Publikationen

Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.

Especially in lifelong or professional learning, the picture of a continuous learning analytics process emerges. In this proces s, het- erogeneous and changing data source applications provide data relevant to learning, at the same time as questions of learners to data cha nge. This reality challenges designers of analytics tools, as it req uires ana- lytics tools to deal with data and analytics tasks that are unk nown at application design time. In this paper, we describe a generic vi sualiza- tion tool that addresses these challenges by enabling the vis ualization of any activity log data. Furthermore, we evaluate how well parti cipants can answer questions about underlying data given such generic versus custom visualizations. Study participants performed better in 5 out of 10 tasks with the generic visualization tool, worse in 1 out of 1 0 tasks, and without significant difference when compared to the visuali zations within the data-source applications in the remaining 4 of 10 ta sks. The experiment clearly showcases that overall, generic, standalon e visualiza- tion tools have the potential to support analytical tasks suffi ciently well