Information visualisation and multimodal presentation

Teacher(s)

Kieffer Suzanne;

Language

French

Main themes

·         Visual perception
·         Representation (encoding of values, of relations)
·         Presentation (visualization techniques) and interaction
·         Design principles (Gestalt, Bertin, color theory)
·         Dashboards and visual analytics

Learning outcomes

At the end of this learning unit, the student is able to :
1.	Describe data visualizations in terms of data type, data representation, presentation and interaction technique, and user task ;
2.	Explain the different stages involved in the development of interactive visualizations by illustrating each step through its typical results (e.g. deliverables) ;
3.	Apply Information Visualization principles and techniques to design and develop an interactive visualization of a large data set ;
4.	Evaluate a visualization using criteria and propose improvements.

Teaching methods

The course is delivered in hybrid mode, in three phases.

• Phase 1 in person only – Flipped classroom: Students work independently on H5P resources on Moodle and complete practice tests. In-person sessions focus on Q&A and consolidation activities. The phase concludes with a knowledge test.
• Phase 2 online only – Project: Students prepare and structure a dataset, then develop a dashboard using Tableau.
• Phase 3 in person only – Visualization critique: Students analyze and critique a series of data visualizations, identifying their strengths and limitations.

Evaluation methods

Assessment combines continuous assessment and an examination session.

• Continuous assessment: individual written knowledge test (30%) and Tableau project (35%).
• June session: written examination (35%).
• September session: oral examination. If the project is failed, the project must also be resubmitted on the first day of the session.

Artificial intelligence (AI): The use of AI tools is governed by the rules set out in the faculty guidelines on this subject, available on the faculty intranet, in the information provided to students.

• Permitted without disclosure: language assistance; exploration and ideation; literature research. References must have been consulted and analyzed by the students and may not be used solely on the basis of a response provided by an AI tool.
• Permitted with disclosure: quantitative and qualitative data analysis; figure generation; generation, correction, or adaptation of computer code (Python, R, etc.). For these uses, students must indicate the tool used and provide a link to the conversation as well as the complete prompts. A non-functional link or a link that does not allow the declared use to be verified will result in a grade of 0 for the work concerned.
• Prohibited: simulation of participants; generation of data instead of actual data collection; and, more generally, any use of AI that would prevent the assessment of the student’s own knowledge, skills, and work process.

Other information

Tableau project: https://www.tableau.com/
All teaching resources and activities are available on Moodle.
Some resources (e.g. bibliographic resources, slides, explanatory videos) are in English.

Faculty or entity

> COMU