Understanding Effects of Feedback on Group Collaboration

http://www.aaai.org/Press/Reports/Symposia/Spring/ss-09-04.phpSmall group collaboration is vital for any type of organization to function successfully. Feedback on group dynamics has been proven to help with the performance of collaboration. We use sociometric sensors to detect group dynamics and use the data to give real-time feedback to people. We are especially interested in the effect of feedback on distributed collaboration. The goal is to bridge the gap in distributed groups by detecting and communicating social signals. We conducted an initial experiment to test the effects of feedback on brainstorming and problem solving tasks. The results show that real-time feedback changes speaking time and interactivity level of groups. Also in groups with one or more dominant people, the feedback effectively reduced the dynamical difference between co-located and distributed collaboration as well as the behavioral difference between dominant and non-dominant people. Interestingly, feedback had a different effect depending on the type of meeting and types of personality. We intend to continue this direction of research by personalizing the visualization by automatically detecting type of meeting and personality. Moreover we propose to demonstrate the correlation of group dynamics with higher level characteristics such as performance, interest and creativity

Sensor-based organizational design and engineering

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-127).We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information such as e-mail, surveys, and performance data in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system's general specifications and discuss several studies that we conducted in different organizations using the sociometric badge experimental sensing platform. We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual and group outcomes, both in terms of job quality characteristics and organizational performance.by Daniel Olguín Olguín.Ph.D

Understanding people through the aggregation of their digital footprints

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 160-172).Every day, millions of people encounter strangers online. We read their medical advice, buy their products, and ask them out on dates. Yet our views of them are very limited; we see individual communication acts rather than the person(s) as a whole. This thesis contends that socially-focused machine learning and visualization of archived digital footprints can improve the capacity of social media to help form impressions of online strangers. Four original designs are presented that each examine the social fabric of a different existing online world. The designs address unique perspectives on the problem of and opportunities offered by online impression formation. The first work, Is Britney Spears Span?, examines a way of prototyping strangers on first contact by modeling their past behaviors across a social network. Landscape of Words identifies cultural and topical trends in large online publics. Personas is a data portrait that characterizes individuals by collating heterogenous textual artifacts. The final design, Defuse, navigates and visualizes virtual crowds using metrics grounded in sociology. A reflection on these experimental endeavors is also presented, including a formalization of the problem and considerations for future research. A meta-critique by a panel of domain experts completes the discussion.by Aaron Robert Zinman.Ph.D