Group Membership Management Framework for Decentralized Collaborative Systems

Scientific and commercial endeavors could benefit from cross-organizational, decentralized collaboration, which becomes the key to innovation. This work addresses one of its challenges, namely efficient access control to assets for distributed data processing among autonomous data centers. We propose a group membership management framework dedicated for realizing access control in decentralized environments. Its novelty lies in a synergy of two concepts: a decentralized knowledge base and an incremental indexing scheme, both assuming a P2P architecture, where each peer retains autonomy and has full control over the choice of peers it cooperates with. The extent of exchanged information is reduced to the minimum required for user collaboration and assumes limited trust between peers. The indexing scheme is optimized for read-intensive scenarios by offering fast queries -- look-ups in precomputed indices. The index precomputation increases the complexity of update operations, but their performance is arguably sufficient for large organizations, as shown by conducted tests. We believe that our framework is a major contribution towards decentralized, cross-organizational collaboration

Supporting context-aware collaborative learning through automatic group formation

Collaborative learning is based on groups of students working together with traditional and computer-based tools or applications. We have found that to make these supporting applications more effective we need to address the problem of automating group awareness in CSCL applications by estimating group arrangements from location sensors and the history of interaction. This contextual information can enable the construction of applications that facilitate communication among group members in synchronous and collocated collaborative learning activities. We used data traces collected from the study of students‟ behavior to train and test an intelligent system. Results show that context-information can be effectively used as a basis for a middleware for automating group management. Inferring group membership is technically feasible, can be integrated in group-support applications and can be used in real-world settings.Postprint (published version

A Capabilities-Based Theory of Technology Deployment in Diverse Teams: Leapfrogging the Pitfalls of Diversity and Leveraging Its Potential with Collaborative Technology

Previous research on groups with diverse membership indicates that they generally exhibit high levels of conflict and experience low levels of cohesion; however, they also tend to outperform their homogeneous counterparts. We examine this apparent paradox and discuss a theory-based technology-oriented approach to resolving it. Based on an extensive review of three research streams~{!*~}group diversity, group development, and collaborative technologies~{!*~}we develop an integrated model of ongoing team interaction that describes how the purposeful deployment of certain collaborative technology capabilities, based on temporal milestones, can help leverage the positive aspects of diversity while limiting its negative aspects. We conclude by developing a set of propositions that can be tested empirically

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics

Group Prediction in Collaborative Learning

We propose an approach for predicting group formations, to address the problem of automating the incorporation of group awareness into CSCL applications. Contextual information can enable the construction of applications that effectively assist the group members to automatically communicate in synchronous and collocated collaborative learning activities. We used data traces collected from the study of students’ behavior to train and test an intelligent system. Results have shown that context-information can be effectively used as a basis for a middleware for a dynamic group management. Inferring group membership is technically viable and can be used in real world settings.Postprint (published version

A Qualitative Analysis of Belonging in Communities of Practice: Exploring Transformative Organizational Elements within the Choral Arts

A qualitative analysis was conducted with a community choir as an exemplar of a community of practice. Semi-structured, collaborative interviews with eighteen of the choir’s members and eleven hours of field observation were conducted. The socialization process was briefly examined and discussed as it informed membership experiences in the choir. Four research questions were proposed to examine the ways in which the defining characteristics of communities of practice were communicatively enacted within the choral context. The construct of belonging was examined as an addition to Wenger’s (1998) communities of practice framework. Data analysis followed the grounded theory methodology of Strauss and Corbin (1990). The choral context was chosen for its particular intersections of art form characteristics with Wenger’s (1998) theoretical framework. The communal nature of choral singing required the mutual engagement of a variety of members. Singers negotiated their joint enterprise over the years as membership stabilized and the technical expertise of the group increased. The community’s shared repertoire was evident in the musical repertoire, sense of a cohesive group, and the informal discourses which indicated membership. As choir members engaged in the process of choral singing, negotiated its meaning for their particular group, and enacted belonging while drawing from a shared repertoire, they were communicatively constructing a community. Belonging was enacted by members through their mutual recognition of membership and strong emotional connection through artistic expression. This research is a test of Wenger’s (1998) communities of practice theory in a unique organizational setting. Belonging reflected importance as a motivator for new membership and an essential component for sustained membership. Strategic disengagement and select socialization emerged as theoretical implications for socialization and participation in communities of practice. These elements warrant further examination. Implementation of the theoretical characteristics of communities of practice and belonging as it is communicatively enacted can foster healthier organizational environments and membership retention

Collaboration Made It Happen! The Kansas Archive-It Consortium

This case study explores the formation, current membership, and future goals of the Kansas Archive-It Consortium (KAIC), one of the larger consortia contracting with the Web archiving service Archive-It. KAIC, which is composed of the state historical society and five public universities, has its foundation in a statewide culture of collaboration, and participants have agreed on an informal governance structure with a strong commitment to broadening accessible web resources for researchers. After establishing consortial consistency during its first two years, members have shared documentation with partners and are beginning to do collaborative collecting. In the future, the consortium will seek additional members and work with Archive-It to develop a consortial search tool. This web archiving collaborative has helped member institutions overcome challenges by having group discussions, sharing documentation and guidelines, and jointly serving a primary user group, Kansas residents