Since the advent of the Internet, we have been observing a fast-paced development within the computing world. One of the major innovations in recent years is the “Internet of Things”, which brings interconnectedness between devices and humans to unprecedented heights. This technological breakthrough enabled the emergence of a new sub-field within Learning Analytics, Multimodal Learning Analytics, which makes use of several types of data sources to study learning-related processes. As computers and sensors become increasingly cheaper and more accessible,  research within this new sub-field grows, yet some gaps remain unexplored. Additionally, there is a research bias toward computer-assisted learning environments, rather than physical ones. At the same time, the current labor market is highly competitive, and possessing profession-related skills is not sufficient to land a job. Besides these skills, there is an increasing demand for social skills, such as communication, teamwork, and collaboration. However, there is a gap between the skills that are trained in an academic setting and the ones that are required by the labor market.

Having this background in mind, this work aims at designing and evaluating an IoT sensor system capable of tracking patterns observed under social interactions within a group, and more specifically, in terms of the distance between group members while solving a task. Another important aspect of this study is the system's cost-effectiveness so that it can be employed in a scalable and sustainable manner. To achieve this goal, a multimethodological approach for Design Science Research was adopted, which implied the combination of several methods such as sketching, prototyping, and testing. As a result, this study contributes both to the research area of Multimodal Learning Analytics, and to educational practices.