1st Edition

Regression and Machine Learning for Education Sciences Using R

By Cody Dingsen Copyright 2025
    364 Pages 104 B/W Illustrations
    by Routledge

    364 Pages 104 B/W Illustrations
    by Routledge

    This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making.

    Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning. With each chapter, the discussion and development of each statistical concept and data analytical technique are presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts.

    Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial. The dataset and examples used in the book will be from the educational setting, and students will find that this text provides good preparation for studying more statistical and data analytical materials.

    A brief introduction to R and R Studio

    Part 1: Regression models: Foundation of machine learning

    Chapter 01: First thing first: Simple regression

    1.1. Introduction
    1.2. An example
    1.3. What is the regression model
    1.4. How to interpret the regression model
    1.5. What is the sum of squares and r2 in the regression model
    1.6. What are the predicted values and the residuals?
    1.7. How to estimate regression line and what method is used?
    1.8. Inference about regression coefficients
    1.9. Regression with categorical independent variable
    1.10. Summary
    Hands-on practice

    Chapter 02: Beyond simple: Multiple regression analysis

    2.1. Introduction
    2.2. An example
    2.3. What is a multiple regression model
    2.4. How to interpret the results from multiple regression analysis
    2.5. Assessing the importance of multiple independent variables
    2.6. Recap on categorical independent variables
    2.7. How the multiple regression model is estimated
    2.8. Summary
    Hands-on practice

    Chapter 03: It takes two to tangle: Regression with interaction

    3.1. Introduction
    3.2. An example
    3.3. The difference between regression model with and without interaction
    3.4. The meaning of βi associated with an interaction term
    3.5. Interpretation of interaction
    3.6. Summary
    Hands-on practice

    Chapter 04: Are we thinking correctly: Checking assumptions of regression model

    4.1. Introduction
    4.2. What are the assumptions of the regression model
    4.3. How to check the assumptions
    4.4. Summary
    Hands-on practice

    Chapter 05: I am not straight but robust: Curvilinear Robust and Quantile regression

    5.1. An example
    5.2. What is curvilinear regression?
    5.3. Piecewise regression
    5.4. Robust regression
    5.5. Quantile regression
    5.6. Summary
    Hands-on practice

    Chapter 06: Predicting the class probability: Logistic regression

    6.1. An example
    6.2. What is logistic regression
    6.3. Interpreting the results from the logistic regression
    6.4. The logistic regression model with interaction
    6.5. Multinomial logistic regression
    6.6. Assumptions of the logistic regression model
    6.7. Summary
    Hands-on practice

    Part 2: Machine learning: Classification and predictive modeling

    Chapter 07: Introduction to machine learning

    7.1. Big data, data science, and data mining
    7.2. What is machine learning
    7.3. Data preprocessing: A critical step in machine learning
    7.4. Machine learning algorithms
    7.5. Data splitting for validation
    7.6. Summary

    Chapter 08. Machine learning algorithms and process

    8.1. Introduction to caret package
    8.2. Steps in performing machine learning
    8.2.1 Detailed discussion of each step of machine learning
    8.3. Summary

    Chapter 09. Let me regulate: Regularized Machine learning

    9.1. Data preprocessing
    9.2. Linear regression using machine learning
    9.3 Lass, ridge, and elastic net regression models
    9.4. Multivariate adaptive regression spline
    9.5. Regression tree
    9.6. Summary
    Hands-on practice

    Chapter 10. Finding ways in the forest: Prediction with Random Forest

    10.1. Random forest
    10.2 Basic principles
    10.3 Randomization
    10.4. Single tree with CART
    10.5 Bagging
    10.6 Tuning parameters
    10.7. Variable importance
    10.8. Example
    10.9. Adaptive boosting (AdaBoost) with decision trees
    10.10. Gradient boosting with decision trees
    10.11. Summary
    Hands-on practice

    Chapter 11. I can divide better: Classification with support vector machine

    11.1. What is Support Vector Machine
    11.2. Tuning parameters
    11.3. Multiclass classification
    11.4 Estimated class probabilities
    11.5. Other classification methods
    11.6. An example of SVM classification
    11.7. An example of SVM regression
    11.8. Summary
    Hands-on practice

    Chapter 12. Work like a human brain: Artificial neural network

    12.1. What are artificial neural networks?
    12.2. Types of artificial neural networks
    12.3. Single-layer feedforward neural network
    12.4. Multilayer feedforward neural networks
    12.5. Recurrent neural networks
    12.6 An Example
    12.7. Summary
    Hands-on practice

    Chapter 13. Desire to find causal relations: Bayesian network

    13.1. Bayesian network and causal discovery
    13.2. Construction of Bayesian network
    13.3. Example
    13.4. Summary
    Hands-on practice

    Chapter 14. We want to see the relationships: Multivariate data visualization

    14.1. Commonly used data visualization methods
    14.2. Multidimensional scaling visual method for classification
    14.3. Example
    14.4. Summary
    Hands-on practice


    Cody Dingsen is a professor in the Department of Educational Sciences & Professional Programs at the University of Missouri-St. Louis. His research interests include Multidimensional Scaling models for change and preference, psychometrics, data science, cognition and learning, emotional development, and biopsychosocial development.