1 Introduction
  • 2 Related Work: EDM Review Papers
  • 2.1 General EDM Review Papers
  • 2.2 Specific EDM Review Papers
  • 2.3 Findings on EDM Review Papers
  • 3 Review on Other Public Educational Datasets
  • 4 Proposed Methodology for Building Datasets
  • 4.1 The Methodology Used for Data Collection
  • 4.2 Structure of the Dataset
  • 5 Conclusions
  • References
  • Building Data Analysis Workflows that Provide Personalized Recommendations for Students
  • 1 Introduction
  • 2 Machine Learning Workflows
  • 3 Inferring Personalized Recommendations by Course Difficulty Prediction and Ranking
  • 4 Personalized Message Recommendation by Usage of Decision Trees
  • 5 Conclusions
  • References
  • Building Interpretable Machine Learning Models with Decision Trees
  • 1 Introduction
  • 2 Related Work
  • 2.1 Background Related to View Techniques for Better Model Analysis
  • 2.2 Related Work for Innovative Ways to Rank Instances
  • 2.3 Related Work on Building Interpretable Models
  • 2.4 Weka
  • 3 Design of the Proposed Techniques
  • 3.1 Design of a View Technique for Better Model Analysis
  • 4 Experiments and Results
  • 4.1 Results on the View Technique for Better Model Analysis
  • 4.2 Short Dataset Example
  • 4.3 Validation of the Procedure of Ranking Instances Based on Leaf Analysis
  • 5 Conclusions
  • References
  • Enhancing Machine Learning Models by Augmenting New Functionalities
  • 1 Introduction
  • 2 Related Work
  • 2.1 Related Work in Student Modelling Based on Text Analysis
  • 1 Introduction
  • 2 Related Work
  • 2.1 Related Work in Student Modelling Based on Text Analysis
  • 3 Design of Improved User Modelling Based on Messages from E-Learning Platforms
  • 3.1 Algorithm Selection for Data Analysis
  • 3.2 Design of New Functionalities to Improve Student Modelling Based on Forum Activity
  • 4 Conclusions
  • References
  • Increasing Engagement in e-Learning Systems
  • 1 Introduction
  • 2 Proposed Approaches for Increasing Engagement
  • 2.1 Modelling Students Based on Their Activity on Social Media Platforms
  • 2.1 Background Related to View Techniques for Better Model Analysis
  • 2.2 Related Work for Innovative Ways to Rank Instances
  • 2.3 Related Work on Building Interpretable Models
  • 2.4 Weka
  • 3 Design of the Proposed Techniques
  • 3.1 Design of a View Technique for Better Model Analysis
  • 4 Experiments and Results
  • 4.1 Results on the View Technique for Better Model Analysis
  • 4.2 Short Dataset Example
  • 4.3 Validation of the Procedure of Ranking Instances Based on Leaf Analysis
  • 5 Conclusions
  • References
  • Enhancing Machine Learning Models by Augmenting New Functionalities
  • 2.2 Finding the Learners that Simulate Activity and Explore the Correlation Between Social Activity and Learning Performance
  • 2.3 Engagement by Alerts
  • 3 Experiments and Results
  • 3.1 Gathering Data from Several Social Media Platforms
  • 3.2 Marking the Learners that Simulate Activity and Analyze the Correlation Between Social Activity and Learning Performance
  • 3.3 Exploring the Impact of Social Media on Students' Performance
  • 4 Conclusions
  • References
  • Usability Evaluation Roadmap for e-Learning Systems
  • 1 Introduction
  • 2 Related Work
  • 3 Design of Improved User Modelling Based on Messages from E-Learning Platforms
  • 3.1 Algorithm Selection for Data Analysis
  • 3.2 Design of New Functionalities to Improve Student Modelling Based on Forum Activity
  • 4 Conclusions
  • References
  • Increasing Engagement in e-Learning Systems
  • 1 Introduction
  • 2 Proposed Approaches for Increasing Engagement
  • 2.1 Modelling Students Based on Their Activity on Social Media Platforms
  • 2.2 Finding the Learners that Simulate Activity and Explore the Correlation Between Social Activity and Learning Performance
  • 2.3 Engagement by Alerts
  • 3 Experiments and Results
  • 3.1 Gathering Data from Several Social Media Platforms
  • 3.2 Marking the Learners that Simulate Activity and Analyze the Correlation Between Social Activity and Learning Performance
  • 3.3 Exploring the Impact of Social Media on Students' Performance
  • 4 Conclusions
  • References
  • Usability Evaluation Roadmap for e-Learning Systems
  • 1 Introduction
  • 2 Related Work
  • 3 Proposed Approaches for Interface Optimisation
  • 3.1 Interface Optimisation by Usability Analysis
  • 3.2 Experiments for Interface Optimisation for Better Usability
  • 3.3 Experiments Obtained from Analysing Key Issues that Influence the Interaction in e-Learning Platforms
  • 3.4 Results Obtained from Exploring How Professors Perceive the Ease of Use of e-Learning Platforms
  • 3.5 Recommending Tutors to Students for Increasing Engagement
  • 4 Conclusions
  • References
  • Developing New Algorithms that Suite Specific Application Requirements
  • 1 Introduction
  • 2 Related Work
  • 3 Building a New Classifier
  • 3.1 The General Architecture of the Classifier
  • 3.2 Implementation of the Classification Algorithm
  • 3.3 Visualization Plugin
  • 3.4 Demo of the New Classifier
  • 3.5 Sample Application: Determining Tutors Using the New Classifier
  • 4 Conclusions
  • 4.1 The Methodology Used for Data Collection
  • 4.2 Structure of the Dataset
  • 5 Conclusions
  • References
  • Building Data Analysis Workflows that Provide Personalized Recommendations for Students
  • 1 Introduction
  • 2 Machine Learning Workflows
  • 3 Inferring Personalized Recommendations by Course Difficulty Prediction and Ranking
  • 4 Personalized Message Recommendation by Usage of Decision Trees
  • 5 Conclusions
  • References
  • Building Interpretable Machine Learning Models with Decision Trees
  • 1 Introduction
  • 2 Related Work
  • References

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