| Prerequisites and co-requisites |
Basic knowledge of HCI is required. This includes basic knowledge of human communication and information processing according to the ABCS concept by Ritter et al. (Ritter; Baxter; Churchill 2014) Ritter, Frank E .; Baxter, Gordon D .; Churchill, Elizabeth F. (2014). Furthermore, basic knowledge of contextual design (persona, scenarios) according to Holtzblatt et al. (Holtzblatt; Beyer 2016) and evaluation (Rubin 1994) (such as paper prototyping see (Snyder 2003) and expert evaluation see (Nielsen; Mack 1994)) are required.
Jeff Johnson (Johnson 2021) and Donald A. Norman (Norman 2013) are recommended for guidance.
Knowledge of solution tools such as
Data wrapper, Flourish, Google Data Studio, PowerBI, Python, R, Tableau are advantageous. |
| Course content |
- Basics and structure of data visualization
- Basics in storytelling
- Human perceptual apparatus
- Colors and color spaces
- Layout and composition
- 1D, 2D, 3D
- Visualization techniques
- Visual highlighting and information search
- Spatial perception
- Design patterns
- Interaction with visualizations
- Cognition and visualizations
- Design sketching
- Guidelines
- Semiotics
- Social / crowd visualization techniques
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| Learning outcomes |
The students get to know various extended data visualization and data storytelling concepts for their field of application, to question them critically and to develop and implement application scenarios.
The focus is initially on getting to know (factual knowledge) the specialist areas and their overall location (meta-cognitive aspects of knowledge).
The conceptual knowledge is deepened independently and analyzed in the group in order to then document and implement the expansion through combination, recoil, variation, transposition, analogy, adaptation and mutation of what has been learned on the basis of research (analysis and synthesis) in individual tasks (level of Evaluating and creating). |
| Planned learning activities and teaching methods |
- Critical "reception"
- Discussions and free lectures
- Case study: The students work out a story in full
- Reflection: The students work on a questionnaire at regular intervals during the semester and finally a synthesis report on the individual video tutorial contributions by their colleagues.
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| Assessment methods and criteria |
The course has a strongly discursive character and therefore personal participation is a criterion for success.
Presence in the lectures and active participation in the thematic discussions are therefore required and are included in the results as an oral component at 20%.
(The exact assessment criteria are presented in the first VL.)
Concepts for the visualization of multidimensional data in different technologies are developed in group work.
This includes both the theoretical basics of visualization related to the selected technology, as well as the presentation of the concept in the event and the discussion and, if necessary, defense of the concept.
(The exact assessment criteria are presented in the first VL.)
For a positive overall grade, at least 50% of the points must be achieved in each part of the examination. |
| Comment |
None
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| Recommended or required reading |
- Ware, Colin (2012): Information Visualization: Perception for Design. 3rd Ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
- Knaflic, Cole Nussbaumer. (2015): Storytelling with Data: A Data Visualization Guide for Business Professiona. John Wiley & Sons.
- Knaflic, Cole Nussbaumer, , Madden, Catherine, (2020): Storytelling with data : let’s practice! John Wiley & Sons.
- Kirk, A. (2019): Data Visualisation: A Handbook for Data Driven Design. SAGE Publications.
- Treue, Stefan (2003): “Visual attention: the where, what, how and why of saliency.” In: Current Opinion in Neurobiology, 13 (2003), 4, p. 428–432.
- Card, Stuart K.; Mackinlay, Jock D.; Shneiderman, Ben (eds.) (1999): Readings in Information Visualization: Using Vision to Think. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
- Tufte, Edward R. (1986): The Visual Display of Quantitative Information. Cheshire, CT, USA: Graphics Press.
- Tufte, Edward (1990): Envisioning Information. Cheshire, CT, USA: Graphics Press.
- Tufte, Edward R. (2006): Beautiful Evidence. Graphis Press.
- Mullet, Kevin; Sano, Darrell (1995): Designing Visual Interfaces: Communication Oriented Techniques. Englewood Cliffs, NJ: Prentice Hall.
- Bertin, Jacques (1981): Graphics and graphic information-processing. Berlin ; New York: de Gruyter.
- Neurath, Otto, , Eve, Matthew,, Burke, Christopher, (2010): From hieroglyphics to Isotype : a visual autobiography. London: Hyphen Press.
- Williams, Robin (2014): The Non-Designer’s Design Book. Pearson Education.
- Lazar, Jonathan (2007): Universal Usability: Designing Computer Interfaces for Diverse User Populations. Hoboken, NJ, USA: John Wiley & Sons, Inc.
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| Mode of delivery (face-to-face, distance learning) |
Face-to-face event with recording of the lecture
The contents and objectives of the course are prepared and guided by selected contributions (text, sound, image) by the students, analyzed, discussed and implemented as examples. Current contributions are welcome. According to the interests of the individual participants, each participant selects a topic and connects it to the main specialization. In this individual elaboration, the focus is on evaluation and creation based on solid research and design implementation. |