Illustration of Digital Culture's Complex Networks

ENGL 334-001 / MW 13:00-14:15 / HLG 321

Pr. John Laudun / HLG 356 /


Digital storytelling uses all the possibilities of digital media — text, images, audio, video — to communicate effectively and, most importantly, meaningfully with audiences. It combines the art of traditional storytelling with the tools of modern technology, allowing individuals to share their experiences and ideas with a wider audience. While a lot of materials and tutorials focus on the technology, in this course we will focus on what matters, the story. What makes for a good story? How do stories work? What can we learn from thousands of years of storytelling, and how can we adapt all these things we know about how our brains work and how ideas are embedded in texts in order to tell a story that our audience cannot stop listening to, reading, watching, playing, experiencing?

This course explores all the fundamentals of storytelling and surveys some of the varieties of media production (e.g., micro-blogging, long form writing, audio, video, games). By the course’s end, students have designed and produced a variety of stories and published them on sites of their choosing and design. Course features include guest lectures (including storytellers), exploration of generative text AI, as well as the usual readings and viewings that make up a university course.


In taking this course, participants will become familiar with the basic structures of narrative (both cognitive and formal) and have some experience in using those structures, or their knowledge of them, to produce meaningful texts of their own. Participants will also explore publishing options and will pursue at least a few of those options.


The course assumes that participants are self-motivated and willing to experiment, and to fail. Failing at something is success. Failing to try is failure. If you do not understand an assignment, ask. Ask a peer or ask me. There are a lot moving parts to this course: publishing in some fashion on the web, writing, making audio, making video, sketching out a game. With today’s smart phones and computers, you have access to all the hardware and software you need to do not only basic but also outstanding work. The only limitation is your willingness to try, to take a risk.

That noted, this course is built around participants producing things. We can call those things texts, and they can take the form of written stories, or they can be realized in audio, video, dramatic presentation, scripted interactive game play (either live or through a board or video game).

By the end of the semester, you will have published at least ten such productions. From those ten, you will select the five best as your portfolio to be assessed by me (and perhaps your peers — we’ll see how that goes). The portfolio must contain at least three written texts and two non-written productions (audio, video, game, mystery). [Please note that any and all mysteries must be approved by my in advance of portfolio submission.]

Participation (70%) includes being active in class, doing in-class assignments, submitting out-of-class assignments on time.

Portfolio (30%) includes the five productions described above along with a cover essay in which you state your understanding of stories and storytelling and explain how your portfolio either exemplifies that vision or fails to do so. (That is, you can be disappointed in your portfolio and still do well: this is a university class; it’s about learning.)


Unit 1

Week 1 (A22/24): What it’s all about

All the usual things on the first day, including some participant interaction focused on defining things like “stories” and “narrative.” Homework includes determining the venue for the course (e.g., GitHub pages, Medium, etc.).

Consumables : Read Arguelles and watch the video at the end of the essay.

Arguelles, Carlos. 2021. The Importance of Story-Telling in Software Engineering: Amazon’s Cult of the 6-pager: why narrative matters. Geek Culture, Nov 23.

Week 2 (A28/30): What it’s not about but also why it matters

That will be followed by focused consideration of the four dimensions of narrative.

Consumables: Herman (in Moodle library).

Week 3 (S4/6): Labor Day & Early Fall Break

This course is untouched by Fall Break. Consider this week to be an early fall break.

W4 (S11/13): The Shapes of Stories

Most of you will have been exposed to Freitag’s pyramid in some form. All of you will have been assured that stories have beginnings, middles, and ends, but have you heard of the hero’s journey? What about the Hollywood Formula? The Story Circle?

Week 5 (S18/20)

Most vector space representations of texts consider them to be bags of words with the only importance attached to the co-occurrence of words within the larger bag. But words have meanings and those meanings are revealed through collocations: words accompany other words with regularity.

Week 6 (S25/27)

Not only do words go with other words, they usually go with those words in a particular order. That is, words occur in regular sequences, and those sequences either constitute strings we recognize as texts or pieces of texts.

Unit 2 | getting data

Week 7 (O2/4)

We can’t do much without data, but data just doesn’t appear out of nowhere: we have to go out and get it, which means we have to make decisions about what drives us: a question or a phenomenon. How we answer that questions determines what data we collect. (Most data scientists estimate that at least 25% of their work involves collecting and munging data.)

Week 8 (O9/11)

Once we have data, or even in the process of accumulating data, we need to decide how we are going to store it and how we are going to recall, or load, it. Some of our responses here will be based on the computational and storage resources available to us.

UNIT 3 | getting to work

Week 9 (O16/18)

Our project has already begun with data collection but now we need to explore the scope of our work and what our outcomes might be. This is an iterative process, and so the sketching we do here will be just that, a sketch, a draft subject to revision.

Week 10 (O23/25)

An important aspect of any analytical project is documentation: whether you are working in a research or in an applied environment, others are going to want to know how you got your results. That is, your results are only as good as their replicability.

Week 11 (O30/N1)

Throughout this process, we have engaged in various forms of visualization, and we will continue to do so, but it is important to consider what visualizations can, and cannot do.

UNIT 4 | getting chatgpt

Week 12 (N6/8)

A number of sources would have you believe that SkyNet lurks around the corner from ChatGPT. Others suggest that GPT is little more than a souped-up neural network with some word vectors mixed in. The truth is complicated and relies a great deal on the shear amount of data that lies behind GPT and BERT. In this unit we explore the basics of transformers and build our own to see if the models they produce tell us anything interesting about the corpora we have built.

Week 13 (N13/15)

Week 14 (N20/22): Thanksgiving Week

Week 15 (N27/29): Final Week