Instructors
This guide provides information on how to structure your course, how to use the textbook, and how to access additional resources to support your teaching.
Overview
The structure of the book takes an approach which first introduces the conceptual aspects and then the practical aspects and implementation strategies of quantitative text analysis. To understand how you might approach teaching with this book, it is important to understand the structure of the book and how the resources are connected to the book.
The Data to Insight Hiearchy, introduced in the Preface, is used as a framework to understand the process of text analysis.
Figure 1 illustrates how the relationship between each of the parts of the book and the overall goal of the book. Part I is an orientation to the area of text analysis. Subsequently, Part II, III, IV, and V cover the foundational concepts, preparation, analysis, and contribution, respectively.
The book, its parts, and their chapters are connected to a series of materials to develop student experience and confidence in working with the concepts and practical strategies employed in text analysis research.
In Table 1, the chapters are connected to lessons and labs that can be used to support the learning process. The lessons are interactive programming tutorials that introduce students to essential R programming skills. Recipes elaborate on concepts in the chapter and provide more engagement with the programming techniques from the lesson(s) and chapter to further develop student experience in the practice of data analysis. The labs are hands-on exercises that allow students to practice the skills they have learned throughout the sequence and apply it in a more free-form environment.
| Part | Chapter | Lesson1 | Recipe | Lab2 |
|---|---|---|---|---|
| Preface | Intro to Swirl | Literate Programming and Quarto | Writing with code | |
| Part I: Orientation | ||||
| Text analysis | Workspace Vectors |
Academic writing with Quarto | Crafting scholarly documents | |
| Part II: Foundations | ||||
| Data | Objects Packages and functions |
Reading, inspecting, and writing datasets | Dive into datasets | |
| Analysis | Summarizing data Visual summaries |
Descriptive assessment of datasets | Trace the datascape | |
| Research | Project environment | Understanding the computing environment | Scaffolding reproducible research | |
| Part III: Preparation | ||||
| Acquire | Control Statements Custom Functions |
Collecting and documenting data | Harvesting research data | |
| Curate | Pattern matching Tidy datasets |
Organizing and documenting datasets | Taming data | |
| Transform | Reshape Datasets by Rows Reshape datasets by Columns |
Transforming and documenting datasets | Dataset alchemy | |
| Part IV: Analysis | ||||
| Explore | Advanced objects | Exploratory analysis methods | Pattern discovery | |
| Predict | Advanced visualization | Building predictive models | Text classification | |
| Infer | Advanced tables | Building inference models | Statistical inference | |
| Part V: Contribution | ||||
| Communicate | Computing environment | Manage project and computing environments | Future-proofing research |
Course structures
Given the structure of the book and its resources, there are various options for organizing a course. Below I have structured three potential courses: introductory, intermediate, and advanced. Each option will have a recommended course structure and a list of materials that can be used for support. Each of these considers the following questions:
- What the student profile is.
- What the learning outcomes are.
- What content to cover.
- What computing environment to use.
- What supplementary materials to use.
I will also suggest some possible deliverables for each course type as well as some general considerations from my own experience in teaching with this book.
Student profile
- No prior experience with programming or data analysis.
- An interest in or are studying linguistics, or a language-related field.
- Want to know how to use programming to analyze language data.
Goals
To introduce students to the practice of data analysis with a focus on language data. An introduction to the fundamental aspects of R programming and an undestanding of reproducible research and its importance in data analysis.
Content
Chapters 0-4 are covered in this course. The corresponding supplementary materials are used to support the learning process.
Deliverables
- Literature review, research statement, and research question
Computing environment
- Posit Cloud or other cloud-based computing environment.
The reasoning here is to help avoid some of the hurdles that come with setting up a local environment. This will allow students to focus on the content and not the setup. It also allows for a uniform environment for all students.
Notes
Given the fact that only 5 chapters are covered, the text and materials can be completed in much shorter time than a typical 15 week semester. It is recommended to supplement this book and its resources with other materials relevant to the course that will enhance the students’ learning experience and preparation for the literature review, research statement, and research question.
For intro students, the material in the Preparation, Analysis, and Contribution parts can be quite overwhelming. However, if there is interest, content from those chapters may be used as needed.
Student profile
- Students have some experience with data analysis, likely with graphical user interfaces (GUIs) and some experience with programming.
- Students have taken an introductory course in linguistics or a related field.
- Students are interested in learning how to use programming with R to analyze language data.
Goals
- To introduce students to designing a reproducible research project, preparing data for analysis, and choosing an appropriate analysis method (exploratory, predictive, or inferential). The students will be able to prepare a prospectus which outlines this research project.
Content
- Textbook (12) chapters
- Lessons (swirl interactive programming lessons: numbers 1-17)
- Recipes: 0-10
Deliverables
- Lab exercises: 0-11
- Prospectus
Computing environment
- An IDE (RStudio) running in a containerized environment.
Students will likely want to have more control over their working environments. While instructors will want to have a consistent environment for the class. Using a containerized environment will works well in this scenario. Furthermore, the isolation between the host and container will help to avoid any unexpected issues or interactions with the host system.
Notes
The textbook and resources are an ideal fit for this student profile and course goals. In this course structure, each chapter is covered and the preparatory and follow up materials are used to support the learning process. The lessons and labs are used to help students develop their programming skills and apply them to the content of the chapters, respectively.
In my experience, the prospectus allows students to demonstrate their understanding of the fundamental aspects of the research process and highlight their ability to apply these concepts to a topic of their choosing. Given that there is a lot to learn in the course, and the learning curve is steep for some students, a full research project may be too much to ask in a single semester.
Student profile
- Students have experience in programming (likely not with R) and data analysis.
- Students have taken an intermediate course in linguistics or a related field.
- Students are interested in learning how to apply these skills to language data and/ or want to learn how to approach projects with reproducibility in mind.
Goals
- To provide students with the skills to work with designing and conducting a reproducible research project. Students will demonstrate the ability to reproduce the results and collaborate with others on a project.
Content
- Textbook (12) chapters
- Lessons (swirl interactive programming lessons: numbers 1-17)
- Recipes: 0-10
Deliverables
- Lab exercises: 0-11
- Prospectus
- Research project
Computing environment
- IDE (RStudio or VSCode) running on the host machine (or in a containerized environment).
Given that students have experience with programming and data analysis, they will likely have a preferred IDE or Editor. RStudio and VSCode both have functionality (or plugin functionality) which make the particularly well-suited for working with R and Quarto.
Furthermore, you may choose to allow students to use their preferred environment. However, it is also worth considering the use of containerized environments to ensure that all students have a consistent environment for the course.
Notes
In this learning scenario, some of the content early on may be condensed or skipped altogether (particularly Chapters 1-4). Note that the lessons and labs are still useful for students to develop R fundamentals and apply them. These may be assigned in a more self-directed manner, with students working through them at their own pace. In addition to the prospectus, students will complete a research project that demonstrates their ability to apply the concepts and skills learned in the course. In addition, to the research process proper, creating a reproducible research project will be a key component of the course.
Teaching resources
Templates
Computing
Environments
Resources
Further reading
Data and datasets
- View Guide 06 for more information on data and datasets.