Module description
In the digital age, data visualisations are becoming an increasingly prominent way to represent and organise collective life – from digital analytics dashboards, to interactive news graphics. Data visualisations are also becoming popular as tools for exploration and analysis in research contexts, including for social and cultural inquiry. This module will provide an introduction to studying, creating and interpreting data visualisations in these various contexts.It will cultivate a "critical data practice" for working with data visualisations as both instruments and objects of study. As well as facilitating creative experimentation with tools, methods, approaches and practices for creating data visualisations to better understand their affordances, it will also draw on leading research from media studies, science and technology studies, critical data studies and feminist studies in order to support critical reflection about their evolving role in the digital age. This module may use a flipped classroom model which would support active student learning by working on group projects with instructor support and a package of materials (e.g.worksheets, video tutorials, "how-to" articles, datasets, tools).This would leave the lion's share of classroom time for deep, collaborative work on project development with instructor support in a "data sprint" format. Prior to class students would typically be expected to watch video lectures, do readings, learn how to use software tools and implement research protocols with the help of software tool tutorials and worksheets, as well as to progress with their research projects. Time in class would typically be dedicated to hands-on work on group research projects with supportfrom module convenors and possibly other guest experts. With this kind of format, individual learning activities prior to classes are normally essential for students to be able to make progress on their projects during class and for student success on this module. In the final seminar students would typically present the preliminary outcomes of their digital investigations with opportunities for formative feedback ahead of finalising and submitting the projects for summative assessment at the end of the module.
Assessment details
- One x Data visualisation group project (100%)
Educational aims & objectives
Over the course of the semester, students will be:
- Introduce students to data visualisation as an analytical and communication device used across a diverse set of fields and practices.
- Equip students with a critical understanding of the work that data visualisation does in various settings drawing on insights from science and technology studies, feminist theory and critical data studies.
- Familiarise students with data visualisation principles and approaches that draw on science and technology studies, feminist theory and critical data studies.
- Support students to collaboratively design and implement projects in the areas of data visualisation research and practice.
Learning outcomes
At the end of the module, students will be expected to be able to:
- Understand how collections of materials can be visually explored with different tools and techniques.
- Critically assess the affordances and limits of various forms of data visualisation.
- Experiment with strategies to challenge dominant data visualisation modes and imagine how data visualisations can be rethought and aligned with critical and feminist principles.
- Engage in self-regulated learning by identifying their learning needs, setting learning goals, identifying sources and resources that would address their needs and goals, and efficiently organising their learning time.
- Collaborate with others as a member of a project team, inhabit a project role responsibly, and make contributions and decisions in group work settings.
Teaching pattern
Lectures 1 (hour weekly) and seminars 1 (hour weekly)