Postgraduate Course: Text Remix (Online) (EFIE11565)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | *EFI Skills and Methods Suite*
Please Note:
This course is only available to students enrolled on one of Edinburgh Futures Institute's postgraduate programmes.
This fun introductory course explores the co-creation of text through collaboration, focusing on categorising and transforming source material into experimental creative works. Using Notebook-based worksheets, students will learn fundamental programming and Natural Language Processing (NLP), including the use of LLMs and Markov chains, to drive creative exploration. Delivered via the Edinburgh Futures Institute's fusion model, the sessions combine tutorials with group discussion and require no prior experience in coding or creative writing. |
| Course description |
This course explores the practical creation and co-creation of text through collaboration with human and computational partners. Specifically, we will focus on methods for categorising, transforming, and responding to source material, culminating in the production of experimental creative pieces and innovative forms.
The practical components involve programming via Notebook-based worksheets. Students will be introduced to fundamental computer programming concepts, using text as a primary data source to drive their technical and creative exploration. Early tasks, for instance, will involve replicating simple text-based games in code to stimulate the development of original artworks.
The programming content is designed to introduce key concepts, including:
- Simple Automata: Markov chain text generators and automated bots.
- Data Acquisition: Web scraping techniques to obtain text data.
- Natural Language Processing (NLP): An introduction to various aspects of NLP, including Large Language Models (LLMs).
- Analysis: An overview of both quantitative and qualitative text analysis methodologies.
Taught sessions will follow a collaborative fusion model, consisting of:
1) Lectures and example sharing.
2) Interactive text-based games.
3) Practical 'code-along' tutorials.
4) Peer feedback and discussion on creative and technical tasks.
5) Group work and collaborative problem-solving.
As this is an introductory course, no prior experience in programming or creative writing is required.
Edinburgh Futures Institute (EFI) - Online Hybrid Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed (see the Lecture Recording and Virtual Classroom policies for more details). There will, however, be options to control whether or not your video and audio are enabled.
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 10,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course will be assessed by means of the following components:
1) Portfolio and Critical Reflection (50%)
A portfolio of 5 creative pieces reflecting different permutations of the same textual source. Includes a 1,000 word critical reflection on the rationale for selection and editing, grounded in research.
Learning Outcomes Assessed by Component: 1, 3, 4
2) Group Project (50%)
A group project using corpora to conduct text analysis and infer/create an output (e.g., a Twitterbot or FAQ Chatbot). |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Coding feedback will be in-person at drop-in times (either online or physical). Quizzes will be automated to provide automated frequent and immediate feedback.
Part of the organised sessions will be devoted to discussions between students in groups. These will be focussed around both creative and coding tasks, and will provide an element of peer feedback on individual pieces of work. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Develop and implement simple text games and automata in a suitable programming language.
- Scrape text from various sources and conduct simple analyses using these as data.
- Generate new creative pieces using text and computational techniques as inspiration and input.
- Understand and be able to use critically some simple computational models for analysing a piece of text.
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Reading List
All these texts are electronically accessible apart from the reading by Hannah Fry.
Essential Reading:
Excerpt from: Shane, Janelle. You Look Like a Thing and I Love You. Wildfire, 2019. pp.168-184 are available in digitised form on the resource list but we would recommend the whole book.
Vasilev, Yuli. Natural Language Processing with Python and Spacy. No Starch Press Inc. 2020.
Recommended Reading:
Downey, Allen B. 'Think python 2nd edition' - https://greenteapress.com/wp/think-python-2e/
Fry, Hannah. Hello World: how to be human in the age of the machine. London: Black Swan, 2019
Hartman, Charles O. Virtual Muse Experiments in Computer Poetry. Hanover, N.H.: University Press of New England, c1996.
Kac, Eduardo. Media Poetry an international anthology. Intellect, 2007.
Schäfer, Mirko Tobias, and van Es, Karin. The Datafied Society: Studying Culture through Data. Amsterdam University Press, 2017. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Text,data science,digital humanities,machine learning,natural language processing,creativity |
Contacts
| Course organiser | Dr Stuart King
Tel: (0131 6)51 7032
Email: S.King@ed.ac.uk |
Course secretary | Miss Abby Gleave
Tel: (0131 6)51 1337
Email: abby.gleave@ed.ac.uk |
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