Data analytics in e-learning : approaches and applications / edited by Marian Cristian Mihăescu.

Format:

Book

Contributor:

Mihăescu, Marian Cristian, editor.

Series:

Intelligent Systems Reference Library

Intelligent Systems Reference Library ; v.220

Language:

English

Subjects (All):

Artificial intelligence--Educational applications.

Artificial intelligence.

Physical Description:

1 online resource (167 pages)

Edition:

1st ed.

Place of Publication:

Cham, Switzerland : Springer, [2022]

Summary:

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.

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

4.1 The Methodology Used for Data Collection

4.2 Structure of the Dataset

Building Data Analysis Workflows that Provide Personalized Recommendations for Students

2 Machine Learning Workflows

3 Inferring Personalized Recommendations by Course Difficulty Prediction and Ranking

4 Personalized Message Recommendation by Usage of Decision Trees

Building Interpretable Machine Learning Models with Decision Trees

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

Enhancing Machine Learning Models by Augmenting New Functionalities

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

Increasing Engagement in e-Learning Systems

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

Usability Evaluation Roadmap for e-Learning Systems

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

Developing New Algorithms that Suite Specific Application Requirements

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.

References.

Notes:

Includes bibliographical references.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Mihăescu, Marian Cristian Data Analytics in e-Learning: Approaches and Applications

ISBN:

9783030966447

OCLC:

1309028858