Learning Analytics Methods and Tutorials : A Practical Guide Using R. / Mohammed Saqr, Sonsoles López-Pernas.

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

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.