Digital Integration Teaching Initiative

Summary

This module introduces the Zotero citation management software, including how to use Zotero, its different features, and how it can be effective for research.

Learning Goals

• Understand how to use citation management software to organize research materials
• Understand Zotero’s functions and features

Learning Objectives

• Differentiate source types
• Define several important functions of Zotero and citation management, including libraries, ISBNs, tags, and more
• Input bibliographic information both manually and automatically into Zotero
• Transfer a library into a Works Cited page

Examples from Courses

Introduction to Zotero for Global Justice (Serena Parekh, Philosophy)

Introduction to Zotero for Criminology Research Methods (Megan Denver, Criminology)

Introduction to Computational Text Analysis

Summary

This is a one-day workshop that provides an introduction to text analysis using digital methods. This workshop can either expose students to browser-based tools that perform text analysis (such as Voyant, WordTree, and WordCounter) or demonstrate the basics of more powerful text analysis tools, such as those that use Python or R. By the end of this workshop, students should have a basic understanding of different types of textual data, recognize how the different tools can be used to manipulate and analyze data, and perform some basic textual analysis themselves.

Learning Goals

• Understand the basics of text analysis
• Learn the fundamentals for corpus building and basic pre-processing methods
• Understand the differences between various text analysis tools and how they work with textual data

Learning Objectives

• Use web-based or programmatic tools to analyze textual data
• Understand the behind-the-scenes work of the text analysis tools covered
• Locate and prepare data that can be analyzed by these tools
• Discuss the results of the text analysis tools

Examples from Courses

Computational text analysis for content analysis for Academic Writing (Maryam Monalisa Gharavi, Writing Center)

Web-based text analysis tools in practice for Resilient Cities (Daniel Aldrich, Political Science, Public Policy and Urban Affairs)

Corpus-building and web-based text analysis tools for Introduction to Writing Studies (Neal Lerner, English)

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Advanced Computational Text Analysis

Summary

This is a one-day workshop that provides an advanced introduction to text analysis as a digital method. This workshop is targeted towards upper-level undergraduate courses and graduate courses. This workshop exposes students to the browser-based tool Lexos that perform data preparation and text analysis. The workshop also provides installation and use instructions for AntConc. By the end of this workshop, students should have a basic understanding of different types of textual data, recognize how the tools can be used to manipulate and analyze data, and perform some basic textual analysis themselves.

Learning Goals

• Understand the basics of text analysis as a method
• Learn the fundamentals for corpus building and pre-processing methods
• Understand the differences between various text analysis tools and how they work with textual data

Learning Objectives

• Use Lexos and AntConc to analyze textual data
• Understand the behind-the-scenes work of the text analysis tools covered
• Locate and prepare data that can be analyzed by these tools
• Discuss the results of the text analysis tools

Example from Courses

Advanced Text Analysis with Lexos and AntConc for Readings and Methods for Digital History (Dan Cohen, History)

Summary

This module introduces basics of data modeling through introduction to the CSV (comma-separated values) file format to students. Through introduction to this module, students are learning the basics of principles of planning and organization of data creation and gathering, issues in the ethics of data accessibility, and basic principles of cleaning data in accordance with the presented file format.

Learning Goals

• Get a sense of ideas about how to “properly” create and organize data
• Get a sense of how to plan data collection and organization for open use
• Learn how the csv files can be used for the greatest effect

Learning Objectives

At the end of this module students will be able to:

• Turn a human-readable into a machine-readable file
  • They will learn the difference between excel and csv files
  • They will learn some data cleaning operations
  • They will know how to find out the properties of their files relevant for preparing the file for computer processing
• Use and think through basic vocabulary on data organization, open data, and CSV file format: based on this they will be equipped to follow and explore issues in data organization in their individual work

Examples from Courses

Introduction to Comma-separated Values for Digital Histories of Ethnic Boston (Simon Rabinovitch, History)

Summary

This in-class workshop teaches students how to prepare their data in Excel for mapping using geographic information systems (GIS). The class began by explaining how to gather data and know what a dataset contains using metadata. Students then work hands-on in Excel to import CSV files, describe the data, clean the data, and prepare a final dataset to be used in a future GIS workshop.

Learning Goals

• Understand what datasets are and how to find, upload, and read datasets
• Learn basic Excel skills
• Understand file formats
• Learn to clean and create a final dataset

Learning Objectives

• Discuss what a dataset is and what variables are
• Examine example datasets in the UN Sustainable Development Goals
• Go on the UN Sustainable Goals website to download a dataset and review its metadata
• Learn to convert a CSV file into Excel format and vice versa
• Utilize Excel charts to prepare a final dataset and remove unneeded data
• Prepare a final dataset in Excel with the necessary data points for GIS mapping

Examples from Courses

Data Preparation for GIS for Global Governance (Denise Garcia, Political Science)

Summary

This module introduces strategies and best practices for data visualization. It focuses primarily on understanding and working effectively with two tools for data visualization: Tableau and Knight Lab Timeline. The module on ‘Introduction to Tableau’ demonstrates how to use the Tableau software suite to perform spatial analysis and create visualizations. The module on ‘Storytelling with Knight Lab Timeline’ demonstrates how to create a story using an interactive timeline, to guide an audience through a temporal narrative.

Learning Goals

Introduction to Tableau module

• Learn the basics of Tableau, a powerful data visualization tool
• Understand how Tableau interprets data and values
• Develop research questions that can be answered through data visualization and data analysis

Storytelling with Knight Lab Timeline

• Understand the components of compelling storytelling
• Understand the particular choices made when building a timeline
• Understand how to collect data for the timeline

Learning Objectives

At the end of the Introduction to Tableau module, students will be able to:

• Understand spatial data structure in the context of Tableau
• Learn Tableau terminology and understand how Tableau displays data
• Know how to plot points in Tableau based upon x/y coordinates
• Understand filtering and visualization options for spatial data
• Learn how to visualize data using other Tableau methods such as line and bar graphs

At the end of the Storytelling with Knight Lab Timeline module, students will be able to:

• Articulate particular choices made when telling a story using a timeline
• Follow a step-by-step guide for creating, saving, and publishing the timeline spreadsheet using Knight Lab Timeline
• Understand how to insert data into Knight Lab TimelineJS, including text and various media elements
• Navigate presenting the relation between events, processes, and periods of various duration in the timeline

Examples from Courses

Knight Lab Timeline for British Literature Survey (Erika Boeckeler, English)

Knight Lab Timeline for Sociology of the Family (Linda Blum, Sociology)

Visualizations using Tableau in Research Methods (Megan Denver, Criminology)

Summary

This module is an introduction to web publishing using Omeka. It covers basic Omeka terminology, how to navigate the program, how to add items, files, and metadata, how to organize items through tags and collections, and how to display items through simple pages and exhibits.

Learning Goals

• Explore how to navigate Omeka
• Learn how to add items, files, and metadata
• Learn different ways to organize and display items in Omeka

Learning Objectives

• Navigate Omeka’s dashboard
• Add items and files, using Dublin Core and item-specific metadata standards
• Edit and create new item types and item type elements
• Create collections and tags to organize items
• Create simple pages and exhibits to display items

Examples from Courses

Introduction to Omeka for Queer Digital Curation (K.J. Rawson, English)

Summary

This module introduces students to computational social science, digital humanities, and how digital methods shape our society. The module also provides examples of what types of data are being collected, how this data is being used by both large corporations and social scientists, and the ethical implications of living in a digital world.

Learning Goals

• Introduce concepts such as big data; algorithms; algorithmic bias; digital surveillance; and fairness, accountability, and transparency in machine learning
• Examine how data is being used in society
• Understand the basic logic behind machine learning, including input data, weighing values, output results, and the impact of these results
• Understand how digital methods can inform social science and humanities research

Learning Objectives

• Use tech companies’ individual data collection and categorizations to understand what type of data is collected about us and how companies use this data
• Learn concrete examples of how data impacts our daily lives
• Become familiar with what code looks like and how scholars use programming to answer research questions
• Discuss the ethical implications surrounding digitized data

Examples from Courses

Digital Ethics: Understanding Big Data and Algorithmic Bias for Academic Writing (Maryam Monalisa Gharavi, Writing Center)

Digital Ethics: Understanding Big Data and Algorithmic Bias for Advanced Writing in the Disciplines (Cecelia Musselman, Writing Center)

Data and Digital Ethics for Introduction to Sociology (Ineke Marshall, Sociology)

Data Ethics, Collection, and Organization for First Year Writing (Kelly Garneau, Writing Program)

This module introduces strategies and best practices for digital communication and presentation. It focuses primarily on understanding and improving the effectiveness of three popular modes of information delivery: PowerPoint presentations, Podcasts, and TEDTalks. The module on ‘Creative and Effective Presentations’ provides tips and best practices for making visually appealing and effective PowerPoint presentations. The module on ‘Analyzing Podcasts and TEDTalks’ introduces the structures, rhetoric, and strategies commonly used in making them interesting and impactful. 

Learning Goals

Creative and Effective Presentations module

• Learn the best practices for making a PowerPoint presentation.
• Learn advanced design and collaboration functions.
• Understand how to consider accessibility in designing presentations.

Analyzing Podcasts and TEDTalks module

• Introduce the structures, rhetoric, and strategies commonly used in podcasts and TEDTalks. 
• Use case studies and ask critical questions to analyze these two popular modes of information delivery.

Learning Objectives

At the end of the Creative and Effective Presentations module, students will be able to

• Make creative, effective, and accessible presentation slides.
• Work in collaboration on PowerPoint presentations.
• Identify differences between effective and ineffective presentations.

At the end of Analyzing Podcasts and TEDTalks module, students will be able to

• Understand TEDTalk and Podcast anatomy and devices.
• Critically analyze structural and stylistic conventions.
• Identify areas of potential exploration, development, or change.

Examples from Courses

Introduction to Twine: Narrative and Storyboarding (Philip Gilreath, Advanced Writing in the Sciences)

Creative and Effective Presentations for Cities, Sustainability, and Climate Change (Joan Fitzgerald, Public Policy and Urban Affairs)

Analyzing Podcasts and TEDTalks: Structures, Rhetorics, and Strategies for Advanced Writing in the Technical Professions – “Hot Topics in Tech” (Talia Vestri, Writing Program)

Summary

This is a multi-week assignment in which students develop digital scholarly editions of historical or literary documents. The project begins with an introduction to the fundamentals of WordPress and includes discussions of different methods, tools, and strategies for editing humanities research materials. Students work in pairs or small groups to edit materials from a shared document, making decisions about regularization, document selection, contextualization and annotation, management of textual uncertainty, editorial transparency, and other aspects of digital scholarly editing. Students determine the goals and audiences for their editions and articulate in an editorial headnote how their own editions support and highlight their desired readings. The project concludes with collective feedback and reflection.

Learning Goals

• Understand the fundamentals of digital scholarly editing and the range of decisions available to editors
• Identify a particular editorial stance and edit a text or texts for digital publication according to that stance
• Articulate the rationale behind specific editorial choices

Learning Objectives

• Gain proficiency with the WordPress content management system
• Learn how to edit and publish humanities research materials for the web
• Annotate and contextualize a historical or literary document

Examples from Courses

Cacodemon Shakespeare for Introduction to Shakespeare (Erika Boeckeler, English). Digital edition of The Merchant of Venice and assignment materials.

Summary

This module is an introduction to organizing co-op application data using Excel. It teaches basic terminology, how to navigate the program, and how to use relevant functions.

Learning Goals

• Understand the basic terminology and anatomy of Excel
• Learn how to use Excel functions that are relevant to the co-op application process

Learning Objectives

At the end of this module students will be able to use Excel to store, organize, and analyze data for co-op applications and future professional work. Students will be able to successfully navigate the Excel software and understand Excel terminology.

Examples from Courses

Introduction to Excel for Professional Development for Co-op (Lisa Doherty, Co-op Social Sciences/Humanities)

Introduction to Excel for Development Economics (Silvia Prina, Economics)

Summary

This module will go over the basics of how to install NVivo and use the software as a literature review and research tool. Students will be introduced to the basic concepts of NVivo and then taught how to import documents, create nodes, and basic analytical visualizations and queries. This module will ask students to choose one person to interview, transcribe that interview, and qualitatively code it using Nvivo. For this portion, we will use a sample source as well as the students’ transcripts of their interviews to practice qualitative coding and visualizing results with NVivo.

Learning Goals

• Define qualitative coding and why it can be useful while doing research
• Understand what NVivo is as a research tool and what it can do
• Learn important NVivo-specific vocabulary to aid independent research

Learning Objectives

• Create a basic set of nodes using sample transcripts
• Learn how to import data, create nodes, code data, and interpret results of coding
• Use NVivo to begin creating nodes and coding own interview transcripts

Examples from Courses

Qualitative Coding with NVivo for Criminology Research Methods (Megan Denver, Criminology)

Qualitative Coding with NVivo for Sociology Senior Seminar (Ineke Marshall, Sociology)

Summary

This module is an introduction to podcasting using Audacity. It covers basic podcast anatomy and techniques, tips for getting started with audio production, how to record and edit audio in Audacity, and best practices for fair use audio attribution.

Learning Goals

• Understand podcast anatomy and devices
• Learn best practices for audio recording
• Learn about and explore Audacity as a podcast editing tool

Learning Objectives

Recording and editing audio in Audacity, including: 

• Clip audio
• Add/move/delete tracks
• Fade audio in/out
• Add sound effects and/or background music
• Save and export projects

Examples from Courses

Introduction to Podcasting for Opening the Archive (K. J. Rawson, English)

Introduction to Podcasting for History of Information in the United States (Victoria Cain, History)

Summary

This is a hands-on workshop that provides a brief introduction to the scripting languages Python or R, with an eye toward data logic, representation, and analysis. By the end of the workshop, students should have enough knowledge to do basic data analysis and visualization using Python or R. The assumption is these skills will be used and built on throughout the course, to equip students to do a final statistical project.

Learning Goals

• Understand what Python or R is, why these languages are useful, and how to use Python or R for data analysis
• Understand how Python or R interacts with, and represents, data

Learning Objectives

• Understand basic concepts in Python or R—such as variables, data types, and dataframes
• Understand the different libraries that Python or R offer and what they can do
• Write code to: read in a data set and manipulate a few variables; write and apply custom functions to execute calculations; produce basic summary statistics from a dataframe; produce visualizations from the dataframe, such as histograms, scatter plots, and bar charts; implement a T-Test; implement a simple OLS regression model and interpret the output

Examples from Courses

Introduction to Python for Statistics in Health Economics and Health Care Policy (Angela Kilby, Economics)

Introduction to R for Statistics for Labor Economics (Alicia Sasser Modestino, Economics)

Introduction to Python for Reasoning in Information and Uncertainty (Don Fallis, Philosophy)

Summary

This hands-on workshop teaches students how to accomplish a set of statistical computing tasks using both Microsoft Excel and the R programming language. The lesson focuses heavily on best practices in Excel for data management, how to use R and Excel together, and how to determine which tool to use to accomplish the task at hand. Learners are introduced to: the potential pitfalls of Excel;concepts of bias and ethics in data and analytics; and how to develop an analytical mindset to clean, organize, and visualize data and craft effective data deliverables for stakeholders.

Learning Goals

• Participants will develop proficiency in both Microsoft Excel and the R programming language for statistical computing tasks.
• Participants will learn best practices in Excel for data management and analysis, and how to integrate R and Excel effectively.
• Participants will understand the potential pitfalls of Excel, learn about bias and ethics in data and analytics, and develop an analytical mindset for data-driven decision making.

Learning Objectives

• Learn data management best practices in Excel, including data cleaning, organization, and manipulation.
• Gain proficiency in using R for statistical computing tasks, including data cleaning, analysis, and visualization.
• Understand how to use Excel and R together to enhance data analysis capabilities.
• Determine when to use Excel and when to use R for specific data analysis tasks to optimize efficiency and accuracy.
• Gain awareness of bias and ethics in data and analytics, and develop an analytical mindset for cleaning, organizing, and visualizing data effectively.

Summary

This module introduces students to the fundamentals of coding in Python with a focus on creating computational poetry. It covers relevant pre-defined functions; variables; various data types including strings, lists, and dictionaries and how to access and manipulate them; conditional statements; and libraries and modules and how to import and use functions from them. It also provides a high-level conceptual introduction to generating poetry using artificial intelligence (AI) models.

Learning Goals

• Understand what Python is and how to use Python for writing computational poetry
• Gain awareness of the basic concepts behind generative AI models

Learning Objectives

• Understand basic concepts in Python including functions, variables, data types, conditional statements, and libraries and modules
• Write code to: import a module, initialize variables containing lists and dictionaries of strings, access values in lists and dictionaries, call functions from a module, and print values in lists and dictionaries based on conditional statements

Example from Course

Introduction to Python for Poetry in Technologies of Text (Jessica Linker and Erika Boeckeler, English and History)

Summary

This module is a one day hands-on workshop, usually offered as an introduction to a website building assignment. It provides a brief introduction to using a website building platform, such as WordPress or Wix, centering what the platforms can offer and how students can use them effectively for their assignments. By the end of the workshop, students know how to navigate the particular website platform as well as understand that their choices when using this platform are rhetorical.

Learning Goals

• Understand the basics and more advanced features of a website building tool, as well as the constraints and affordances of the platform
• Understand the rhetorical choices that go into building a website

Learning Objectives

• Learn the vocabulary used for the tool (ex: in WordPress, the difference between “pages” and “posts”)
• Create a website by following a step-by-step process
• Know how to use the website platform as well as the rationale behind particular choices

Examples from Courses

Introduction to Wix and WordPress for Advanced Writing in the Business Administration Professions (Amy Patterson, Writing Center)

Introduction to WordPress for Professional Development for English/Political Science Co-op (Lisa Doherty, English)

Introduction to Wix and WordPress in Elementary German 1 (Carolin Fuchs, German)

Summary

This module covers components of storytelling and how these may be integrated in maps. Specifically, this in-class workshop teaches students how to use Knight Lab’s StoryMap web-browser application by providing step-by-step instructions, a sample map, and sample data for students to use as they build their own maps.

Learning Goals

• Understand the components of compelling storytelling
• Understand the choices made when building a map
• Understand how to collect data for mapping
• Understand Knight Lab’s StoryMap’s interface and where it gathers its information

Learning Objectives

• Articulate choices made when telling a story using a map
• Follow a step-by-step guide for creating, saving, and publishing maps using Knight Lab’s StoryMap
• Add data into StoryMap, including location information, images, and text
• Navigate StoryMap’s map markers and location-finding system
• Use image as a map through StoryMap’s Gigapixel option

Examples from Courses

Storytelling with Mapping for Jews in the Modern World (Simon Rabinovitch, History)

Spatial Mapping for Science of Play (Emily Mann, Human Services)

Storytelling with Mapping for Literature and the Visual Arts (Erika Boeckeler, English)

Digital Storytelling with Mapping for Introduction to the History of the United States (Gretchen Heefner, History)

Summary

This is module is a one-day workshop that provides a comprehensive overview of survey creation and analysis using the software Qualtrics. Students will be introduced to the general concepts of writing surveys, general errors to avoid, and research process including a discussion of research ethics. This module also introduces and guides students in using Qualtrics to design questions, gather and analyze survey data, and export finding and visualizations for publication or analysis. This module uses a demonstration survey on people’s commute to demonstrate the process with screenshots to practice using the Qualtrics interface.

Learning Goals

• Understand components for informative surveys
• Understanding the particular choices made when crafting survey questions
• Understanding how to program survey questions in Qualtrics
• Understanding Qualtrics’ interface

Learning Objectives

• Articulate particular choices made when designing a survey
• Follow a step-by-step guide for creating, saving, and distributing surveys using Qualtrics
• Collect, export and process survey results in Qualtrics

Examples of this Course

• Creating and Analyzing Qualtrics Surveys for Journalism Research for Media Strategy (Myojung Chung, Journalism)
• Social Science Research & Survey Design and Analysis for English Graduate Rhetoric seminar (Mya Poe, English)

Summary

This is a multi-week assignment that provides an introduction to modeling archival documents using the Text Encoding Initiative (TEI) markup language. The project begins with a class visit introducing the core concepts of TEI markup and demonstrating some analytical applications of text encoding. Students then transcribe and encode an archival document of their own, while working in groups to make editorial decisions about how they will encode their documents in TEI/XML markup and publish them online. Individual students are responsible for encoding their archival documents, writing reflective introductions on their projects, and presenting on their work to the class; groups are responsible for authoring editorial declarations that describe all of their choices in modeling and representing their documents.

Learning Goals

• Understand how text encoding can be used to model humanities research materials
• Learn some of the goals and theoretical foundations of TEI encoding
• Understand the fundamentals of digital scholarly editing

Learning Objectives

• Understand the basics of XML and TEI
• Understand how to edit TEI documents using the Oxygen XML editor
• Learn how to: encode archival documents using TEI/XML; use the TEI Guidelines to look up elements and attributes; add special characters with entity references and Unicode; use CSS to control some basics of how encoded documents appear online; recognize the ramifications of different approaches to encoding; make informed decisions about how to mark documents up, apply those decisions consistently, and articulate them clearly

Examples from Courses

Encoding Digital Editions for Gender, Sex & the Renaissance/Restoration Body (Marina Leslie, English)

Summary

This hands-on workshop teaches students how to accomplish a set of statistical computing tasks using both Microsoft Excel and the R programming language. The lesson focuses heavily on best practices in Excel for data management, how to use R and Excel together, and how to determine which tool to use to accomplish the task at hand. Learners are introduced to: the potential pitfalls of Excel;concepts of bias and ethics in data and analytics; and how to develop an analytical mindset to clean, organize, and visualize data and craft effective data deliverables for stakeholders.

Learning Goals

• Participants will develop proficiency in both Microsoft Excel and the R programming language for statistical computing tasks.
• Participants will learn best practices in Excel for data management and analysis, and how to integrate R and Excel effectively.
• Participants will understand the potential pitfalls of Excel, learn about bias and ethics in data and analytics, and develop an analytical mindset for data-driven decision making.

Learning Objectives

• Learn data management best practices in Excel, including data cleaning, organization, and manipulation.
• Gain proficiency in using R for statistical computing tasks, including data cleaning, analysis, and visualization.
• Understand how to use Excel and R together to enhance data analysis capabilities.
• Determine when to use Excel and when to use R for specific data analysis tasks to optimize efficiency and accuracy.
• Gain awareness of bias and ethics in data and analytics, and develop an analytical mindset for cleaning, organizing, and visualizing data effectively.

Examples from Courses

Introduction to Statistical Computing with Excel and R (Sahar Abi-Hassan, Business)