Visualization of Mixed Playtesting Data
March 24th, 2021, 14:00 CET
About the Speaker:
Günter is Prof. for Game Computing at the Johannes Kepler University Linz and holds positions at the Eindhoven University of Technology and Ontario Tech University. His work particularly centers on understanding player behavior in games and on researching methods to explore and communicate the collected data to derive actionable insights for game design and development. As part of this, he is working on data visualizations to support the analysis of the increasingly large-scale player behavioral datasets used in game analytics. He is editor of the 'Data Analytics Applications in Gaming and Entertainment' book.
Data collected through Games User Research (GUR) is constantly growing in volume. Moreover, playtesting frequently results in multi-modal datasets consisting of qualitative and quantitative data. Simultaneously displaying mixed playtesting data can offer additional benefits for user researchers as it can provide relevant contextual information. This talk will show how visualizations can be helpful for interpreting (mixed) GUR datasets.
The presentation will start with a high-level discussion of the benefits of visualization for analyzing playtesting data. Afterwards, possible ways of visualizing individual player data will be introduced. The talk will then demonstrate different aggregation techniques for different types of playtesting data (movement, discrete events, and continuous player-related variables such as health or physiological measurements) to convey common patterns and outliers. Aggregation techniques can be very helpful when dealing with larger datasets to avoid difficult to read visualizations. The presented techniques can be applied individually or in combination to triangulate the different data types.
Lastly, the talk will address a thus-far mostly neglected issue in GUR, namely how visual design choices for data visualizations can impact the interpretation of gameplay data. An inappropriate visual design can be misleading, result in wrong conclusions, and have practical consequences. Attendees will come away with an understanding of how visualizations can help in understanding playtesting data.
Machine Learning meets Visualization
January 20th, 2021, 10:30 am CET
About the Speaker:
Michael Sedlmair is a junior professor at the University of Stuttgart, where he works at the intersection of human-computer interaction, visualization, and data analysis. Previously, Michael has worked at Jacobs University Bremen, University of Vienna, University of British Columbia, University of Munich, and the BMW Group Research and Technology. He also holds visiting positions at the Vienna University of Technology, and the Shandong University. His interests focus on information visualization, interactive machine learning, virtual and augmented reality, as well as the research and evaluation methodologies underlying them.
Based on our experience conducting projects at the intersection of machine learning (ML) and interactive visualization (Vis), my talk will reflect on and discuss the current relation between these two areas. For that purpose, the talk’s structure will follow two main streams. First, I will talk about *Vis for ML*, that is, the idea that visualization can help machine learning researchers and practitioners gain interesting insights into their models. In the second part, I will then turn the relationship around and discuss how *ML for Vis* can guide visualization designers and analysts towards interesting visual patterns in the data. The talk will conclude with research challenges that lie ahead of us and that will pave the way for future interfaces between humans and data.