Gaussian mixture models are classical but still popular machine learning models. An appealing feature of Gaussian mixture models is their tractability, that is, they can be learned efficiently and exactly from data, and also support efficient exact inference queries like soft clustering data points. Only seemingly simple, Gaussian mixture models can be hard to understand. There are at least four aspects to understanding Gaussian mixture models, namely, understanding the whole distribution, its individual parts (mixture components), the relationships between the parts, and the interplay of the whole and its parts. In a structured literature review of applications of Gaussian mixture models, we found the need for supporting all four aspects. To identify candidate visualizations that effectively aid the user needs, we structure the available design space along three different representations of Gaussian mixture models, namely as functions, sets of parameters, and sampling processes. From the design space, we implemented three design concepts that visualize the overall distribution together with its components. Finally, we assessed the practical usefulness of the design concepts with respect to the different user needs in expert interviews and an insight-based user study.

Digital Literacy (formerly ICT Capability) is one of the seven general capabilities outlined in the Australia curriculum designed to equip young people with the knowledge, skills, behaviours, and dispositions to live and work in the twenty-first century. Whilst opinion commonly supports the premise that there is a need to prepare our children for a digital world, successfully deploying such programs in schools has been fragmented. In this paper we explore some common factors that lead to a program being successful and identify key areas that require deeper investigation and adoption into practice. This work-in-progress paper will be exploring technology as an educational affordance across curriculum areas rather than a specific subject area. This content will provide a foundation for what factors have been proven to be important in a technology program and where further development is required.

When point clouds are labeled in information visualization applications, sophisticated guidelines as in cartography do not yet exist. Existing naive strategies may mislead as to which points belong to which label. To inform improved strategies, we studied factors influencing this phenomenon. We derived a class of labeled point cloud representations from existing applications and we defined different models predicting how humans interpret such complex representations, focusing on their geometric properties. We conducted an empirical study, in which participants had to relate dots to labels in order to evaluate how well our models predict. Our results indicate that presence of point clusters, label size, and angle to the label have an effect on participants' judgment as well as that the distance measure types considered perform differently discouraging the use of label centers as reference points.

The Stay Centered project at Technische Universitat Chemnitz has the goal to improve the overall security of air traffic controllers. Therefore, we attempt to empirically comprehend the usual controller workspace and their dyadic team structure. Within this context, the following paper describes actual interfaces and visualization, discusses recent research within this field and outlines the project's intention.

Abstract The operation of an airline is a very complex task and disruptions to the planned operation can occur on very short notice. Already a small disruption like a delay of some minutes can cost the airline a tremendous amount of money. Hence, it is crucial to proactively control all operations of the airline and efficiently prioritize and handle disruptions. Due to the complex setting and the need for ad hoc decisions this task can only be carried out by human operation controllers. In the field of airline operations control there exists already a vast variety of different software in productive use. We analyze the different approaches from two of the market leaders and identify problematic design choices. We take into account this analysis and develop a set of rules for an intuitive visualization of airline disruption data. Finally, we introduce our tool for visualizing such data which complies to these rules. The visualization enables the user to gain a fast overview over the current problem situation and to intuitively prioritize different problems and problem hierarchies. The efficiency of the design is evaluated with the help of a user study which shows that the new system significantly outperforms the current state of the art.