LIDIS4240 Introduction to Digital Humanities & Computational Social Sciences Course Descriptor 

Course Overview

This course provides an engaged introduction to Digital Humanities and Computational Social Sciences, their methods, theories, and applications. It is designed both to support humanities and social sciences students to develop more digital and computational skills, and to support computer, data, and wider science students to engage with the humanities and social sciences.

In this course, which employs case studies and short applied projects, students will explore key tools and methodologies of digital humanities and computational social sciences and acquire an engaged awareness of their benefits and limitations, including what types of questions or studies they can answer, enrich, or transform.

By the end of the course, students are expected to understand the core principles of textual, visual and spatial digital humanities or computational social sciences, including core principles of data ethics, and have applied these in handling textual, visual, or spatial sources.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students in their studies.

The teaching and learning activities for this course are:

• 40 scheduled hours (lectures, workshops, and scheduled assessment activities)
• 110 private study hours (with regular structured assignments)

Faculty hold regular Office Hours, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are expected to attend and participate in all the teaching and learning activities for this course and to manage their directed learning and private study.

Indicative total learning hours: 150

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

Set Exercises

The Set Exercises are integrated across the course, with scope for some work to be completed during class time. Indicative examples:

• Text frequency analysis and topic modelling of media articles (non-coding, e.g. Voyant-tools).
• Joining a crowdsourcing platform and analysing it from the point of view of the user.
• Designing a digital exhibition in respect to data ethics, copyrights, public engagement.
• Creating a collaborative map (non-coding, e.g. Carto, ArcGIS).
• Visualising textual information (non-coding, e.g. Tableau).
• Basic Social Network Analysis from provided data set (e.g. Gephi).

Written Assignment

• Project proposal with reflective report.

Further information about the assessments can be found in the Course Syllabus.

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).      

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

• Gardiner, E., & Musto, R. G. (2015) The digital humanities: A primer for students and scholars. New York, NY: Cambridge University Press
• Jemielniak, D. (2020) Thick Big Data: Doing Digital Social Sciences. Oxford: Oxford University Press.
• Causer and Terras (2014) “Crowdsourcing Bentham: Beyond the Traditional Boundaries of Academic History”, International Journal of Humanities and Arts Computing 8.1, 46-64.
• Cocq, C. (2021) ‘Revisiting the digital humanities through the lens of Indigenous studies—or how to question the cultural blindness of our technologies and practices.’ Journal of the Association for Information Science and Technology, 73(2), 333– 344.
• Yau, N. (2013) Data Points: Visualisation that Means Something. Indianapolis: John Wiley & Sons, Inc, Chapter 1.

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.

• Benefits and Limits of Digital Humanities and Computational Social Sciences
• Text-based tools and methods
• Image-based tools and methods
• Working with spatial data    
• Collaboration and crowdsourcing
• Data ethics
• Born digital data and networks