Enabling visual community learning analytics with Internet of Things devices

Abstract

Industry 4.0 is currently transforming industrial workplaces into sensor-assisted high-tech environments. While it is often feared that jobs will be lost through increasing automation, we are convinced that there is an enormous potential for versatile and competent workers at innovative workplaces. Training at the workplace may contribute to this. In this context, both body-near wearables and industrial devices produce an enormous amount of data. However, the question is how to deal with this amount of data. To this end, visual analytics is a combination of computational techniques from data mining and machine learning with human perceptional methods from human-computer interaction. In this article we present a method and tool support to create rich and interactive visual analytics charts to analyze innovative training solutions in high-tech workplace settings. This brings together the computer's capacities to handle large data and calculation resources with the human ability to quickly grasp relationships. Our technical evaluation shows, that the approach is feasible from a computational perspective; usability tests revealed that the developed pipeline metaphor reaches its goal. Our results may help in designing future systems that fulfill the needs of both trainers and learners in Industry 4.0 settings.