FB1 Sozial- und Bildungswissenschaften
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A topology of groups
(2020)
In this work, we study the collaboration patterns of open source software projects on GitHub by analyzing the pull request submissions and acceptances of repositories. We develop a group typology based on the structural properties of the corresponding directed graphs, and analyze how the topology is connected to the repositorys collective identity, hierarchy, productivity, popularity, resilience and stability. These analyses indicate significant differences between group types and thereby provide valuable insights on how to effectively organize collaborative software development. Identifying the mechanisms that underlie self-organized collaboration on digital platforms is important not just to better understand open source software development but also all other decentralized and digital work environments, a setting widely regarded as a key feature of the future work place.
Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.
Any-Cubes
(2019)
Here we present Any-Cubes, a prototype toy with which children can intuitively and playfully explore and understand machine learning as well as Internet of Things technology. Our prototype is a combination of deep learning-based image classification [12] and machine-to-machine (m2m) communication via MQTT. The system consists of two physical and tangible wooden cubes. Cube 1 ("sensor cube") is inspired by Google's teachable machine [14,15]. The sensor cube can be trained on any object or scenery. The machine learning functionality is directly implemented on the microcontroller (Raspberry Pi) by a Google Edge TPU Stick. Via MQTT protocol, the microcontroller sends its current status to Cube 2, the actuator cube. The actuator cube provides three switches (relays controlled by an Arduino board) to which peripheral devices can be connected. This allows simple if-then functions to be executed in real time, regardless of location. We envision our system as an intuitive didactic tool for schools and maker spaces.