Postgraduate Course: Text Remix (fusion on-site) (EFIE11004)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will introduce some programming concepts and practice using text as data, as well as various transformation techniques and games as a means to stimulate creative skills. Further, the course will explore some of the ways that text can be algorithmically generated, or collected and then analysed. |
| Course description |
The course will explore practical creation and co-creation of text with human and/or computational partners. In particular, the course will concentrate on ways of categorising, transforming and responding to source text resulting in the creation of new experimental pieces or forms.
The practical work in the course will involve programming though Notebook-based worksheets. Students will therefore be introduced to some basic concepts and practice in computer programming using text as a data source to drive their exploration of these concepts. For example, one of the first tasks will be to replicate some simple text games to stimulate the creation of a new artwork.
The programming content will be built to include some simple automata (e.g. Markov chain text generator, twitter bot), web scraping to obtain text data, and an introduction to some aspects of natural language processing. We will cover some quantitative text analysis ideas, discuss qualitative analysis, and the concept of automated 'coding' will be demonstrated.
Taught sessions will cover a mix of time spent on:
(1) Lectures/sharing of examples;
(2) Text-based games;
(3) Code-alongs;
(4) Discussion and peer feedback on creative and coding tasks;
(5) Group working and discussion.
Edinburgh Futures Institute (EFI) - On-Site Fusion Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. 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 be aware that:
- Classrooms used in this course will have additional technology in place: students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- Unless the lecturer or tutor indicates otherwise you should assume the session is being recorded.
As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
| Course Start Date |
19/09/2022 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 3,
Supervised Practical/Workshop/Studio Hours 10,
Online Activities 10,
Other Study Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
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| Additional Information (Learning and Teaching) |
Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 2
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Summative Assessment:
The course will be assessed by means of the following assessment components:
1) Individual Creative Response (50%)
A folio of five pages (with one piece per page) drawn from the creative exercises introduced in class. Students can use a selection of their class-based pieces, edit them, or offer new pieces inspired by the classes. A short 500-word reflection should accompany the work. 50% (Individual).
2) Analysis and Creation Group Task (50%)
Analysis and creation task using corpora to conduct a simple text analysis and make or infer something from it, e.g. creation of a twitterbot art piece through analysis of all tweets with #Edinburgh in them or production of a chatbot for new EFI students' FAQs. 50% (Group-based).
Formative Assessment:
Each course within Edinburgh Futures Institute includes the opportunity for you to participate in a formative feedback exercise or event which will help you prepare for your summative assessment. The formative assessment does not contribute to your overall course mark.
Additional formative automated online quizzing of programming concepts and practical work. |
| Feedback |
Feedback on the 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 will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
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 Hannah Fry.
Essential
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
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.
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Additional Information
| Graduate Attributes and Skills |
1) Students will develop key knowledge skills through analysis of readings, presentations and the production of research material.
2) Students will use computational methods to address questions and issues of interest to researchers across a range of disciplines.
3) Students will develop original and creative responses using text as a source of data.
4) Students will communicate with peers and academic staff in a collegial setting, be introduced to a range of ICT applications to support and enhance work at this level, and will gain a grasp of basic programming concepts.
5) Working in small multidisciplinary teams, students will develop communication, autonomy, accountability and skills in working with others. |
| 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 | Mr David Murphy
Tel:
Email: dmurphy7@ed.ac.uk |
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