Course Description
In this course, we will evaluate a variety of digital humanities projects through theoretical scholarship in the field, in order to create our own projects and critically assess the value of the new knowledge that is being generated. We will teach how to weigh that knowledge with a combination of traditional humanities methods and theories for analysis. We will explore the fundamental arguments that are being advanced about these new methods and how they interact with the humanities’ interpretive underpinnings. This course will prepare students to apply digital methods in ethical, reflective, and responsible ways—understanding the potentials of the digital within the humanities.
As an area of scholarly research, digital humanities (DH) acts as a bridge between the Arts and Humanities, on the one hand, and the Sciences on the other hand, and therefore cultivates the ability of each student to translate between these disciplines. In order to make such translations, DH projects take a ‘both-and’ approach: it begins with the artisanal craft of the Arts and Humanities through imaging, digitization, and digital curation; it then prepares the relevant sources for critical analysis (i.e. exegesis and hermeneutics) through qualitative analysis, tagging, encoding, and linking datasets with the same specialist knowledge from each discipline within the Humanities through computational tools; it then approaches these curated sources empirically, posing new questions which are conducted through exploratory data analysis, visualization, and documentation; lastly it deals responsibly with the interpretation of the results, and conveys any assumptions made through testing the reproducibility and replicability of the study at hand. DH projects exemplifying these methods have proven to be transformative for their fields and departments in the Arts and Humanities, and across the academic landscape. They have allowed for cross-disciplinary engagement not only at the theoretical level, but also the levels of qualitative and quantitative analysis.
In addition to mapping and modeling DH encompasses efforts to apply computational technologies to the questions of humanities disciplines such as literature, history, and cultural studies (i.e. ‘cultural analytics’), along with analysis of the theoretical assumptions and ethical implications of existing technologies and methods.
The emphasis of this introductory digital humanities course is on the theoretical and methodological considerations involved with data-intensive research within the context of the humanities. We will explore the ways the union of data analysis and humanities research is both productive and in tension.
Students will engage with the theoretical and methodological foundations of digital humanities work through participation in discussion of the readings, a review of an existing digital humanities project, and crafting a proposal for a unique digital humanities project. Through this course, students will learn to evaluate computational systems and data-driven arguments and to develop reflective, ethical, and critically-aware projects in the digital humanities.
Daily Attendance and/or watch daily lectures + Reading + Group Discussion (10%).
Discovery tasks for group project website (10%)
Make 1 Jupyter Notebook in the Collaborative SandBoxes (20%).
Complete 4 Poster Assignments for your Digital Humanities Project (25 Points each = 50%).*
Complete a 5 min. Video Presentation of your Digital Humanities Project (10%).
Provide feedback on team members Digital Humanities Projects.
*If you do not submit the first two assignments, you will be dropped from the class.
The main hubs for this course will be in Google Drive, Please contact me immediately if you are unable to access our shared Drive Folder. where students and the instructor will collaborate, share readings and slides for the course. Students will receive feedback from the instructor on their project brainstorm diagrams and Omeka projects.
4 Assignments (25 points each):
1. Initial Diagram of your Digital Humanities Project
(Due: June 7)
Prepare a poster (24x36) illustrating your ideas & questions for the subject you will make a DH project. Describe the type of analysis you intend to explore. Include examples of how you would like it to look based on other DH projects (see Digital Humanities Projects to Review). Points for describing the following:
Course Title & Instructor (1)
Your Name & Date (1)
Descriptions of your Dataset (10)
Questions for Exploratory Data Analysis (5)
Descriptions of Tools & Methods (5)
Interpretations of results (1)
Works Cited / References (2)
Total 25 Points
2. Second Draft of your Digital Humanities Project
(Due June 14)
In the second draft you should re-evaluate your questions:
Are your questions answerable with the methods you are proposing?
Who is your audience and what is the frame of reference for your project?
Anticipate the results (expectations) and how will you interpret them?
Course Title & Instructor (1)
Your Name & Date (1)
Descriptions of your Dataset (5)
Questions for Exploratory Data Analysis (10)
Descriptions of Tools & Methods (5)
Interpretations of results (1)
Works Cited / References (2)
Total 25 Points
3. Description & Interpretation of your Digital Humanities Project
(Due June 21)
Your Poster should include a walk through of the major features of the project: a description and assessment of the project dataset, empirical questions, analytical methods & tools, and an interpretation of the preliminary results project citing at least two of the course readings.
Course Title & Instructor (1)
Your Name & Date (1)
Descriptions of your Dataset (5)
Questions for Exploratory Data Analysis (5)
Descriptions of Tools & Methods (5)
Interpretations of results (10)
Works Cited / References (3)
Total 25 Points
4. Final Poster of your Digital Humanities Project
(Due June 28)
Course Title & Instructor (1)
Your Name & Date (1)
Descriptions of your Dataset (2)
Questions for Exploratory Data Analysis (5)
Descriptions of Tools & Methods (1)
Interpretations of results (5)
Works Cited / Hyperlinks / References (10)
Total 25 Points
Group Discussion and Reading Citation: 10%
Group Project Website: 10%
Sandbox Notebook: 20%
4 Assignments: 50%
Final Project Video Presentation: 10%
Course Title & Instructor
Your Name & Date
Descriptions of your Dataset
Questions for Exploratory Data Analysis
Descriptions of Tools & Methods
Interpretations of results
Works Cited / Hyperlinks / References
Accommodations for Students with Disabilities
Please see me as soon as possible if you need particular accommodations, and we will work out the necessary arrangements.
TL;DR: Cite all your sources using Hyperlinks (Links to an external site.) AND consistent formatting (e.g. MLA (Links to an external site.)). If you are found plagiarizing (Links to an external site.), you will not pass this class.
You are a member of an academic community at one of the world’s leading research universities. Universities like Berkeley create knowledge that has a lasting impact in the world of ideas and on the lives of others; such knowledge can come from an undergraduate paper as well as the lab of an internationally known professor. One of the most important values of an academic community is the balance between the free flow of ideas and the respect for the intellectual property of others. Researchers don't use one another's research without permission; scholars and students always use proper citations in papers; professors may not circulate or publish student papers without the writer's permission; and students may not circulate or post materials which are not heir own (handouts, exams, syllabi—any class materials) from their classes without the written permission of the instructor.
Any paper or project submitted by you and that bears your name is presumed to be your own original work that has not previously been submitted for credit in another course unless you obtain prior written approval to do so from your instructor. In all of your assignments, including your homework or drafts of papers, you may use words or ideas written by other individuals in publications, web sites, or other sources, but only with proper attribution (MLA (Links to an external site.) citation). If you are not clear about the expectations for completing an assignment or taking a test or examination, be sure to seek clarification from me beforehand. Finally, you should keep in mind that as a member of the campus community, you are expected to demonstrate integrity in all of your academic endeavors and will be evaluated on your own merits. The consequences of cheating and academic dishonesty—including a formal discipline file, possible loss of future internship, scholarship, or employment opportunities, and denial of admission to graduate school—are simply not worth it. If you are found plagiarizing, you will not pass this course without making up the work.
25 Points
