Publikationen

In the context of the Internet of Things (IoT), every device have sensing and computing capabilities to enhance many aspects of human life. There are more and more IoT devices in our homes and at our workplaces, and they still depend on human expertise and intervention for tasks as maintenance and (re)configuration. Using biophilic design and calm computing principles, we developed a nature-inspired representation, BioIoT, to communicate sensor information. This visual language contributes to the users’ well-being and performance while being as easy to understand as traditional data representations. Our work is based on the assumption that if machines are perceived to be more like living beings, users will take better care of them, which ideally would translate into a better device maintenance. In addition, the users’ overall well-being can be improved by bringing nature to their lives. In this work, we present two use case scenarios under which the BioIoT concept can be applied and demonstrate its potential benefits in households and at workplaces.

This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.