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Fall 2026: Classes meet M W F 10:10 – 11am, Library 006

Schedule: Fall 2026

DIGIT 110: Lionpath class number: 6108. This course fulfills a core course requirement for the Digital Media, Arts, and Technology (DIGIT) major at Penn State.

Instructor

Dr. Elisa Beshero-Bondar (Dr. B), Professor of Digital Humanities and Program Chair of DIGIT.

Text Encoding: Course Description

This course teaches you the art and science of archiving documents digitally using text encoding and markup, which has roots in hand-written editorial practice and the publishing industry. Text encoding sits on the borders between humanities, art, and computer science. Encoding or markup itself is definitely humanities work. It is writerly and editorial reflecting your decisions about what matters for your research, often for cultural heritage and curation. The technologies that process markup (checking it, finding patterns, extracting data) are computational, an accessible entry point to programming and artful digital design. You do not need to be an expert programmer to excel in this course, but you will gain foundational skills: real experience with mindful file management, asking good questions, and thinking algorithmically through problems.

We build our markup with eXtensible Markup Language (XML), the internal format behind bank and hospital records, the Microsoft Office and LibreOffice documents, and much of the web (since HTML itself is a form of XML). We'll explore the XML family of languages which includes schemas, TEI, XPath, XSLT, and beyond as a foundation for structuring documents, curating heritage resources, and developing a real digital project.

Artificial Intelligence (AI) in our time has great benefits and serious limitations in text encoding, and we will investigate both. We will investigate AI-assisted handwriting recognition on manuscripts, and preview a local small-language-model tool from our own DH lab that reviews markup consistency. You will gain some experience with AI through class exercises but we will never require you to use it on your projects. When you use AI, you will need to document precisely how you used it and what it contributed, much like citing a source in a research project.

Most importantly, completing this course requires you to collaborate and make decisions in teams and take charge of the technologies to build, present, and publish a real digital archive of your own. You will be making decisions about your text material, the platform to share your work (GitHub or Codeberg), and how you design your web interface and your visual language, including SVG (Scalable Vector Graphics), all built from your XML markup. This course is just enough of a beginning to familiarize you with the world of text encoding, but it is also just enough to lead to next steps if you are excited to continue with it: perhaps with a more extensive research project or presentation at a professional conference.

Learning Objectives:

Class Resources

Other resources: Project Guidelines and Past Student Projects

Explanatory Guides and Exercises: Complete List

Class Web Resources:

Grading:

Homework and Portfolio Checkpoints (25%):

To keep up with this class, you must work on exercises regularly. Each day will involve some small assignment, to prepare you for the next class, and to help you build your course project. Homework is time-sensitive and due before class begins. Points are awarded based on engagement with the assignment and timeliness of completion:

  • Completed before class, demonstrating thoughtful engagement: 3 points.
  • Submitted after we've discussed it in class, showing your own approach: 2 points.
  • Submitted before the unit's test or milestone closes: 1 point.
  • Submitted after the unit closes, without prior arrangement: 0 points, though you may still wish to complete it to review a concept for your project.
  • Each week (or, once project teams form, each milestone period), these points are tallied at a Portfolio Checkpoint: This represents a view of the points you have already earned day to day, plus a quick look for anything worth a second glance or a redo. If you complete 90% of the homework on time, you will receive an A in this portion of the course grade. Homework does not have to be perfect to be awarded full credit. I may request you to redo an assignment to make sure you understand it.
  • Students are not eligible to join a project team if they have not completed a majority of the homework when the class forms project teams.

Homework Expectations: Coding and project review exercises in this course are about your active learning, not as a way of testing whether you have already learned something we covered in class or in a reading. You may often need to look up how to do something you don't already know how to do. That’s expected, and it's why we ask you to keep a brief Resources Used note on submissions where it applies: what you consulted (documentation, a forum, a classmate, an AI tool), what you used it for, and what you decided or changed on your own. This is not meant to be a confession, just documentation, and basically the same concept as responsibly citing a source.

There will be times you don’t get the result you want, and that’s expected. You can still receive full credit if you've made a serious attempt and submit, along with your code, a description of what you tried, what you expected, what you got, and what you think went wrong, also any resources used to help along the way. Getting stuck is part of the learning process! You’ll see me get stuck sometimes too, and I will need your eyes to help me fix something. Documentation of how you got stuck is key. One of our goals is a supportive coding community, comfortable unsticking each other.

I may ask you to redo an assignment for partial credit if it's incomplete or problematic; resubmitting a corrected version will likely (eventually) earn full credit. I'll post comments on Canvas, sometimes directly in your coded file. If I don't return your assignment, that means I found nothing to add beyond our posted solution. If you have questions after reviewing that solution or our class discussion, please ask!

Engagement and Live Demonstration (15%):

Coding and markup work is a social activity. Professionals in the field aren't isolated experts; they are tuned into their communities, asking and answering questions to stay sharp. This category reflects that expectation, and it is earned primarily through the live walkthroughs threaded across the semester: short (5–10 minute) sessions, in class or office hours, where you explain a piece of your own work (a schema decision, a Schematron rule, your CSS choices) to me and sometimes your teammates. These are not meant to be intimidating; they are a chance to show what you actually understand, in a format that’s about your thinking, not just your output.

The rest of this category is earned by actively participating in the class community: participating when classmates present their own walkthroughs, asking good questions, and using our GitHub and Discord spaces to seek and share help. Early in the course, I will ask you to join our DIGIT Discord. If you have a question about an assignment, try your peers there first: think of the Discord as a way to earn credit in this category because your questions help the class! Others likely have the same question, and answers are best shared. (You are always welcome to email me, but I may ask you to take a question to your peers first. The Discord group also includes students from other DIGIT courses, possibly some DIGIT student mentors who have taken this course already, and who may be able to help faster than I can alone.)

Tests (25%):

Across the semester there will be a few (three or four) tests on the concepts and markup technologies we're learning. Most are take-home, open-book, open-notes, completed individually, designed to show what you have learned after finishing a coding unit, not to catch you out. Where a test carries a serious risk of being completed without real understanding (as with some XPath/XSLT work), it is paired with a short live walkthrough rather than graded purely on the take-home artifact. As with homework, document your Resources Used on any test where you consulted something beyond class material.

Project (35%):

Part of the course grade is based on your work in a team-based semester project to construct a digital archive on the web, applying what you learn in homework and tests. We have requirements for initiating team semester projects: if you have not made satisfactory progress in homework and tests, you will not be permitted to join a project team.

Project work is scheduled with paced milestones throughout the semester. Starting with an initial team schema plan at project formation, formalized once you've worked with TEI and Relax NG side by side, and continuing through deployment, styling, and visualization decisions all the way to the Digit Works showcase and final project submission. These milestones double as your project's own portfolio checkpoints, giving you real experience with a design project's workflow: sharing and documenting your markup, processing it to investigate and visualize your data, and presenting the result to a real audience, responding to feedback and completing goals.

If you are not part of a project team (whether by the homework/tests eligibility requirement, or by choice), you may complete an Individual Project Alternative instead: a smaller-scope solo digital edition of a single document, applying the semester's core skills: a validated schema, an XML-to-HTML transformation, and at least one navigation or visual element with milestone check-ins and live walkthroughs paced to your situation rather than a fixed schedule. If you are not on a team when teams form, that is not necessarily final. Meet with me, and depending on your timeline we can either fold you into a team later or continue on the individual path. Because this alternative is narrower in scope than a full team project, it can earn up to 65% of the Project category’s value. Strong work here, combined with solid performance elsewhere, can reach a C or better. Because teamwork carries more challenges for collaboration and project file management than solo work, an A in this category is only attainable by project teams.

Grading Scale:

Grades for the course are calcuated and posted on Canvas, and follow this standard scale: A: 93-100%, A-: 90-92%, B+: 87-89%, B: 83-86%, B-: 80-82%, C+: 77-79%, C: 70-76%, D: 60-69%, F: 59% and below. In taking the course on a pass-fail basis, students must earn a C to receive Satisfactory (passing) credit.

Course Policies:

Each day we are covering material that builds on earlier material and assignments, so your success depends upon regular attendance and completing each assignment on time.

Due dates and why we need them:

Your daily homework for this course is time-sensitive! Coding assignments, response posts, and other homework exercises must be uploaded to Canvas (or GitHub or our web server as specified), by the due date and time indicated on the class schedule. Homework assignments will be posted online to our class website and linked from our schedule, so students who miss class are nevertheless expected to consult the schedule and submit assignments on time. Because we post and share answers to homework exercises after submission deadlines, we will usually not accept late homework submissions.

Exam Policy:

Exams (tests) in this class are time sensitive. I can give extra time as needed to support an accommodation request. However, because I will be posting solutions to tests and discussing them in class, I do not allow people to write tests after the solutions are posted. I will drop your lowest test score for the class, so that you may miss one exam without penalty.

Attendance and Classroom Courtesy:

Attendance is about connecting and being part of our class community of coders. I expect your active presence and interaction with me and your classmates this semester, because we need to rely on each other in the classroom and online in our coding environemnts to learn and develop projects.

This class is fast-paced and requires that we all be making the best use we can of our in-person class sessions. Arriving late and leaving early disrupts the important collective mental activity of class. So does in-class texting and checking your cell phone. During classtime, I ask that you put mobile devices in Do Not Disturb mode. While class is in progress, talking disruptively, leaving the classroom, texting or using a cell phone or computer, reading a newspaper, or other distracting behavior will be actively discouraged.

When you must be absent from class

Please do not attend our physical class if you are not feeling healthy! This is not the semester to suffer through a fever or chills heroically to attend class in person. Stay home, report symptoms, get tested. This applies to me as your professor as well as to you!

If you need to miss classes for health reasons, it is your responsibility to make arrangements with me and your peers to catch up. We will always be connected in some way on line (via e-mail, Slack chat, and GitHub asynchronously) and we will find ways to keep you looped in.

Student (and Faculty) Health and Wellness Services

If any of us, you students or me, are feeling seriously ill this semester, please contact the Behrend Student Health & Wellness Center at 814-898-6217. Reporting in when you do not feel well is not shameful; it is responsible and important to protect yourself and our community.

Counseling Services

Many students at Penn State face personal challenges or have psychological needs that may interfere with their academic progress, social development, or emotional well being. Seek help! The university offers a variety of confidential services to help you through difficult times, including individual and group counseling, crisis intervention, consultations, online chats, and mental health screenings: see resources posted at https://behrend.psu.edu/student-life/student-services/personal-counseling. These services are provided by staff who welcome all students and embrace a philosophy respectful of clients’ cultural and religious backgrounds, and sensitive to differences in race, ability, gender identity and sexual orientation. Get started from the Behrend Personal Counseling Site: https://behrend.psu.edu/student-life/student-services/personal-counseling or visit the Personal Counseling Office in Reed Union Bldg. Rm 1: 814-898-6504.

LionHELP

LionHELP is a smartphone application, available for both iOS and Android, that you can download if you or someone you know may be facing a mental health emergency. This app provides information about the signs of a mental health crisis, how to talk to someone who may be in crisis, a guide to help refer someone to the appropriate resource, and a full list of resources available on campus. The app can be downloaded free of charge, and there is absolutely no tracking of any information. Please note that LionHELP is not a diagnostic tool and should not take the place of services provided by a licensed mental health professional.

Equity

Penn State takes great pride to foster a diverse and inclusive environment for students, faculty, and staff. Acts of intolerance, discrimination, or harassment due to age, ancestry, color, disability, gender, gender identity, national origin, race, religious belief, sexual orientation, or veteran status are not tolerated and can be reported through Educational Equity via the Report Bias webpage (http://equity.psu.edu/reportbias/).

E-mail:

Each student is issued a University email address (username@psu.edu) upon admission. This email address may be used by the University for official communication with students. Students are expected to read email sent to this account on a regular basis. Failure to read and react to University communications in a timely manner does not absolve the student from knowing and complying with the content of the communications. The University provides an email forwarding service that allows students to read their email via other service providers (e.g., Gmail, Hotmail, AOL, Yahoo). Students who choose to forward their email from their psu.edu address to another address do so at their own risk. If email is lost as a result of forwarding, it does not absolve the student from responding to official communications sent to their University email address. To forward email sent to your University account, go to https://accounts.psu.edu/, log into your account, click on Edit Forwarding Addresses, and follow the instructions on the page. Be sure to log out of your account when you have finished.

Academic Integrity

Penn State Erie, The Behrend College, puts a very high value on academic integrity, and violations are not tolerated. Academic integrity is the pursuit of scholarly activity in an open, honest and responsible manner. Academic integrity is a basic guiding principle for all academic activity at The Pennsylvania State University, and all members of the University community are expected to act in accordance with this principle. Consistent with this expectation, the University’s Code of Conduct states that all students should act with personal integrity; respect other students’ dignity, rights and property; and help create and maintain an environment in which all can succeed through the fruits of their efforts. Academic integrity includes a commitment by all members of the University community not to engage in or tolerate acts of falsification, misrepresentation or deception. Such acts of dishonesty violate the fundamental ethical principles of the University community and compromise the worth of work completed by others.” (Senate Policy 49-20 and G-9 Procedures. Any violation of academic integrity will receive academic and possibly disciplinary sanctions, including the possible awarding of an XF grade which is recorded on the transcript and states that failure of the course was due to an act of academic dishonesty. All acts of academic dishonesty are recorded so repeat offenders can be sanctioned accordingly. More information on academic integrity can be found at: http://psbehrend.psu.edu/intranet/faculty-resources/academic-integrity/academic-integrity.

Students facing allegations of academic misconduct may not drop/withdraw from the affected course unless they are cleared of wrongdoing (see G-9: Academic Integrity). Attempted drops will be prevented or reversed, and students will be expected to complete course work and meet course deadlines. Students who are found responsible for academic integrity violations face academic outcomes, which can be severe, and put themselves at jeopardy for other outcomes which may include ineligibility for Dean’s List, pass/fail elections, and grade forgiveness. Students may also face consequences from their home/major program and/or The Schreyer Honors College.

Academic Integrity and Use of AI Text Generative Technology

Here is an official Penn State policy on academic integrity and generative technology, recommended for adaptation based on the educational requirements for specific courses:

  • Students may not use generative AI tools to complete multiple-choice, matching, fill-in the blank, open-ended, or essay exam questions.
  • Students may use generative AI tools for other assignments but must indicate which generative AI tool they have used and how they have used it. To do that, students must include an in-text citation directly after any sentence containing information that has been re-worded, paraphrased, copied, or created by a generative AI tool. Students must put copied text within quotation marks and must indicate when other media (e.g., images, graphics, etc.) has been copied or generated. Students must also list the AI tools they've used as a Sourced Tool at the end of the paper or project. This policy applies to the use of all generative AI tools, including Grammarly, Copilot, and other artificially intelligent tools.

What this means in a coding and markup course:
Penn State’s policy is written with prose assignments in mind: sentence-by-sentence citation, quotation marks, a source list at the end of a paper. Most of what you submit here is code and markup, not prose, so here is the equivalent for our work:

  • We will indicate some assignments that are not appropriate for completion with AI. Usually these are learning exercises to help you get used to thinking how a process works. When the assignment instructs you not to use AI, it is for the benefit of your learning. Simply do not use AI on these assignments. Cultivating your understanding will help you to optimize and professionalize your use of AI later.
  • When you do use AI, instead of an in-text citation after each sentence, attach a brief note headed Resources Used to your assignment submission. In this Resources Used note, indicate what you consulted, what you used it for, and what you decided or changed yourself. This satisfies the same underlying requirement as Penn State’s more general policy: precise, honest documentation of what’s yours and what came from elsewhere, in a form that actually fits our work.

Here is how we approach AI and generative technology specifically in this class:
Looking things up is core to how we learn: documentation, forums, classmates, AI, whatever gets you unstuck. AI is one resource among those, not a special case to hide or apologize for. You are never required to use AI on an assignment or your project, with one exception: a few units are specifically about evaluating AI tools. In these units you'll experiment with tools for AI-assisted Handwritten Text Recognition (HTR) and preview a schema-consistency tool from our own DH lab’s research, where using the AI tool is the whole point of the exercise.

When you do use AI, two things matter more than whether you used it at all: document precisely (tool, what you asked it to do, what you kept or changed), and review and revise. AI output is a draft, not a submission. What you turn in should reflect decisions you made about what to keep, change, or reject, and your statement of Resources Used should say what those decisions were. A note like asked Claude why my schema wasn’t validating, then rewrote the content model myself once I understood the error is exactly what this is for: It shows real thinking, not a shortcut, and it is a professional precision that we want you to cultivate, that supports you far better than silence would in cases when integrity is questioned. For more on how to cite an AI assistant, MLA’s guidance is a good reference: What to include in a citation of an AI assistant.

Source Citation and Plagiarism: One goal of our course is to reflect on how best to credit sources in digital contexts, including applications of artificial intelligence. We will consider how and why such citations differ from documenting printed texts. Our emphasis is on providing a clear information trail for a digital resource: where is it hosted? When was it last updated, and when did you access it? Who is responsible for maintaining the resource? We will also consider the ease and frequency with which digital texts and graphics are plagiarized on the worldwide web, and discuss how the omission of source citations detracts from the authority of a digital information resource. We expect you to practice mindful source citation, and plagiarism on your part will have very serious consequences.

Representing the voice of another individual as your own voice constitutes plagiarism, however generous that person may be in helping you with an assignment. Turning in an assignment generated collectively under the name of a single individual is considered plagiarism. When instructed to collaborate on a project, project collaborators share collective authorship and should identify themselves directly as a team. To avoid plagiarism, cite your sources whenever you quote, paraphrase, or summarize material, or use digital images from any outside source (including websites, articles, books, course readings, Canvas or GitHub postings, or someone else’s notes). When using the copy and paste features as you read and research, be sure that you are carefully marking that these passages are unprocessed from their source, so that you know to process it later. Forgetting to do so not only produces sloppy work but (whether you intended it or not) results in a false representation. As long as you make a good faith and clear effort to cite your sources, you will not be faulted for plagiarism, but your work will be penalized if citations are inaccurate, unclear, or lack important information.

That said, our entire class encourages collaboration and teamwork. We have adopted our colleague David Birnbaum's Collaboration policy, since his course, like ours, involves daily coding assignments and team projects. This policy specifies that students identify collaborators in a comment on submitted asignments and take care on projects that all students contribute equally (and no student is contributing excessively more than what everyone else has done). When joining a group homework session, always work on the assignment by yourself first so you can be an equal participant, and write up the assignment by yourself, after the session is over so you take care not to copy from the other students. While we want you to consult with each other, you are responsible for doing all your writing and coding by yourself, using your own words.

Disability Services:

This course could pose certain issues related to physical abilities. Please talk to me if you need help navigating the course or accessing our resources. In the case of documented disabilities, students must meet with the instructor to discuss their specific accommodations. In order to receive consideration for reasonable accommodations, you must contact the appropriate disability services office at the campus where you are officially enrolled, participate in an intake interview, and provide documentation: See documentation guidelines (http://equity.psu.edu/sdr/guidelines). If the documentation supports your request for reasonable accommodations, your campus disability services office will provide you with an accommodation letter. Please share this letter with your instructors and discuss the accommodations with them as early as possible. You must follow this process for every semester that you request accommodations. Penn State Behrend’s Disability Services Coordinator is Amy James (ajk7@psu.edu)

Career Services

Career Services prepares Penn State students to enter the workforce or graduate school through a variety of services. Career professionals will assist with resume and cover letter reviews, internship and job searches, interview prep and mock interviews, career fair prep, development of career competencies, and graduate school prep. Be sure to utilize Career Services for all of your career endeavors, start planning your career early! See the Career Services website at https://behrend.psu.edu/academics/academic-services/career-services/resources and/or stop into their office which is located in Reed 125. You may also schedule an appointment through Starfish or call 814-898-6164.

Projects that inspire us:

  • Obdurodon: where we learned what we can teach, and where we’re still learning.
  • The Programming Historian (full collection of tutorials)
  • Venice Time Machine: very ambitious, enormous project team of faculty and students to study and model a thousand years of Venice, digitizing "kilometers of archives."
  • Map of Early Modern London
  • Lord Byron and His Times: The very thoughtful stylistic design of this important project reproduces the style of nineteenth-century print and layout. The content makes many rare materials about Lord Byron’s social network searchable and connected to the web of linked open data.
  • The Shelley-Godwin Archive: digitizes the manuscripts of Percy and Mary Shelley, and Mary Shelley’s parents, William Godwin and Mary Wollstonecraft—manuscripts often written in multiple hands. Provides an important study of the Frankenstein notebooks to demonstrate how much of a role Percy Shelley played in the writing of Frankenstein. The archive provides a good model of the use of TEI for manuscript encoding and of complex and multiple visualizations of manuscript texts.
  • Voyant Tools: a text visualization, analysis, and play tool
  • Clay Shirky on Love, Internet Style (9 minutes of Youtube inspiration: on what lasts, and why community matters in our digital worlds.)

Previous versions of this course