Table of Contents: Learning Analytics Methods and Tutorials :

Learning Analytics Methods and Tutorials : A Practical Guide Using R.

Bibliographic Details
Main Author:	Saqr, Mohammed.
Other Authors:	López-Pernas, Sonsoles.
Format:	eBook
Language:	English
Published:	Cham : Springer, 2024.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Foreword
Foreword
Preface
Competing Interests
Acknowledgments
Contents
List of Contributors
Editors
Associate Editors
Authors
Reviewers
List of Abbreviations
Capturing the Wealth and Diversity of Learning Processes with Learning Analytics Methods
1 Introduction
2 How the Book Is Structured
2.1 Introductory Chapters
2.2 Machine Learning Methods
2.3 Temporal Methods
2.4 Network Analysis
2.5 Psychometrics
3 The Companion Code and Data
References
Part I Getting Started
A Broad Collection of Datasets for Educational Research Training and Application
1 Introduction
2 Types of Data
2.1 Contextual Data
2.2 Self-reported Data
2.3 Activity Data
2.4 Social Interaction Data
2.5 Performance Data
2.6 Other Types of Data
3 Dataset Selection
3.1 LMS Data from a Blended Course on Learning Analytics
3.1.1 Events
3.1.2 Demographics
3.1.3 Results
3.1.4 AllCombined
3.2 LMS Data from a Higher Education Institution in Oman
3.2.1 Student Academic Information
3.2.2 Moodle
3.2.3 Activity
3.2.4 Results
3.2.5 eDify
3.3 School Engagement, Academic Achievement, and Self-regulated Learning
3.4 Teacher Burnout Survey Data
3.5 Interdisciplinary Academic Writing Self-efficacy
3.6 Educators' Discussions in a MOOC (SNA)
3.7 High School Learners' Interactions (SNA)
3.8 Interactions in an LMS Forum from a Programming Course (SNA)
3.9 Engagement and Achievement Throughout a Study Program
3.9.1 Longitudinal Engagement Indicators and Grades
3.9.2 Longitudinal Engagement and Achievement States
3.10 University Students' Basic Need Satisfaction, Self-regulated Learning and Well-Being During COVID-19
4 Discussion
References
Getting Started with R for Education Research
1 Introduction
2 Learning R
3 RStudio.
4 Best Practices in Programming
4.1 R Markdown
4.2 How Is Code Developed?
5 Basic Operations
5.1 Arithmetic Operators
5.2 Relational Operators
5.3 Logical Operators
5.4 Special Operators
6 Basic Data Types and Variables
7 Basic R Objects
8 Working with Dataframes
8.1 tibble
9 Pipes
9.1 magrittr pipe %&gt
%
9.2 Native pipe |&gt
10 Lists
11 Functions
12 Conditional Statements
13 Looping Constructs
14 Discussion and Other Resources for Learning R
References
An R Approach to Data Cleaning and Wrangling for Education Research
1 Introduction
2 Reading Data into R
3 Grouping and Summarizing Data
4 Selecting Variables
5 Filtering Observations
6 Transforming Variables
7 Rearranging Data
8 Reshaping Data
9 Joining Data
10 Missing Data
11 Correcting Erroneous Data
12 Conclusion and Further Reading
References
Introductory Statistics with R for Educational Researchers
1 Introduction
2 Descriptive Statistics
2.1 Measures of Central Tendency
2.2 Measures of Dispersion
2.3 Covariance and Correlation
2.4 Other Common Statistics
3 Statistical Hypothesis Testing
3.1 Student's t-test
3.1.1 One-Sample t-test
3.1.2 Two-Sample t-test
3.1.3 Paired Two-Sample t-test
3.2 Chi-Squared Test
3.3 Analysis of Variance
3.4 Levene's Test
3.5 Shapiro-Wilk Test
4 Correlation
5 Linear Regression
6 Logistic Regression
7 Conclusion
8 Further Reading
References
Visualizing and Reporting Educational Data with R
1 Introduction
2 Visualization in Learning Analytics
3 Generating plots with ggplot2
3.1 The ggplot2 grammar
3.2 Creating Your First Plot
3.2.1 Installing ggplot2
3.2.2 Downloading the Data
3.2.3 Creating the Aesthetic Mapping
3.2.4 Add the Geometry Component.
3.2.5 Adding the Color Scale
3.2.6 Working with Themes
3.2.7 Changing the Axis Ticks
3.2.8 Titles and Labels
3.2.9 Other Cosmetic Modifications
3.2.10 Saving the Plot
3.3 Types of Plots
3.3.1 Bar Plot
3.3.2 Histogram
3.3.3 Line Plot
3.3.4 Jitter Plots
3.3.5 Box Plot
3.3.6 Violin Plot
3.3.7 Scatter Plots
3.4 Advanced Features
3.4.1 Plot Grids
3.4.2 Combining Multiple Plots
4 Creating Tables with gt
5 Discussion
6 Additional Material
References
Part II Machine Learning
Predictive Modelling in Learning Analytics: A Machine Learning Approach in R
1 Introduction
2 Predictive Modelling: Objectives, Features, and Algorithms
3 Predicting Students' Course Success Early in the Course
3.1 Prediction Objectives and Methods
3.2 Context
3.3 An Overview of the Required Tools (R Packages)
3.4 Data Preparation and Exploration
3.5 Feature Engineering
3.6 Predicting Success Category
3.7 Predicting Success Score
4 Concluding Remarks
5 Suggested Readings
References
Dissimilarity-Based Cluster Analysis of Educational Data: A Comparative Tutorial Using R
1 Introduction
2 Clustering in Education: Review of the Literature
3 Clustering Methodology
3.1 K-Means
3.1.1 K-Means Algorithm
3.1.2 K-means Limitations and Practical Concerns
3.2 Agglomerative Hierarchical Clustering
3.2.1 Linkage Criteria
3.2.2 Cutting the Dendrogram
3.3 Choosing the Number of Clusters
4 Tutorial with R
4.1 The Data Set
4.1.1 Pre-processing the Data
4.2 Clustering Applications
4.2.1 K-means Application
4.2.2 K-medoids Application
4.2.3 Agglomerative Hierarchical Clustering Application
4.2.4 Identifying the Optimal Clustering Solution
4.2.5 Interpreting the Optimal Clustering Solution
5 Discussion and Further Readings
References.
An Introduction and R Tutorial to Model-Based Clustering in Education via Latent Profile Analysis
1 Introduction
2 Literature Review
3 Model-Based Clustering
3.1 Latent Variable Models
3.2 Finite Gaussian Mixture Models
4 Gaussian Parsimonious Clustering Models
4.1 Model Selection
4.2 mclust R Package
4.3 Other Practical Issues and Extensions
4.3.1 Bayesian Regularisation
4.3.2 Bootstrap Inference
4.3.3 Entropy and Average Posterior Probabilities
5 Application: School Engagement, Academic Achievement, and Self-regulated Learning
5.1 Preparing the Data
5.2 Model Estimation and Model Selection
5.3 Examining Model Output
6 Discussion
References
Part III Temporal Methods
Sequence Analysis in Education: Principles, Technique, and Tutorial with R
1 Introduction
2 Review of the Literature
3 Basics of Sequences
3.1 Steps of Sequence Analysis
3.1.1 The Alphabet
3.1.2 Specifying the Time Scheme
3.1.3 Defining the Actor
3.1.4 Building the Sequences
3.1.5 Visualizing and Exploring the Sequence Data
3.1.6 Calculating the Dissimilarities Between Sequences
3.1.7 Finding Similar Groups or Clusters of Sequences
3.1.8 Analyzing the Groups and/or Using Them in Subsequent Analyses
3.2 Introduction to the Technique
3.3 Sequence Visualization
4 Analysis of the Data with Sequence Mining in R
4.1 Important Packages
4.2 Reading the Data
4.3 Preparing the Data for Sequence Analysis
4.4 Statistical Properties of the Sequences
4.5 Visualizing Sequences
4.6 Dissimilarity Analysis and Clustering
5 More Resources
References
Modeling the Dynamics of Longitudinal Processes in Education. A Tutorial with R for the VaSSTra Method
1 Introduction
2 VaSSTra: From Variables to States, from States to Sequences, from Sequences to Trajectories.
3 Review of the Literature
4 VassTra with R
4.1 The Packages
4.2 The Dataset
4.3 From Variables to States
4.4 From States to Sequences
4.5 From Sequences to Trajectories
4.6 Studying Trajectories
5 Discussion
References
A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education
1 Introduction
2 Methodological Background
2.1 Markov Model
2.2 Mixture Markov Model
2.3 Hidden Markov Model
2.4 Mixture Hidden Markov Models
2.5 Multi-Channel Sequences
2.6 Estimating Model Parameters
3 Review of the Literature
4 Examples
4.1 Steps of Estimation
4.1.1 Defining the Model Structure
4.1.2 Estimating the Model Parameters
4.1.3 Examining the Results
4.2 Markov Models
4.2.1 Markov Model
4.2.2 Hidden Markov Models
4.2.3 Mixture Markov Models
4.2.4 Mixture Hidden Markov Models
4.3 Stochastic Process Mining with Markovian Models
5 Conclusions and Further Readings
References
Multi-Channel Sequence Analysis in Educational Research: An Introduction and Tutorial with R
1 Introduction
2 Multi-Channel Sequence Analysis
2.1 Step 1: Building the Channel Sequences
2.2 Step 2: Visualising the Multi-Channel Sequence
2.3 Step 3: Finding Patterns (Clusters or Trajectories)
2.3.1 Traditional Sequence Analysis Extensions
2.3.2 Mixture Hidden Markov Models
2.4 Step 4: Relating Clusters to Covariates
3 Review of the Literature
4 Case Study: The Longitudinal Association of Engagement and Achievement
4.1 The Packages
4.2 The Data
4.3 Creating the Sequences
4.3.1 Engagement Channel
4.3.2 Achievement Channel
4.3.3 Visualising the Multi-Channel Sequence
4.4 Clustering via Multi-Channel Dissimilarities
4.5 Building a Mixture Hidden Markov Model
4.6 Incorporating Covariates in MHMMs
5 Discussion.
6 Further Readings.

Learning Analytics Methods and Tutorials : A Practical Guide Using R.

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