Syllabus

v1.5.0 last updated on March 20, 2021.

This is the syllabus for ENGL304: Topics in Digital Research in Spring 2021. Please read the entire syllabus carefully.

Instructor Information

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Course Tools

We use a course website, course repository, Slack, and Canvas to facilitate class activities. The course website is for annotating Charles Dickens’s Our Mutual Friend with Hypothesis and sharing course materials. Tutorials for each session are provided through the tutorial website created by the instructor. We use Slack for class announcements, discussions, questions, and group work. Canvas is mainly for turning in assignments and distributing grades.

Textbook

Required Software

Students are required to use their own laptops/desktops in the classroom for course activities.

  • RStudio Cloud : a cloud-based R environment which is free to use. Students are not required to purchace a RStudio Cloud membership for this course. If students want to run R in their local environment, they can choose R & RStudio. R needs to be installed first in order to use RStudio. RStudio is a user-friendly programming tool which is free to use.
  • Google Colab: a cloud-based notebook environment which is suitable for machine learning and data analysis. A Google account is required to use Colab. You are not required to purchase a Colab membership for this course since we will only be using the free features.
  • Gephi: an open-source network analysis and visualization software package, which is free to download.

Required Signup for Websites

  • Hypothesis: Students are required to have a free account on Hypothesis to annotate texts on the course website.
  • GitHub: Students need to sign up for GitHub in order to do homework assignments. Students do not have to get a Pro membership, but they can get a Pro membership for free upon request at GitHub for Students. GitHub is a subsidiary of Microsoft.

Course Description

Topics in Digital Research. (3-0) Credit 3. This course does not require any background in computer programming. We cover many topics in the digital humanities, specifically research methods deployed in computational literary studies.

Using computational tools based on quantitative methods is a way to examine literary texts in order to find answers to critical questions based on qualitative methods. This course is centered on literary texts; learning computational approaches to the humanities is secondary. In this course, we aim to become meaningful digital humanists across all fields by learning computational approaches in the humanities.

Learning Outcomes/Course Objectives

At the end of the semester, you will be able to do the following:

  • Learn collaboration by working with other members on digital projects.
  • Grasp digital archives publicly shared in the digital humanities.
  • Be familiar with basic coding using R and Python.
  • Successfully create humanities datasets and share them with the public.
  • Visualize data and extricate visualizations.

Grading and Course Policies

Your grade is based on the average of the assignments listed below.

  • Participation (In-Class Discussions and Attendance): 10%
  • Annotations: 15%
  • Homework Assignments: 15%
  • 2 Blog Posts: 10%
  • Project Presentation: 10%
  • Digital Project (40% total)
    • Proposal: 5%
    • Progress Report: 10%
    • Final Report: 25%
  • Total: 100% Grading Scale: A (90-100); B (80-89); C (70-79); D (60-69); F (59 or less)

Attendance and Late Paper Policy

Expectations of the English Department and the University Rules are that you complete classwork and submit all assignments in the sequence they are assigned, by the deadlines posted on Canvas. It is your responsibility to check if your assignments were submitted successfully and correctly on Canvas. You are also expected to attend class regularly. Cameras should remain turned on during class. If your camera is not on, you will not be counted present. Any student in excess of two unexcused absences is eligible to be reported for excessive absences; any unexcused absence over this limit will lead to the loss of three percent of your final grade per absence. Being more than 5 minutes late to class is considered an absence.

Annotations and Homework Assignments will not be accepted late. For all other assignments, you will lose 10% off the final assignment grade for every day past the due date. The Late Paper Policy for this course follows the guidelines described in the University Student Rules, “Section 7. Attendance” (http://student-rules.tamu.edu/rule07).

Makeup Work Policy

Students will be excused from attending class on the day of a graded activity or when attendance contributes to a student’s grade, for the reasons stated in Student Rule 7, or other reason deemed appropriate by the instructor.

Please refer to Student Rule 7 in its entirety for information about makeup work, including definitions, and related documentation and timelines.

Absences related to Title IX of the Education Amendments of 1972 may necessitate a period of more than 30 days for make-up work, and the timeframe for make-up work should be agreed upon by the student and instructor” (Student Rule 7, Section 7.4.1).

“The instructor is under no obligation to provide an opportunity for the student to make up work missed because of an unexcused absence” (Student Rule 7, Section 7.4.2).

Students who request an excused absence are expected to uphold the Aggie Honor Code and Student Conduct Code. (See Student Rule 24.)

Academic Integrity Statement and Policy

“An Aggie does not lie, cheat or steal, or tolerate those who do.”

“Texas A&M University students are responsible for authenticating all work submitted to an instructor. If asked, students must be able to produce proof that the item submitted is indeed the work of that student. Students must keep appropriate records at all times. The inability to authenticate one’s work, should the instructor request it, may be sufficient grounds to initiate an academic misconduct case” (Section 20.1.2.3, Student Rule 20).

You can learn more about the Aggie Honor System Office Rules and Procedures, academic integrity, and your rights and responsibilities at aggiehonor.tamu.edu.

Americans with Disabilities Act (ADA) Policy

Texas A&M University is committed to providing equitable access to learning opportunities for all students. If you experience barriers to your education due to a disability or think you may have a disability, please contact Disability Resources in the Student Services Building or at (979) 845-1637 or visit disability.tamu.edu. Disabilities may include, but are not limited to attentional, learning, mental health, sensory, physical, or chronic health conditions. All students are encouraged to discuss their disability related needs with Disability Resources and their instructors as soon as possible.

Title IX and Statement on Limits to Confidentiality

Texas A&M University is committed to fostering a learning environment that is safe and productive for all. University policies and federal and state laws prohibit gender-based discrimination and sexual harassment, including sexual assault, sexual exploitation, domestic violence, dating violence, and stalking.

With the exception of some medical and mental health providers, all university employees (including full and part-time faculty, staff, paid graduate assistants, student workers, etc.) are Mandatory Reporters and must report to the Title IX Office if the employee experiences, observes, or becomes aware of an incident that meets the following conditions (see University Rule 08.01.01.M1):

  • The incident is reasonably believed to be discrimination or harassment.
  • The incident is alleged to have been committed by or against a person who, at the time of the incident, was (1) a student enrolled at the University or (2) an employee of the University.

Mandatory Reporters must file a report regardless of how the information comes to their attention – including but not limited to face-to-face conversations, a written class assignment or paper, class discussion, email, text, or social media post. Although Mandatory Reporters must file a report, in most instances, a person who is subjected to the alleged conduct will be able to control how the report is handled, including whether or not to pursue a formal investigation. The University’s goal is to make sure you are aware of the range of options available to you and to ensure access to the resources you need.

Students wishing to discuss concerns in a confidential setting are encouraged to make an appointment with Counseling and Psychological Services (CAPS).

Students can learn more about filing a report, accessing supportive resources, and navigating the Title IX investigation and resolution process on the University’s Title IX webpage.

Statement on Mental Health and Wellness

Texas A&M University recognizes that mental health and wellness are critical factors that influence a student’s academic success and overall wellbeing. Students are encouraged to engage in healthy self-care by utilizing the resources and services available from Counseling & Psychological Services (CAPS). Students who need someone to talk to can call the TAMU Helpline (979-845-2700) from 4:00 p.m. to 8:00 a.m. weekdays and 24 hours on weekends. 24-hour emergency help is also available through the National Suicide Prevention Hotline (800-273-8255) or at suicidepreventionlifeline.org.

COVID-19 Temporary Addendum

Campus Safety Measures

To promote public safety and protect students, faculty, and staff during the coronavirus pandemic, Texas A&M University has adopted policies and practices for the Spring 2021 academic term to limit virus transmission. Students must observe the following practices while participating in face-to-face courses and course-related activities (office hours, help sessions, transitioning to and between classes, study spaces, academic services, etc.):

  • Self-monitoring—Students should follow CDC recommendations for self-monitoring. Students who have a fever or exhibit symptoms of COVID-19 should participate in class remotely if that option is available, and should not participate in face-to-face instruction.
  • Face Coverings—Face coverings (cloth face covering, surgical mask, etc.) must be properly worn in all non-private spaces including classrooms, teaching laboratories, common spaces such as lobbies and hallways, public study spaces, libraries, academic resource and support offices, and outdoor spaces where 6 feet of physical distancing is difficult to reliably maintain. Description of face coverings and additional guidance are provided in the Face Covering policy and Frequently Asked Questions (FAQ) available on the Provost website.
  • Physical Distancing—Physical distancing must be maintained between students, instructors, and others in course and course-related activities.
  • Classroom Ingress/Egress—Students must follow marked pathways for entering and exiting classrooms and other teaching spaces. Leave classrooms promptly after course activities have concluded. Do not congregate in hallways and maintain 6-foot physical distancing when waiting to enter classrooms and other instructional spaces.
  • To attend a face-to-face class, students must properly wear an approved face covering If a student refuses to wear a face covering, the instructor should ask the student to leave and join the class remotely. If the student does not leave the class, the faculty member should report that student to the Student Conduct office for sanctions. Additionally, the faculty member may choose to teach that day’s class remotely for all students, or dismiss the class in the case of a traditional face to face lecture.

Personal Illness and Quarantine

Students required to quarantine must participate in courses and course-related activities remotely, if that option is available, and must not attend face-to-face course activities. Students should notify their instructors of the quarantine requirement. Students under quarantine are expected to participate in courses and complete graded work unless they have symptoms that are too severe to participate in course activities.

Students experiencing personal injury or Illness that is too severe for the student to attend class qualify for an excused absence (See Student Rule 7, Section 7.2.2.) To receive an excused absence, students must comply with the documentation and notification guidelines outlined in Student Rule 7.

ENGL 304 Course Schedule

The course schedule is subject to changes.
(CV = Canvas; TR = Tutorials; CW = Course Website)

  • Week 1: What are the Digital Humanities?

    • Tue (1/19)
      • Syllabus and course schedule
      • Introductions
      • CW: Annotating on the course website
    • Thu (1/21)
      • What are the digital humanities?
      • What is digital literacy?
      • Charles Dickens
      • Introduction to Course Tools
      • Introduction to Our Mutual Friend

  • Week 2: Digital Projects/Archives/Databases

  • Week 3: Word Frequencies I

    • Tue (2/2)
      • CW: Our Mutual Friend (bk.1 ch.5-8)
      • R & RStudio
      • RStudio Cloud
      • Voyant
    • Thu (2/4)
      • TR: R basics
      • TR: Word frequencies with R

  • Week 4: Word Frequencies II

    • Tue (2/9)
      • CW: Our Mutual Friend (bk.1 chs.9-13)
      • TR: Word clouds with R
      • TR: Word frequencies with Python (NLTK)
    • Thu (2/11)
      • Guest Speaker: Quinn Dombrowski (Stanford University)
      • CW: Our Mutual Friend (bk.1 chs.14-17; bk.2 chs.1-4)
      • TR: Word frequencies with Python (NLTK)

  • Week 5: No Class

    • Tue (2/16)
      • Class Canceled for Weather Emergency
    • Thu (2/18)
      • Class Canceled for Weather Emergency

  • Week 6: Network Analysis

    • Tue (2/23)
      • Network Analysis
      • Introduction to Stylometry
      • Installing software
    • Thu (2/25)
      • CW: Stylometry

  • Week 7: OCR & Digitization

    • Tue (3/2) - Texas Independance Day (NO CLASS)
    • Thu (3/4)
      • CW: Our Mutual Friend (bk.2 chs.5-9)
      • CW: Our Mutual Friend (bk.2 chs.10-12)
      • Introduction to OCR
      • Lab: OCR apps / programs
      • Digital Archives: HathiTrust / Project Gutenberg

  • Week 8: Visualization with Python
    • Tue (3/9)
      • CW: Our Mutual Friend (bk.2 chs.13-16; bk.3 chs.1-3)
      • Google Colab
      • TR: NumPy / pandas
      • TR: Matplotlib / Seaborn
    • Thu (3/11)
      • CW: Our Mutual Friend (bk.3 chs.4-6)
      • TR: Word Frequencies with NLTK

  • Week 9: Topic Modeling

    • Tue (3/16)
      • CW: Our Mutual Friend (bk.3 chs.7-12)
      • Introduction to topic modeling
      • Playing with tools
      • TR: Topic modeling with LDA
    • Thu (3/18)
      • Redefined day

  • Week 10: Sentiment Analysis / Victorian Sentiments

    • Tue (3/23)
      • CW: Our Mutual Friend (bk.3 chs.13-17; bk.4 chs.1-2)
      • TR: Topic modeling with MALLET & Visualization
    • Thu (3/25)
      • CW: Our Mutual Friend (bk.4 chs.3-5)
      • CW: What is sentiment analysis?
      • Ekman’s theory of basic emotions
      • Plutchik’s wheel of emotions
      • Russel’s circumplex model
      • Stoic sentiments
      • Sentimentalism, sentimentality, and sentiment in the Victorian era.
      • Charles Dickens’ moral sentiments



  • Week 11: Web Scraping & Visualization

    • Tue (3/30)
      • CW: Our Mutual Friend (bk.4 chs.6-10)
      • TR: Curating datasets
      • TR: Playing with tools
      • TR: Visualizing results
    • Thu (4/1)
      • CW: Our Mutual Friend (bk.4 chs.11-13)
      • TR: Web scraping
      • TR: Word frequencies and word cloud

  • Week 12: Humanities Data / Word Embeddings

    • Tue (4/6)
      • CW: Our Mutual Friend (bk.4 chs.14-17)
      • Humanities dataset projects
      • Kaggle
      • Google Dataset Search
      • TR: NLTK
    • Thu (4/8)
      • What are word embeddings?
      • TF-IDF, Word2Vec, LDA, ELMo, GPT, and BERT

  • Week 13: Introduction to Deep Learning

    • Tue (4/13)
      • What is deep learning?
      • What is AI bias?
      • Deep learning in literature
      • GPT-3 (OpenAI) / BERT (Google Research)
    • Thu (4/15)
      • GAN / cGANs
      • TR: Colorization
      • TR: The Victorian400 dataset

  • Week 14: Lightning Talks

    • Tue (4/20)
      • Project presentations (Day 1)
    • Thu (4/22)
      • Project presentations (Day 2)

  • Week 15: Lightning Talks & Review

    • Tue (4/27)
      • Project presentations (Day 3)
    • Thu (4/29)
      • Review day
      • Course evaluation (AEFIS)

License: CC BY-NC 4.0