TY - CHAP A1 - Quedenbaum, Peggy T1 - Mit Mut und Liebe : Eine Jugend im Kampf gegen Ungerechtigkeit und Terror. Gisela Konopka T2 - Bibliothek der Gründerinnen : Bibliographische Annäherungen an die Gründerinnen und Gründer der professionellen Sozialarbeit / hrsg. von Helmut Knüppel. Bearb. von Ulf Damerow. - Potsdam: Fachhochsch., 2005. - 330 S. N2 - Gisela wurde stark von Gertrude Willson beeinflusst, die zur Entwicklung der Sozialarbeit im umfassenden Amerikanischen Sinne beigetragen hatte, sowie von zwei weiteren Emigranten, dem österreichischen Vertreter der Psychoanalytischenpädagogik, Fritz Redl und dem „Vater“ der Gruppendynamik Kurt Lewin. Ihre eigenen Ausführungen zum „social group work“ sind geprägt von ihren Gruppenerfahrungen in der deutschen Jugend- und Arbeiterbewegung sowie vom Reformpädagogischen Erbe. KW - Konopka, Gisela KW - Gruppendynamik KW - Gruppenarbeit KW - Gruppenpädagogik Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:525-3101 ER - TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Morgan, Jonathan Howard A1 - Rogers, Kimberly B. A1 - Rishi, Deepak A1 - Nagappan, Meiyappan T1 - Artificial Intelligence and Social Simulation BT - Studying Group Dynamics on a Massive Scale JF - Small Group Research N2 - 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. KW - Künstliche Intelligenz KW - Gruppendynamik KW - Maschinelles Lernen KW - Virtuelles Team KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1177/1046496418802362 SN - 1552-8278 VL - 49 IS - 6 SP - 647 EP - 683 PB - Sage Publications CY - London ER - TY - CHAP A1 - Morgan, Jonathan Howard A1 - Zhao, Jun A1 - Zöller, Nikolas A1 - Sedlacek, Andrea A1 - Chen, Lena A1 - Piper, Hayley A1 - Beck, Yliana A1 - Rogers, Kimberly B. A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Modeling the Culture of Online Collaborative Groups with Affect Control Theory T2 - Advances in Social Simulation N2 - We review Affect Control Theory (ACT) as a promising basis for equipping computational agents in social simulations with a sense of sociality. ACT is a computational theory that integrates sociological insights about the symbolic construction of the social order with psychological knowledge about cognitive-affective mechanisms. After explaining the theoretical foundations of ACT and applications of the theory at the dyadic and group level, we describe a case study applying the theory from an ongoing research project examining self-organized online collaboration in software development. KW - Culture KW - Affect KW - Gruppendynamik KW - Kollaboration Y1 - 2021 SN - 978-3-030-61503-1 U6 - https://doi.org/10.1007/978-3-030-61503-1_14 SP - 147 EP - 160 PB - Springer CY - Cham ER - TY - RPRT A1 - Hoey, Jesse A1 - Nagappan, Meiyappan A1 - Rogers, Kimberly B. A1 - Schröder, Tobias A1 - Dametto, Diego A1 - De Zoysa, Nalin A1 - Iyer, Rahul A1 - Morgan, Jonathan H. A1 - Rishi, Deepak A1 - Sirianni, Antonio D. A1 - Yun, Seonghu A1 - Zhao, Jun A1 - Zöller, Nikolas T1 - Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups N2 - Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups (THEMIS.COG) was an interdisciplinary research collaboration of computer scientists and social scientists from the University of Waterloo (Canada), Potsdam University of Applied Sciences (Germany), and Dartmouth College (USA). This white paper summarizes the results of our research at the end of the grant term. Funded by the Trans-Atlantic Platform’s Digging Into Data initiative, the project aimed at theoretical and empirical modeling of identity and sentiments in collaborative groups. Understanding the social forces behind self-organized collaboration is important because technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal organizational hierarchies or through market forces. Our work used a data-driven approach to explore the social psychological mechanisms that motivate such collaborations and determine their success or failure. We focused on the example of GitHub, the world’s current largest digital platform for open, collaborative software development. In contrast to most, purely inductive contemporary approaches leveraging computational techniques for social science, THEMIS.COG followed a deductive, theory-driven approach. We capitalized on affect control theory, a mathematically formalized theory of symbolic interaction originated by sociologist David R. Heise and further advanced in previous work by some of the THEMIS.COG collaborators, among others. Affect control theory states that people control their social behaviours by intuitively attempting to verify culturally shared feelings about identities, social roles, and behaviour settings. From this principle, implemented in computational simulation models, precise predictions about group dynamics can be derived. It was the goal of THEMIS.COG to adapt and apply this approach to study the GitHub collaboration ecosystem through a symbolic interactionist lens. The project contributed substantially to the novel endeavor of theory development in social science based on large amounts of naturally occurring digital data. KW - Computational social science KW - GitHub KW - Gruppendynamik KW - Sozialpsychologie Y1 - 2021 U6 - https://doi.org/10.31235/osf.io/4hukx SP - 1 EP - 36 ER -