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

The datafication of the workplace

Technological changes in the workplace has a long history, but the recent onus on the generation of data as a central part of the digital economy brings about particular transformations that deserve further attention. Communications tools such as phones, email and computers are monitored in many companies, at the same time as new data sources such as social networks, shared calendars or collaborative working tools are being integrated to increase knowledge not only about the professional activities of workers but also about who they are, or what they might be likely to do in the future. In addition, chips, wearables and sensor networks are increasingly integrated within the broader trend of the Internet of Things (IoT)2 to facilitate emotional as well as physical states. The development of machine learning (ML) facilitates the automated processing of information, whilst multimedia databases are being labelled with semantic information to identify and measure activities, and natural language processing (NLP) can extract knowledge from non-structured texts, such as emails and social networking content to perform sentiment and tone analysis. In this report we provide an overview of these trends within the context of Europe, and focus particularly on tools used for hiring, employee surveillance, performance assessment and management. The overview presented here is not intended to be comprehensive, but is intended to identify key trends with concrete examples of prominent companies and tools in this space, as a way to advance further research agendas on the datafication of the workplace

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

Enhancing distributed collaboration using sociometric feedback

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 115-123).Distributed collaboration is often more challenging than co-located collaboration as many of the social signals become lost in computer-mediated communication. I propose a system that improves the performance of distributed groups using sociometric feedback. Sociometric feedback is a real-time visualization of the quantitative measurement of social interactions. Sociometric feedback helps distributed group members have a better understanding of the members that are not co-present. Moreover, a persuasively-designed sociometric feedback can control the direction of change in the communication pattern of groups, so that the change can lead to a performance increase. Laboratory studies verify the strong relationship between communication patterns and group performance in two types of tasks. Based on these relationships, sociometric feedback is introduced to enhance both the communication pattern and the performance of distributed groups. Results show that sociometric feedback influences the communication patterns of distributed groups to be more like that of co-located groups, which results in an increase in performance. Additionally, sociometric feedback helps groups to have a more consistent pattern of communication even when they face a change in member distribution; this effect also results in an increase in performance. Data from two pilot studies of real-world teams suggests that sociometric feedback may be applicable to real-world organizations to benefit their performance.by Taemie Jung Kim.Ph.D

Collaborative personal speaker identification: A generalized approach

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Longitudinal video Investigation of dyadic bodily dynamics around the time of word acquisition

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 105-110).Human movement encodes information about internal states and goals. When these goals involve dyadic interactions, such as in language acquisition, the nature of the movement and proximity become representative, allowing parts of our internal states to manifest. We propose an approach called Visually Grounded Virtual Accelerometers (VGVA), to aid with ecologically-valid video analysis investigations, involving humans during dyadic interactions. Utilizing the Human Speechome (HSP) [1] video corpus database, we examine a dyadic interaction paradigm taken from the caregiver-child ecology, during language acquisition. We proceed to characterize human interaction in a video cross-modally; by visually detecting and assessing the child's bodily dynamics in a video, grounded on the caregiver's bodily dynamics of the same video and the related HSP speech transcriptions [2]. Potential applications include analyzing a child's language acquisition, establishing longitudinal diagnostic means for child developmental disorders and generally establishing a metric of effective human communication on dyadic interactions under a video surveillance system. In this thesis, we examine word-learning transcribed video episodes before and after the age of the word's acquisition (AOA). As auditory stimulus is uttered from the caregiver, points along the VGVA tracked sequences corresponding to the onset and post-onset of the child-caregiver bodily responses, are used to longitudinally mark and characterize episodes of word learning. We report a systematic shift in terms of caregiver-child synchrony in motion and turning behavior, tied to exposures of the target word around the time the child begins to understand and thus respond to instances of the spoken word. The systematic shift, diminishes gradually after the age of word acquisition (AOA).by Kleovoulos (Leo) Tsourides.S.M

Graphs behind data: A network-based approach to model different scenarios

openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc